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This work introduces an efficient load scheduling method for handling the day-to-day power supply needs. At peak load times, due to its instabilitythe power generation system fails and as a measure, the load shedding process is followed. The presented method overcomes this problem by scheduling the load based on necessity. For this load scheduling is handled with an artificial neural network (ANN). For the training purpose the backpropagation (BP) algorithm is used. The whole load essential is the input of the neural network (NN). The power generation of all resources and power losses at the instant of transmission is the NN output. The optimum scheduling of different power sources is important when considering all the available sources. Load scheduling shares the feasibility of entire load and losses. It is well-known as optimal scheduling if the constraints such as availability of power, load requirement, cost and power losses are considered. Training the system using a large number of parameters would be a difficult task. So, finest number of communally independent inputs is selected. The presented method aims to lower the power generation expenditure and formulate the power available on demand without alteration. The network is designed using MATLAB.

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result.

With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task’s start times. The project arrival date and the task’s execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task’s execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task’s execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task’s execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task’s execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning.

Ülkemizde ve dünyada nüfusun sürekli artması ile birlikte, toplu taşıma ve ulaşım sistemlerinin planlanması ve bu süreçte yaşanan sorunların çözümü; çevresel duyarlılık, ekonomik ve toplumsal fayda açısından çok büyük önem arz etmektedir. Şehir içi toplu taşıma sistemlerinde sefer sıklıklarının ve sayılarının optimum seviyesinin belirlenmesi; maliyet, çevresel etki ve toplumsal refah ekseninde toplu taşımanın en önemli sorunlarından olup, şehirde yaşayan tüm bireyler için kritik bir konudur. Bu çalışmada; şehir içi toplu taşıma (otobüs) sistemlerinin verimli ve sürdürülebilir olarak planlanması amacıyla; Tekirdağ/Çorlu Belediyesi toplu taşıma ulaşım sistemlerinin günlük sefer sayılarının, sefer sıklıklarının ve yolcu taşıma kapasitelerinin mevcut durumu incelenerek sefer sayısı optimizasyonu yapılmıştır. Bu amaçla ulaşım yönetim sistemi verileri kullanılmış ve günlük sefer sayısı optimizasyonu için doğrusal hedef programlama modeli geliştirilmiştir. Model, LINGO paket programı yardımıyla çözülerek, gün bazında optimum sefer sayısı belirlenmiştir. Çalışmada seçilen pilot otobüs hattı için detaylı analizler gerçekleştirilmiştir. Mevcut durum sistem tasarımı ve optimum sistem tasarımı sonuçları karşılaştırmalı olarak değerlendirilmiş olup fayda maliyet analizi yapılmıştır. With the continuous increase in the population in our country and in the world, planning of public transportation and transportation systems and solving the problems experienced in this process is of great importance in order to provide environmental awareness, economic and social benefit. The issue of determining the optimum level of trip frequencies and number of trips in urban public transport systems is one of the most important problems and is a critical issue for all individuals living in the city. In this study; in order to plan urban public transport (bus) systems efficiently and sustainably; by examining the current situation number of daily trips, trip frequencies and passenger transport capacities of Tekirdağ / Çorlu Municipality public transportation systems, the number of trips has been optimized. For this purpose, a linear goal programming model was developed for the optimization of the number of daily trips. The model was solved with the help of the LINGO package program and the optimum number of trips per day was determined. In the study; detailed analyzes were carried out for the selected pilot bus line. The current state system design and optimum system design results were evaluated comparatively, and cost-benefit analysis was performed. Çorlu Belediyesi ile TEKULAŞ Araç Takip ve Koordinasyon Merkezi’ne katkılarından dolayı teşekkür ederiz.

Accurate production planning in both the short and long term is very important in cogeneration plants. Especially if the cogeneration unit operates under free electricity market conditions, which complicates the decision-making process as an additional planning condition with variable heat, fuel, and CO2 costs. On the other hand, when a cogeneration plant uses a heat accumulation system, it is impossible to make a production decision without using a computer system; the human factor in decision-making can lead to erroneous decisions without traceability. The role of modern computer systems is growing and greatly influences the optimal production planning process in cogeneration plants, regardless of the installed capacity and in the operation with heat accumulation. One of the problems solved by the research is the integration of real operating modes and conditions (applied thermal insulation solution) into the production decision algorithms. The developed methodology allows not only to plan the operating modes of the cogeneration plant, but also to evaluate the efficiency of the battery solution. This study shows the developed methodology for calculating heat loss for a heat accumulator depending on the operating mode and the need to introduce a correction coefficient. When determining the total influencing expenses of the cost model of the heat accumulator operation mode, their mutual influence is shown and integrated into the decision-making algorithm for the next day's free-market conditions. The aim of the algorithm is maximally increasing the total gross revenue threshold for the planning of cogeneration operations and to exclude operating modes that may cause losses.

Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered – the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms.

The privatization of distribution systems has resulted in the development of multiple-microgrid (multiple-MG) systems where each microgrid independently operates its local resources. Moreover, the high integration of independent distributed energy sources could lead to operational issues such as grid congestion in future distribution systems. Therefore, this paper provides a transactive-based energy management framework to operate multiple-MG distribution systems; while, alleviating grid congestion in a decentralized manner. In this respect, alternating direction method of multipliers (ADMM) is considered to develop an operational framework that copes with distributed nature of multiple-MG systems. In this context, a novel procedure in the context of ADMM is proposed to distributedly determine transactive coordinator signals which address energy prices as well as power losses and grid congestions. Furthermore, each MG takes into account stochastic programming and the conditional value-at-risk index to handle the uncertainty of its operational scheduling. At last, the proposed framework is applied on IEEE 37-bus and 123-bus test grids to investigate its efficacy in distributed energy management of multiple-MG systems.

