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Consumers are increasingly seeking products with a higher degree of customization, and this has intensified the search for more flexible production systems. Unexpected phenomena, including those impacting the economy and public health, have shown the need for high reactivity by manufacturers. Reconfigurable Manufacturing Systems are one of the means to respond to new market needs. They are designed to be able to quickly change their structure and thus adapt their functionalities and capabilities. A central question with regard to these types of systems is when and how they should be reconfigured. It is, however, a question that needs to be addressed simultaneously alongside the classic resource allocation and scheduling problems, since the problems are interdependent. This paper proposes four models that address the problem sequentially, partially sequentially, and integrally. Our numerical analysis demonstrates the advantage and importance of treating these problems in an integrated manner. In addition, we provide and compare three exact methods for the integrated process planning, layout, and scheduling optimization problems. The exact methods are integer linear programming, constraint programming, and constraint programming with search strategies, which our numerical examples show to be greatly superior to the two others. Finally, we present a problem at an industrial scale. [ABSTRACT FROM AUTHOR]

The production process of precast components is labor-intensive, involving various manual tasks. The physically demanding tasks usually result in fatigue and overexertion of workers, leading to increased occupational health risks and reduced productivity. An appropriate work–rest strategy is recognized to effectively promote both workers' health and productivity, while it has rarely been studied in the field of the construction industry. To narrow this gap, this study developed a mixed-integer linear programming approach to optimize the work–rest schedule by integrating workers' overexertion. The objective is to maximize the productive time affected by the workers' accumulative fatigue and recovery. Also, the optimized work–rest strategy can be highly customized by considering personalized factors and task characteristics. Experimenting with a case study compared the default rest schedule provided by the superintendent onsite with the optimal solution solved from the developed model. Results suggested that up to 20% improvement in productive time can be achieved, especially for the task with a relatively higher workload. Computational experiments were conducted to evaluate the sensitivity of total productive time to various personalized and task-specific factors. The proposed method provides superintendents with an applicable strategy to improve workers' productivity and reduce their occupational risks resulting from overexertion. This study can promote the implementation of personalized occupational health management and support the improvement of regulations on the required rest with quantified evidence, thereby contributing to more reliable scheduling and sustainable workforce development for the construction industry. The research scope was limited to the precast production process, and further investigation on broader applications will be conducted. [ABSTRACT FROM AUTHOR]

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost. [ABSTRACT FROM AUTHOR]

This paper proposes a Mixed-Integer Linear Programming (MILP) optimization model for the scheduling problem of the interconnected microgrid system. The proposed model is capable of efficiently minimizing the microgrids' total operating costs and improving the entire system's reliability, as it is constrained based on enhancing the interconnected microgrids' reliability. The Expected Energy Not Supplied (EENS) is considered in order to ensure minimizing the interconnected microgrids' power deficiency. Furthermore, the proposed model has the capability to solve the optimization problem considering the islanded operation of the interconnected microgrids, i.e. when disturbances occur on the upstream grid. Numerical simulations on a test system containing three interconnected microgrids are performed to evaluate the effectiveness of the model and the results demonstrate the merits and features of the reliability-constrained optimal scheduling model in minimizing the interconnected microgrids' total operating costs and enhancing the interconnected system reliability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

More and more vehicles are connected today via emerging connected and automated vehicle (CAV) technologies. An intriguing application of CAVs is to cross intersections without stops through cooperative scheduling by traffic control infrastructure. Nonetheless, with the increase of CAVs' requests for green, two problems will surface: (I) accommodating too many CAVs' green requests will generate severe interruptions to general traffic; (II) simple scheduling policies like first-come-first serve is inappropriate due to heterogeneous importance of CAVs. To overcome these challenges, we present a mixed-integer linear programming (MILP) formulation for congestion-aware heterogeneous CAV scheduling problems at intersections in this paper. The objective is to ensure that intensive and heterogeneous green requests by CAVs can be scheduled at intersections while the mobility of background traffic is still maintained. The MILP formulation is developed in the context of discrete space-time and phase-time networks whose variables are space-time arc choice variables with respect to individual vehicles and phase-time arc choice variables. We also build an efficient search algorithm based on the "A-D curves" for real-time applications. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware CAV scheduling reveal promising results for real-world applications in the future. [ABSTRACT FROM AUTHOR]

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based $(1-1/e)$ (1 - 1 / e) -approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a $(1-\rho)(1-1/e)$ (1 - ρ) (1 - 1 / e) approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a $(1-\rho)(1-1/e-\varepsilon)$ (1 - ρ) (1 - 1 / e - ɛ) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively. [ABSTRACT FROM AUTHOR]

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission's quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe an integer linear programming model to schedule the maintenance crew and the maintenance tasks in a bus operating company. Design/methodology/approach: The proposed methodology relies on an integer linear programming model that finds feasible maintenance schedules. It minimizes the costs associated with maintenance crew and the costs associated with unavailability. The model is applied in a real-world case study of a Portuguese bus operating company. A constructive heuristic approach is put forward, based on solving the maintenance scheduling problem for each bus separately. Findings: The heuristic finds better solutions than the exact methods (based on branch-and-bound techniques) in a much lower computational time. Practical implications: The results suggest the relevance of such heuristic approaches for maintenance scheduling in practice. Originality/value: This proposed model is an effective decision-making support method that provides feasible maintenance schedules for the maintenance technicians and for the maintenance tasks in a fleet of buses. It also complies with several operational, technical and labour constraints. [ABSTRACT FROM AUTHOR]

The multiproduct pipeline network with multi-source and multi-terminal encompasses multi-point injection and multi-point distribution. Upon reorganization of batches at transfer depots, the intricacies of precise batch tracking become increasingly complex, thereby rendering the optimization of multiproduct pipeline network scheduling particularly arduous. In this paper, we introduce a dynamic index batch numbering method. By delineating the boundaries of intermediate injection points, the issue is bifurcated into two sub-problems: the optimization of batch planning for multiproduct pipeline with single-source and multi-terminal and the optimization of resource allocation at intermediate oil depots. The proposed algorithm is validated on two genuine multiproduct pipeline networks in China, demonstrating its capability to generate feasible solutions within a computational constraint of 18,000 CPU seconds. The findings reveal that this methodology holistically considers the entire pipeline network system, encompassing upstream oil intake, downstream oil supply, and the tank capacities of transfer depots, thereby establishing a coherent link between pipeline scheduling and depot planning. This paper may serve as a reference for the automated formulation of multiproduct pipeline network scheduling strategies. • A novel schedule method for multiproduct pipeline network is proposed. • Propose dynamic index batch numbering method for precise batch tracking. • The effectiveness of the proposed method is validated through solid examples. [ABSTRACT FROM AUTHOR]

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle. • Presents a solution framework to large-scale electric bus scheduling problem. • Introduces next day operability for continuous electric bus operations. • Bus range is a key determinant for diesel to electric bus replacement ratio. • 150-mi electric range leads to nearly 60% increase in fleet size. • Higher charging speed considerably reduces fleet size. [ABSTRACT FROM AUTHOR]

A kind of facilities for wafer fabrication, cluster tools (CTs) need to close down to an idle state from time to time because of periodical maintenance and switches from one type of lots to another, which is called a normal close-down process (NCDP). It is crucial to optimize such a transient process since it tends to occur more and more frequently due to customization. Also, process modules (PMs) in CTs are known to be failure-prone. Once a PM failure occurs, a tool needs to close down to an idle state as well, which is different from NCDP and is called a failure close-down process (FCDP). With wafer residency time constraints (WRTCs) being imposed, close-down process optimization for such a tool is challenging, since one needs to not only finish this process as soon as possible but also meet WRTCs during this transient process. In order to tackle this problem, this article first introduces steady state scheduling problems. Then, with a presented backward robot task sequence, a linear programming model is first proposed to optimize NCDP. To deal with the PM failures, efficient PM failure response policies are formulated for the cases in which a PM fails. Then, four linear programs are proposed to optimize an FCDP. Finally, industrial case studies are given to show the usefulness of the proposed approaches. [ABSTRACT FROM AUTHOR]

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes’ operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information. To improve information freshness over fading channels, efficient scheduling methods are highly desired for wireless transmissions. However, due to the channel instability and arrival randomness, optimizing AoI is very challenging. In this paper, we are interested in the AoI-optimal transmissions with rate-adaptive transmission schemes in a buffer-aware system. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, a low complexity algorithm is presented to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semi-threshold-based policy, which indicates that we can achieve well real-time service with a low computational complexity. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to reduce carbon emissions and costs in an automobile production plant by improving the operational management efficiency of a serial assembly line assisted by a feeding electric tow vehicle (ETV). Design/methodology/approach: A multi-objective function is formulated to minimize the energy consumption of the ETV from which emissions and costs are measured. First, a mixed-integer linear programming model is used to solve the feeding problem for different sizes of the assembly line. Second, a bi-objective optimization (HBOO) model is used to simultaneously minimize the most eco-efficient objectives: the number of completed runs (tours) by the ETV along the assembly line, and the number of visits (stops) made by the ETV to deliver kits of components to workstations. Findings: The most eco-efficient strategy is always the bi-objective optimal solution regardless of the size of the assembly line, whereas, for single objectives, the optimization strategy differs depending on the size of the assembly line. Research limitations/implications: Instances of the problem are randomly generated to reproduce real conditions of a particular automotive factory according to a previous case study. The optimization procedure allows managers to assess real scenarios improving the assembly line eco-efficiency. These results promote the implementation of automated control of feeding processes in green manufacturing. Originality/value: The HBOO-model assesses the assembly line performance with a view to reducing the environmental impact effectively and contributes to reducing the existent gap in the literature. The optimization results define key strategies for manufacturing industries eager to integrate battery-operated motors or to address inefficient traffic of automated transport to curb the carbon footprint. [ABSTRACT FROM AUTHOR]