International audience; Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

This paper deals with the home health care service (HHCS) which is defined as a set of medical, paramedical and social services delivered to patients in their domicile rather than in hospital. To support decision making in HHC, optimization models have been used. However, several of those models are deterministic and do not address the dynamical and uncertainty aspects of the system and variability of some patient data. The HHC scheduling problems are facing more and more complex and specific constraints. These constraints have to be respected, meanwhile the problem objective is optimized under parameters uncertainties. This paper aims to formulate a model that integrated home care scheduling problem while taking into account human aspect -- the behavior of patients, and also another aspect like travel time uncertainty and dynamic behavior of involved medical team and social actors in HHC. Robust approaches are adopted to model and handle this uncertainty.

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI).

This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework. Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable (i.e. high-grade) and undesirable (i.e. low-grade or uneconomic) materials and ensure the delivery of only selected quantity of high quality (or high-grade) material to energy, water, and cost-intensive processing plant. The model maximizes the net present value under a range of operational and processing constraints. Given that the proposed model is computationally complex, the authors employ a data pre-processing procedure and then evaluate the performance of the model at several practical instances using computation time, optimality gap, and the net present value as valid measures. In addition, a comparison of the proposed and traditional (without Grade Engineering) models reflects that the proposed model outperforms the traditional formulation.

Energy efficiency plays an increasingly important role not only in supply chains, but also in in-plant supply systems. Manufacturing companies are increasingly using energy-efficient material handling equipment to solve their in-plant material handling tasks. A new example of this effort is the use of drones for in-plant transportation of small components. Within the frame of this article, a new AGV-drone joint in-plant supply model is described. The joint service of AGV-based milkrun trolleys and drones makes it possible to optimize the in-plant supply in production lines. This article discusses the mathematical description of AGV-drone joint in-plant supply solutions. The numerical analysis of the different AGV-drone joint in-plant supply solutions shows that this new approach can lead to an energy consumption reduction of about 30%, which also has a significant impact on GHG emission.

In this paper, we are discussing a research project aiming to optimize the scheduling of production orders within a real application in the packaging field. As a first approach, we model the problem as an extended version of the hybrid and flexible flowshop scheduling problem with precedence constraints, parallel machines, and sequence-dependent setups. The optimization objective considered is the minimization of the total tardiness. To tackle this problem, we use two methodologies: mixed-integer linear programming (MILP) and constraint programming (CP). These two models were further extended by adding resource calendar constraints named also availability constraints; this implies that the tasks should be scheduled only when the machine is available. The different proposed models were compared to each other on a set of generated benchmarks that reflect the specific properties of the industrial partner. Finally, as the studied configuration relies on practical real-world application, where thousands of orders are produced monthly, a novel dedicated heuristic was designed to address the need for quick solutions. The latter outperforms the other proposed algorithms for expected total tardiness minimization. The proposed problem can be readily modified to suit a wide range of real-world situations involving the scheduling of activities that share similar characteristics.

The integration of battery energy storage systems (BESS) in the electrical grid is accelerating to mitigate the challenges associated with the rapid deployment of low carbon technologies (LCTs). This work investigates the ability of BESS to provide the power networks with important ancillary services such as peak shaving and grid power levelling through two case studies conducted in collaboration with Northern Ireland's distribution network operator. A powerful approach consisting of two strategies is developed to operate the BESS powerfully to enhance the operation of the distribution network. The first strategy is day‐ahead scheduling that aims to dispatch the distributed BESS to smooth the grid power and mitigate voltage and lines stresses. A powerful demand forecasting algorithm is utilised to efficiently apply the day‐ahead scheduling. The second strategy is a real‐time operation to flatten the grid power which can be used separately or to adjust the results obtained from the day‐ahead strategy against the forecasting errors. The proposed approach was validated using real measurements and applied to an 11 kV distribution network located in Northern Ireland, UK. The BESS expected revenues from the participation in different services available in the island of Ireland are quantified and the degradation is considered.

Goal: This research aims to investigate how to maximize the throughput and reduce the waiting time for oncological patients of a radiotherapy process. Design / Methodology / Approach: A case study was conducted to develop a simulation model integrated with optimization techniques, including an expert system to support everyday decision making in the operation. Results: The experiments demonstrated that the approach was able to identify better configurations of the process, improving productivity without deteriorating quality. Results also considered the future acquisition of a new linear accelerator allowing the operation to plan its ramp up accordingly. Another result is the devised tool to allow the operation to run its own simulation studies. Limitations of the investigation: The results are valid for processes with similar characteristics of the one studied in the present paper, closing the scope at the treatment stage. Practical implications: This work demonstrates the possibility of applying modern simulation techniques in radiotherapy, not only enabling improvements for the present situation but also integrating the model to the operation to support everyday decision making. Originality / Value: The number of details captured by the model is significantly higher than others studies. The integration of the methodology to the treatment process makes possible for the operation to continue benefiting from the model in a long term.

Charging coordination is employed to efficiently serve electric vehicle (EV) charging requests without overloading the distribution network. Parameters such as parking duration, battery state-of-charge (SoC), and charging amount are provided by EVs to the charging coordination center to schedule their charging requests efficiently. The existing literature assumes that the customers always provide correct information. Unfortunately, customers may provide false information to gain higher charging priority. Assessing the impact of cheating behavior represents a significant and open problem. Herein paper, the impact of providing false information (e.g., parking duration) on the efficiency of the charging coordination mechanism is investigated. The charging coordination strategy is formulated as a linear optimization problem. Two different objectives are used to assess the impact of the objective function on the amount of performance degradation. Our investigations reveal that the degradation of the efficiency of the charging coordination mechanism depends on the percentage of cheating customers and cheating duration versus the typical parking duration. In addition, the impact of cheating behavior increases with the number of deployed chargers. Thus, the severity of the cheating impact will increase in the future as more fast chargers are allocated in charging networks.

NASA’s Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its’ services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN’s limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm’s performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation.

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.

This paper presents the operating cost optimization of an islanded microgrid through day-ahead scheduling. The results are validated using simulations and experiments. The cost optimization is achieved using 3 methods, 2 of which are general ones, Harmony Search Algorithm and Particle Swarm Optimization, and one, named Stochastic-Dynamic Method, is created for the microgrid and the subsequent optimization problem. The results generated using the 3 algorithms are compared and then tested using the Simulink model of the microgrid. The simulations are followed by the experimental validation of the Stochastic-Dynamic Method. Finally, a real-time control method is created and simulated with the purpose of overcoming the limitations that Day-Ahead Scheduling presents.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.

International audience; Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private. With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques). Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective. In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution. We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions. We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem. We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior. These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.

Current aircraft maintenance ensures safe and reliable flight operations based on inspections repeated at fixed time intervals. The time interval between inspections is often much shorter than the average life of aircraft components, in an effort to timely detect potential failures. While this approach successfully prevents most potential failures, it is not the most efficient since airlines frequently need to ground aircraft for visual inspections. Furthermore, most inspections do not find any fault; thus, nothing is actually repaired after these frequent inspections.Predictive aircraft maintenance (PdAM) is a newly emerging approach to maintenance which is expected to be more efficient, while providing the same or higher levels of reliability. PdAM uses the data produced by the plethora of on-board sensors installed on modern aircraft to monitor the health condition of aircraft components, without the need to ground these aircraft for visual inspections. These health condition data are analyzed to predict the Remaining-Useful-Life (RUL) of aircraft components. The core idea of PdAM is to plan maintenance tasks based on the estimated RUL. PdAM is currently not fully implemented in practice, however. Regulatory bodies have only recently started to discuss the integration of aircraft health monitoring (AHM) systems into aircraft maintenance process.This dissertation aims to identify and address the challenges in implementing PdAM. The first challenge is the lack of mathematical models to assess the performance of PdAM. Before implementing PdAM in actual aircraft, the expected performance needs to be quantified to understand the impact on reliability and cost-efficiency. Although a few studies have proposed aircraft maintenance models, these studies only consider cost as a single performance metric. However, it is clear that aircraft maintenance should also be evaluated in terms of reliability and other key performance indicators (KPIs) representing the various (and often conflicting) interests of all stakeholders involved. In this dissertation, we construct a mathematical model of PdAM to evaluate the balance in maximizing various KPIs altogether. Our model captures the stochastic degradation and failure of aircraft components, and the interactions between stakeholders during the maintenance decision making process.The second challenge for PdAM is the lack of optimization frameworks to plan PdAM considering RUL prognostics. In the last decades, most researchers have focused on predicting the RUL of aircraft components, but only a few studies address the question of how to actually integrate RUL prognostics into maintenance planning. Aircraft maintenance planning is a very complex process that should consider different aircraft components, a fleet of aircraft, their flight schedules, the limited hangar availability, tight safety margins, and strict regulations. Considering all these together in a single optimization framework is overly demanding. Hence, in this dissertation, the optimization of the PdAM planning is performed at three levels: component level, fleet level, and strategy level.At the component level, we propose probabilistic RUL prognostics and a deep reinforcement learning (DRL) approach for predictive maintenance planning. The probabilistic RUL prognostics estimate the probability distribution of RUL, instead of a point-estimation. This approach quantifies the uncertainty associated with RUL prognostics. Based on the estimated RUL distribution, the DRL approach determines the optimal moment to replace an aircraft component. In the case study for the maintenance of aircraft turbofan engine, the proposed DRL approach reduces the total maintenance cost by 29.3\% and prevents 94.3\% of unscheduled maintenance, compared to the case when the point-estimation of RUL is used.At the fleet level, PdAM is planned by simultaneously considering a fleet of aircraft having multiple components. The main interest of fleet-level PdAM is to integrate RUL prognostics and operational requirements, such as the flight schedules and the limited hangar availability. We formulate these in an integer linear programming problem that minimizes the cost of fleet-level PdAM. This approach reduces the usage of hangars by grouping the schedule of maintenance tasks when the RUL of the components are similar. Considering the maintenance of aircraft landing gear brakes for a fleet of aircraft, the total maintenance cost is reduced by 20\% compared to the traditional maintenance strategies.At the maintenance strategy level, we optimize the design parameters of PdAM, such as safety margins and thresholds of RUL, considering multiple objectives: cost-efficiency and reliability. Since this multi-objective optimization problem is computationally intensive, we propose an efficient search algorithm using Gaussian process (GP) learning models to identify Pareto optimal design parameters of PdAM. Compared to other state-of-the-art multi-objective optimization algorithms, the proposed GP learning-based algorithm identifies more Pareto optimal solutions within the same computational time. The identified Pareto front shows that PdAM using RUL prognostics dominates traditional maintenance strategies by achieving the beneficial balance between efficiency and reliability indices. With only a 1\% reduction in the efficiency index, the Pareto optimal PdAM strategy achieves a 95\% improvement in the reliability index.The three optimization frameworks at the three different levels of PdAM are proposed and illustrated for case studies on the maintenance of aircraft engines and landing gear brakes. These case studies show three main benefits of PdAM: 1) the maintenance cost is minimized by scheduling maintenance tasks only when necessary; 2) failures and unscheduled maintenance are prevented by considering RUL prognostics; and 3) Pareto optimal performance is achieved considering the balance between reliability and efficiency.Finally, this dissertation identifies the emerging challenges associated with the introduction of PdAM. Such challenges are often attributable to the introduction of new technologies, such as aircraft health monitoring systems, RUL prognostics algorithms, and decision support systems to plan PdAM. Based on structured brainstorming sessions with domain experts and end-users, three major challenges of future PdAM are identified: 1) the (often unknown) reliability of new technologies, 2) the timeliness and accuracy of communication between the stakeholders of the new PdAM, and 3) the end-users' trust in the new technologies.Throughout this dissertation, we have focused on decision support systems of PdAM, in the form of optimization frameworks. These frameworks provide substantial support for the implementation of PdAM in practice. Even so, it remains future work to build users' trust in PdAM, to integrate it into strict aviation legislation, and to adopt PdAM at the business level. The strongest support for trust, legislation, and business regarding PdAM should be based on mathematical models and optimization frameworks. Therefore, this dissertation is a starting point for an informed discussion on the future of predictive aircraft maintenance.

Gozupek, Didem/0000-0001-8450-1897 WOS: 000522523000007 The problem of inefficient and unevenly distributed spectrum usage in industrial wireless networks has led to the emergence of the concept of industrial cognitive radio (CR) networks, which have particularly important applications in automotive industry. Industrial CR networks are planned to function in a wide spectrum range; therefore, they have high energy consumption because of frequency switching while other wireless technologies do not have this problem. A distinctive feature of this switching cost is that it depends on the wideness between the two frequency bands. In this article, we formulate the joint frequency assignment and scheduling problem for multihop industrial CR networks with a single transceiver by considering varying amounts of energy consumption that occurs while CR devices switch to different frequency bands. Our optimization problem, which we formulate as an integer linear program, minimizes the energy cost related to frequency switching while making frequency and time slot allocation to the cognitive devices. We prove that even on star graphs, our formulated problem is inapproximable within any polynomial-time computable function $f(n)$ in addition to being $\textsc {NP}\text {-Hard}$ in the strong sense. Therefore, we propose a polynomial-time heuristic algorithm to solve the energy consumption problem due to channel switching. Simulation results demonstrate that the performance of our heuristic algorithm is very close to the results obtained from the integer linear programming implementation by CPLEX optimization software. We also compare our proposed method with the corresponding constant energy consumption for frequency switching case and two state-of-the-art algorithms and demonstrate that taking into account the different energy consumption while switching to different frequency bands is vital for joint frequency assignment and scheduling in multihop industrial CR networks with a single transceiver. Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [113E567] This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no.113E567.

A battery-swapping station (BSS) can serve as a flexible source in distribution systems, since electric vehicle (EV) batteries can be charged at different time periods prior to their swapping at a BSS. This paper presents an EV battery service transformation from charging to swapping batteries for EVs for the capacity enhancement of a distribution system. A novel mathematical model is proposed to optimally quantify and maximize the flexibility of BSS loads in providing demand response for the utility operator while considering technical operations in the distribution grid. Case studies and numerical findings that consider data from the National Household Travel Survey and a 32-bus distribution system are reported and discussed to demonstrate the effectiveness of the proposed model. Offering battery-swapping services helps reduce not only the peak load, but also the station operation cost.

Preventive maintenance (PM) is a maintenance program with activities created at a determined interval or according to certain principles, designed to reduce the likelihood of failure or deterioration of item performance. This aims to improve overall reliability and system availability. In this research, a preventive maintenance schedule (PMS) was designed for electricity and desalination of water in power plants, subject to meeting relevant constraints. The proposed methodology is used to generate a PMS for the boilers, turbines, and distillers. A nonlinear integer programming (NLIP) model was employed to address this problem. The results of the proposed method were compared with the PMS for a power station in Kuwait. The results were better in terms of the volume of production and in terms of the gap between the available production and demand in order to continue providing consumers with electricity and water without a shortage in the event of a breakdown in equipment. It produces an improvement of 12.12% and 16.58% respectively, for water and electricity. Furthermore, the sensitivity and robustness of the proposed method were analysed by increasing the maintenance duration for some equipment, increasing the demand, and adding various additional conditions. In addition, a comparison of additional conditions with a binary problem method in terms of computer time for the search for an optimal solution was carried out, where the model provided an optimal solution in a reasonable time. Among the most important benefits that the user can obtain for this technique are extending the life of the equipment, increasing efficiency, and reducing expenses.

Current socio-economic conditions impose certain requirements on construction and renovation projects that need new methods making evaluations of construction work performance schedules more reliable. Towards this end, the authors propose a consolidated methodology of construction work scheduling based on the interval estimation technique. The boundaries of the interval, as well as determining minimum and maximum construction time, are obtained by minimizing and maximizing the term of construction work performance by introducing random interruptions into successions of critical and subcritical works. Such reasons for interruptions as the failure of key construction machines, unavailability of labor resources, and accidental man-induced or natural impacts are considered. Risk calculations are employed to devise an approach to evaluating the reliability of construction schedules, including minor schedules designated for single-facility projects and major schedules developed for projects that encompass the construction of groups of buildings and structures. Projects on construction of monolithic reinforced concrete frames of buildings were used to verify the efficiency of the proposed approaches to work performance scheduling.

This study undertakes the line balancing problem while allocating reconfigurable machines to different workstations. A multi-objective model is used to analyze the position of workstations, assignment of configurations to workstations, and operation scheduling in a reconfigurable manufacturing environment. A model is presented that comprises the objectives of the Total Time (TT), the Line Efficiency Index (LEI), and the Customer Satisfaction Index (CSI). The objective is to minimize the completion time and maximize the efficiency of a production line. The proposed model combines the Simple Line Balancing Problems Type 2 and Type E in the form of SLBP-2E. The presented problems are addressed by using a heuristic solution approach due to non-polynomial hard formulation. The heuristic approach is designed to assess different solutions based on no repositioning, separate repositioning of workstations and configuration, and simultaneous repositioning of workstations and configurations. A detailed assessment is presented regarding the efficiency as well as the effectiveness of proposed approaches. Finally, conclusions and future research avenues are outlined.

Background: Periodization implies the systematic planning of training and competition with the goal of reaching the best possible performance in the most important competition. In team sports, this consists of finding a flight-and-practice schedule that maximizes the opportunities to perform the periodized contents (e.g., trips, practices, games, and days off). This process is conducted whilst considering known constraints (e.g., competitive schedule, roster availability, weather, especial events, holidays, or emotional effect of days away). The way a scheduling decision support system (DSS) leads users to make a decision should allow for flexibility, whilst minimizing users' confusion and facilitating the understanding of the recommendation given by the scheduling decision support system. Traditional approaches to solving scheduling problems use either simulation models, analytical models, heuristic approaches or a combination of these methods. When it comes to evaluate how the scheduling DSS is performing, three overarching aspects need to be reviewed: context satisfaction, process efficiency, and output quality. Appropriate training periodization and scheduling of trips and training sessions are critical for teams to optimize training and recovery processes in order to maximize health and performance. This article presents a methodological framework for designing decision-support systems for scheduling in professional team sports.

Surface ships in the Navy require planned and unplanned pier-side maintenance. These maintenance jobs, known as availabilities, are contracted out to private shipyards. Ship maintenance schedules must meet the Navy’s operational requirements and stay within the capacity of the contracted shipyards. At the same time, it is important to minimize workload fluctuation in a port to help private shipyards train and maintain a skilled workforce. Building on recent work that schedules availabilities in a single port to minimize workload fluctuation, this thesis develops a port loading model to minimize workload fluctuation for all regional ports in the Area of Responsibility by allowing some ships to receive maintenance work out of their home ports. Scheduling availabilities across multiple ports simultaneously to level the workload in each port has two additional benefits: First, an increase in the number of eligible companies who can bid on the maintenance job will drive down the cost for the Navy. Second, allowing more flexibility to assign availabilities to different ports has the potential to further level the workload at these ports. In a case study on three ports in the West Coast over a six-year period, we demonstrate the effectiveness of the multi-port loading model. Outstanding Thesis Ensign, United States Navy Approved for public release. Distribution is unlimited.

The thesis defense timetabling problem is a fascinating and original NP-hard optimization problem. The problem involves assigning the participants to defense sessions, composing the relevant committees, satisfying the constraints, and optimizing the objectives. This study defines the problem formulation that applies to Universitas Multimedia Nusantara (UMN) and use the particle swarm optimization (PSO) algorithm to solve it. As a demonstration of concept and viability, the proposed method is implemented in a web-based platform using Python and Flask. The implementation is tested and evaluated using real-world instances. The results show that the fastest timetable generation is 0.18 seconds, and the slowest is 21.88 minutes for 25 students and 18 department members, without any violation of the hard constraints. The overall score of the EUCS evaluation for the application is 4.3 out of 6.

Keuliling Reservoir which is located in Kuta Cot Glie, Aceh Besar is one of the reservoirs that have a function as a provider of water for irrigation needs, supports food self-sufficiency programs and others. The reservoir area has limited volume of water so, the most important water-saving irrigation water should be done to determine the scheduling of the distribution of irrigation water to the area of rice fields. The purpose of this research is to develop a method that can be used to the calculation of scheduling and can be applied to the irrigation of the Keuliling Reservoir watershed in Aceh Besar. In this study, the Genetic Algorithm method was used to determine the optimization of the Keuliling Reservoir in the Aceh Besar. From the analysis that has been done, by test of the Genetic Algorithm method, the minimum optimization of the time and discharge of irrigation water is obtained. While the visualization of these results can be displayed using the Matlab GUI software. The remaining disposal of irrigation water into the Keumireu watershed at one hour of irrigation has the minimum value of 600 Liters / Hour. The best irrigation time obtained is based on the calculation of 72 Genetic Algorithm methods in which the amount of water entering the rice field area is 1,038,211,600 Liters and 257,788,400 Wasted in the river. Based on the results of testing the application of the Keuliling Reservoir watershed irrigation scheduling in Aceh Besar that was built with Genetic Algorithms can solve optimal scheduling problems.

This paper discusses a security-constrained integrated coordination scheduling framework for an integrated electricity-natural gas system (IEGS), in which both tight interdependence between electricity and natural gas transmission networks and their distinct dynamic characteristics at different timescales are fully considered. The proposed framework includes two linear programming models. The first one focuses on hour-based steady-state coordinated economic scheduling on power outputs of electricity generators and mass flow rates of natural gas sources while considering electricity transmission $N-1$ contingencies. Using the steady-state mass flow rate solutions of gas sources as the initial value, the second one studies second-based slow gas dynamics and optimizes pressures of gas sources to ensure that inlet gas pressure of gas-fired generator is within the required pressure range at any time between two consecutive steady-state scheduling. The proposed framework is validated via an IEGS consisting of an IEEE 24-bus electricity network and a 15-node 14-pipeline natural gas network coupled by gas-fired generators. Numerical results illustrate the effectiveness of the proposed framework in coordinating electricity and natural gas systems as well as achieving economic and reliable operation of IEGS.

Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common tradeoff between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common tradeoff relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.

Practical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

The planning of attractive and cost efficient public transport schedules, i.e., timetables and corresponding vehicle schedules is a highly complex optimization process involving many steps. Integrating robustness from a passenger’s point of view makes the task even more challenging. With numerous different definitions of robustness in the literature, a standard way to evaluate the robustness of a public transport system is to simulate its performance under a large number of possible scenarios. Unfortunately, this is computationally very expensive. In this paper, we therefore explore a new way of such a scenario-based robustness approximation by using regression models from machine learning. Training of these models is based on carefully selected key features of public transport systems and passenger demand. The trained model is then able to predict the robustness of untrained instances with high accuracy using only its key features, allowing for a robustness oracle for transport planners that approximates the robustness in constant time. Such an oracle can be used as black box to increase the robustness of public transport schedules. We provide a first proof of concept for the special case of robust timetabling, using a local search framework. In computational experiments with different benchmark instances we demonstrate an excellent quality of our predictions.

The scope of the paper is the problem of the economic effectiveness of the construction project from the contractor’s point of view. The two main construction contract types are taken under consideration: the unit price contract based on the bill of quantities and the lump sum contract. The net present value (NPV) is adopted in this paper as the basic economic efficiency criterion of the construction project. The NPV of the project for the contractor is calculated here as the sum of all future monthly contractor’s cash flows (both incoming and outgoing) discounted by using the monthly cost of contractor’s capital invested into the project. For the cash flow calculations, two basic payment methods are considered, depending on the type of the contract: the monthly payments in the case of the unit price contract and the payments for the WBS (Work Breakdown Structure) billing elements in the case of the lump sum contract. The contractor’s monthly cash flow analysis also makes it possible to determine the monthly cash requirements for the contractor to finance the works each month and the total cash requirement to finance the project as the whole. This allows the contractor to correctly plan the financing of the project depending on the type of contract and the payment method. Moreover, using the optimization techniques known in the scheduling theory, for example based on the priority rules as in the case of the RCSP, one may try to improve the economic effectiveness of the project or to lower the cash requirement for the project.However, as shown by the authors, it does not have bring the expected results in the case of the lump sum contract.

In this study, a workforce scheduling and balancing problem is solved in unpaced sub-assembly lines with buffers feeding the paced body assembly line of a car manufacturer. The goal is to determine the minimum workforce required to process split lots at sub-assembly stations to feed the paced line over a periodic time window. Limited by a given buffer capacity at each station but with flexible start times for each split lot, an efficient workforce scheduling is possible to prevent shortages in downstream stations. Therefore, a stock-continuity equation has been proposed yielding the size of those split lots. Next, a single-objective Mixed Integer Programming (MIP) model is formulated for the problem as a combination of two implicitly weighted goals to minimise the workforce and the unbalanced workloads. The problem is a variant of workforce scheduling and routing problem with time windows and negligible walking distances. Due to the non-deterministic polynomial-time -hardness of the problem, we proposed an improved Artificial Bee Colony (ABC) algorithm named as discrete ABC with solution acceptance rule and multi-search (SAMSABC). The proposed algorithm is compared with different variants of ABC and other well-known metaheuristic algorithms such as Particle Swarm Optimisation and Differential Evolution on generated test cases. The computational results demonstrate the superiority of the proposed ABC algorithm and reveal that the SAMSABC can achieve accurate results within short computational times.

Currently, many squadrons in the Naval Aviation community handwrite their daily flight schedules, which is typically an all-day effort. This thesis creates an optimization model to build schedules computationally instead of manually for Navy’s Training Squadron 22 (VT-22), which specializes in Intermediate Jet and Advanced Strike training. An optimized scheduling process can improve the efficiency of the training pipeline, saving money and improving aviation readiness. A preliminary model, Training Event Scheduling Tool (TEST), was provided to VT-22 in 2019 by Meditz. TEST takes a spreadsheet containing student prerequisites, instructors, and events, and creates a daily or weekly schedule at an hourly resolution. This thesis formulates and tests a revised integer linear program, TEST-2, an enhancement to TEST that models weather, substitutable events, and student currency. TEST-2 creates daily schedules in less than 10 minutes and weekly schedules in about four hours. These schedules consider a majority of the necessary constraints for a useable schedule. For a sample week’s input provided by VT-22, TEST-2 schedules about 60 more events over the course of the week than were manually scheduled and completed. Currently, many events are cancelled due to instructor non-availabilities, weather, and jet availability. Because TEST-2 considers these three factors in building its schedules, cancellations due to these factors are minimized. Ensign, United States Navy Approved for public release. distribution is unlimited

Resource allocation in modern wireless networks is constrained by increasingly stringent real-time requirements. Such real-time requirements typically come from, among others, the short coherence time on a wireless channel, the small time resolution for resource allocation in OFDM-based radio frame structure, or the low-latency requirements from delay-sensitive applications. An optimal resource allocation solution is useful only if it can be determined and applied to the network entities within its expected time. For today's wireless networks such as 5G NR, such expected time (or real-time requirement) can be as low as 1 ms or even 100 μs. Most of the existing resource optimization solutions to wireless networks do not explicitly take real-time requirement as a constraint when developing solutions. In fact, the mainstream of research works relies on the asymptotic complexity analysis for designing solution algorithms. Asymptotic complexity analysis is only concerned with the growth of its computational complexity as the input size increases (as in the big-O notation). It cannot capture the real-time requirement that is measured in wall-clock time. As a result, existing approaches such as exact or approximate optimization techniques from operations research are usually not useful in wireless networks in the field. Similarly, many problem-specific heuristic solutions with polynomial-time asymptotic complexities may suffer from a similar fate, if their complexities are not tested in actual wall-clock time. To address the limitations of existing approaches, this dissertation presents novel real- time solution designs to two types of optimization problems in wireless networks: i) problems that have closed-form mathematical models, and ii) problems that cannot be modeled in closed-form. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, which breaks an original optimization problem into a large number of small and independent sub-problems, (ii) search intensification, which identifies the most promising problem sub-space and selects a small set of sub-problems to match the available GPU processing cores, and (iii) GPU-based large-scale parallel processing, which solves the selected sub-problems in parallel and finds a near-optimal solution to the original problem. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi in the unlicensed spectrum. The real-time requirement for finding the optimal channel division and LTE resource allocation solution is on 1 ms time scale. This problem involves the optimal division of transmission time for LTE and Wi-Fi across multi- ple unlicensed bands, and the resource allocation among LTE users within the LTE's "ON" periods. We formulate this optimization problem as a mixed-integer linear pro- gram and prove its NP-hardness. Then by exploiting the unique problem structure, we propose a real-time solution design that is based on problem decomposition and GPU-based parallel processing techniques. Results from an implementation on the NVIDIA GPU/CUDA platform demonstrate that the proposed solution can achieve near-optimal objective and meet the 1 ms timing requirement in 4G LTE. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal (with respect to the PF objective) resource allocation and MCS selection solution is 125 μs (under 5G numerology 3). In this problem, we need to allocate frequency-time resource blocks on an operating channel and assign modulation and coding scheme (MCS) for each active user in the cell. We present GPF+ — a GPU based real-time PF scheduler. With GPF+, the original PF optimization problem is decomposed into a large number of small and in- dependent sub-problems. We then employ a cross-entropy based search intensification technique to identify the most promising problem sub-space and select a small set of sub-problems to fit into a GPU. After solving the selected sub-problems in parallel using GPU cores, we find the best sub-problem solution and use it as the final scheduling solution. Evaluation results show that GPF+ is able to provide near-optimal PF performance in a 5G cell while meeting the 125 μs real-time requirement. For the second type of problems, where there is no closed-form mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under DL/DRL, we employ deep function approximators (neural networks) to learn the unknown objective function of an optimization problem, approximate an optimal algorithm to find resource allocation solutions, or discover important mapping functions related to the resource optimization. To meet the real-time requirement, we propose to augment DL or DRL methods with optimization techniques at the input or output of the deep function approximators to reduce their complexities and computational time. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms (under 5G numerology 0). A major challenge in solving this problem is that it cannot be modeled using closed-form mathematical expressions. To address this issue, we develop a model-free DRL approach which employs a deep neural network to learn an optimal algorithm to allocate the URLLC puncturing over the operating channel, with the objective of minimizing the adverse impact from URLLC traffic on eMBB. Our contributions include a novel learning method that exploits the intrinsic properties of the URLLC puncturing optimization problem to achieve a fast and stable learning convergence, and a mechanism to ensure feasibility of the deep neural network's output puncturing solution. Experimental results demonstrate that our DRL-based solution significantly outperforms state-of-the-art algorithms proposed in the literature and meets the 1 ms real-time requirement for dynamic multiplexing. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs (under 5G numerology 3). The objective is to have eMBB meet a given block-error rate (BLER) target under the adverse impact of URLLC puncturing. Since this problem cannot be mathematically modeled in closed-form, we proposed a DL-based solution design that uses a deep neural network to learn and predict the BLERs of a transmission under each MCS level. Then based on the BLER predictions, an optimal MCS can be found for each transmission that can achieve the BLER target. To meet the 5G real-time requirement, we implement this design through a hybrid CPU and GPU architecture to minimize the execution time. Extensive experimental results show that our design can select optimal MCS under the impact of preemptive puncturing and meet the 125 μs timing requirement. Doctor of Philosophy In modern wireless networks such as 4G LTE and 5G NR, the optimal allocation of radio resources must be performed within a real-time requirement of 1 ms or even 100 μs time scale. Such a real-time requirement comes from the physical properties of wireless channels, the short time resolution for resource allocation defined in the wireless communication standards, and the low-latency requirement from delay-sensitive applications. Real-time requirement, although necessary for wireless networks in the field, has hardly been considered as a key constraint for solution design in the research community. Existing solutions in the literature mostly consider theoretical computational complexities, rather than actual computation time as measured by wall clock. To address the limitations of existing approaches, this dissertation presents real-time solution designs to two types of optimization problems in wireless networks: i) problems that have mathematical models, and ii) problems that cannot be modeled mathematically. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, (ii) search intensification, and (iii) GPU-based large-scale parallel processing techniques. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi users in the same (unlicensed) spectrum. The real-time requirement for finding the optimal LTE resource allocation solution is on 1 ms time scale. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal resource allocation and modulation and coding scheme (MCS) for each user is 125 μs. For the second type of problems, where there is no mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs.

Part 20: Industrial Workshop; International audience; Due to the growth of e-commerce, retail order picking problems are nowadays one of the most challengeable issues in logistics networks. Conventionally, semi-automated picking systems are implemented, using a conveyor for transportation and operators for preparation. Customer demand being volatile, the sustainability of the system relies on digitalization and reconfigurability, regarding both the physical and information system. From an operational standpoint, the performance is driven by scheduling approaches to manage the bins’ flow however the system is prone to uncertainty. We develop a decision tool based on a simulation model to accurately reflect the real-life situation and the human-conveyor interaction. Our aim is to evaluate the practical flow of the system with uncertain preparation times compared to a theoretical schedule obtained by a deterministic optimization approach. Numerical experiments including an industrial case study are presented and discussed.

Recently, intelligent scheduling approaches using surrogate models have been proposed to efficiently allocate volatile tasks in heterogeneous fog environments. Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached. However, deterministic surrogate models, which estimate objective values for optimization, do not consider the uncertainties in the distribution of the Quality of Service (QoS) objective function that can lead to high Service Level Agreement (SLA) violation rates. Moreover, the brittle nature of DNN training and prevent such models from reaching minimal energy or response times. To overcome these difficulties, we present a novel scheduler: GOSH i.e. Gradient Based Optimization using Second Order derivatives and Heteroscedastic Deep Surrogate Models. GOSH uses a second-order gradient based optimization approach to obtain better QoS and reduce the number of iterations to converge to a scheduling decision, subsequently lowering the scheduling time. Instead of a vanilla DNN, GOSH uses a Natural Parameter Network to approximate objective scores. Further, a Lower Confidence Bound optimization approach allows GOSH to find an optimal trade-off between greedy minimization of the mean latency and uncertainty reduction by employing error-based exploration. Thus, GOSH and its co-simulation based extension GOSH*, can adapt quickly and reach better objective scores than baseline methods. We show that GOSH* reaches better objective scores than GOSH, but it is suitable only for high resource availability settings, whereas GOSH is apt for limited resource settings. Real system experiments for both GOSH and GOSH* show significant improvements against the state-of-the-art in terms of energy consumption, response time and SLA violations by up to 18, 27 and 82 percent, respectively., Comment: Accepted in IEEE Transactions on Parallel and Distributed Systems (Special Issue on PDC for AI), 2022

Multidisciplinary conferences are the types of conferences that allow the presentation of studies in different disciplines such as naturel science, social sciences, health and arts sciences etc. In these conferences, determining the days, halls and sessions and making presentations according to the related main-scope and sub-scopes, are an important limited optimization problem. The fact that there are presentations from different disciplines in same session during the conference is a big problem for the conference participants. In this study, the solution of the scheduling problem of multidisciplinary conferences with Genetic Algorithm approach is discussed. The basic concepts of Genetic Algorithm are given and conference scheduling schemes and the elements to be considered in scheduling are indicated. In this study, two different multidisciplinary conference datasets have been used. An application has been developed with Genetic Algorithm in C# language under some constraints of the different days, different sessions and different rooms. As a result of the study, it is seen that the solutions obtained with Genetic Algorithm are generally close to optimum solutions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. 2-s2.0-85109890102

This paper proposes an operational planning strategy for battery energy storage systems (BESS) in medium voltage distribution networks. This strategy determines the optimal location and size for BESS as well as the discharging and charging schedules. The objective of this methodology is to improve reliability and stability by relieving distribution network congestion, such as voltage violations and lines overloading. Particle Swarm, Firefly, Novel Bat, Krill herd and Coyote optimization algorithms have been utilized to find the optimal solutions that improve the network’s performance by mitigating network stresses. The strategy is implemented and validated using two networks; a 53-node test feeder located in Northern Ireland and the 33-bus radial distribution network. Actual demand measurements were used and high uptake scenarios for low carbon technologies were investigated.

Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution.

The present paper deals with the provision of ancillary services to the power system through the scheduling of the charging requests of electric vehicles. The scheduling process is based on a hybridization of the Artificial Bee Colony algorithm (h-ABC). The starting of e-cars charging is supposed to be postponed according to predetermined deadlines set by users. With a precautionary approach, charging processes are assumed non-preemptable. To highlight the potential benefits of the approach in a real-life scenario, a techno-economic assessment is performed, taking as reference the rules in place in Italy and using real data from the market.

Production activity control approaches, methods, and mechanisms have been widely applied over the last decades, and continue to be of utmost importance nowadays, within the context of the currently fast-growing Industry 4.0 era. In this paper, a Simio-based simulation model is proposed and its application in a printing factory is illustrated. The main aim of this work consists of providing general production planning improvements in the considered factory, with a special focus on the reduction of setup time. The proposed model is based on several distinct production activity control mechanisms, for instance, the CONWIP and the Routing Group mechanisms from Simio, which did enable to reach good improvements regarding a set of performance measures considered, including machines’ setup time reduction, along with the maximization of the percentage of products delivered on time. Future work is also planned to be carried out to improve other kinds of performance measures, and by using other types of production activity control mechanisms, to be further applied in other industrial companies and sectors., This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.