In recent research, hyper-heuristics have attracted increasing attention in various fields. The most appealing feature of hyper-heuristics is that they aim to provide more generalized solutions to optimization problems by searching in a high-level space of heuristics instead of direct problem domains. Despite the promising findings in hyper-heuristics, the design of more general search methodologies still presents a key research. Evolutionary multitasking is a relatively new evolutionary paradigm which attempts to solve multiple optimization problems simultaneously. It exploits the underlying similarities among different optimization tasks by transferring information among them, thus accelerating the optimization of all tasks. Inherently, hyper-heuristics and evolutionary multitasking are similar in the following three ways: 1) they both operate on third-party search spaces; 2) high-level search methodologies are universal; and 3) they both conduct cross-domain optimization. To integrate their advantages effectively, i.e., the knowledge-transfer and cross-domain optimization of evolutionary multitasking and the search in the heuristic spaces of hyper-heuristics, in this article, a unified framework of evolutionary multitasking graph-based hyper-heuristic (EMHH) is proposed. To assess the generality and effectiveness of the EMHH, population-based graph-based hyper-heuristics integrated with evolutionary multitasking to solve exam timetabling and graph-coloring problems, separately and simultaneously, are studied. The experimental results demonstrate the effectiveness, efficiency, and increased the generality of the proposed unified framework compared with single-tasking hyper-heuristics. [ABSTRACT FROM AUTHOR]

The article presents the cost optimization model for multiunit construction projects. Multiunit projects constitute a special case of repetitive projects. They consist in the realization of many different, when it comes to size, types of residential, commercial, industrial buildings or engineering structures. Due to the specific character of construction works, actual schedules of such projects should not only take into account real costs of construction, but also be subject to specific restrictions, e.g. deadlines for the completion of units imposed by the investor. To solve the NP-hard problem of choosing the order of units’ construction there was metaheuristic algorithm of simulated annealing used. The objective function in the presented optimization model was the total value of the project cost determined on the basis of the mathematical programming model, taking into account direct and indirect costs, costs of missing deadlines and costs of work group discontinuities. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

The paper focuses on the day-ahead operational planning of a grid-connected local energy community (LEC) consisting of an internal low-voltage network and several prosumers including generation units, battery storage systems, and local loads. In order to preserve, as much as possible, the confidentiality of the features of prosumers’ equipment and the production and load forecasts, the problem is addressed by designing a specific distributed procedure based on the alternating direction method of multipliers (ADMM). The distributed procedure calculates the scheduling of the available energy resources to limit the balancing action of the external grid and allocates the internal network losses to the various power transactions. Results obtained for various case studies are compared with those obtained by a centralized optimization approach. The results confirm that, in the considered LEC framework, each of the prosumers achieves a reduction in costs or increases revenues in case it participates to the LEC with respect to the case in which it can only transact with an external energy provider. [ABSTRACT FROM AUTHOR]

A practical mathematical programming based approach is introduced for solving the examination timetabling problem at the German Jordanian University (GJU), whereby the complex process of acquiring a feasible examination timetable is simplified by subdividing it into three smaller sub‐problems (phases). Accordingly, the exams are initially allocated to time slots in phase one, the time slots are then allotted to days in phase two, and finally in phase three the exams are assigned to rooms based on the number of students taking each exam and capacities of the rooms. The solution for each phase is acquired based on an integer linear programming (ILP) formulation, while satisfying a set of hard constraints that ensure comfortable exam timetables for all students and meet the desired requirements set by GJU administrative staff. Furthermore, the solver can be controlled and launched from a student information system named MyGJU Admin, which enabled registrars at the university to easily, quickly, and accurately generate final exam timetables in several standard formats. Moreover, the approach was validated based on recent GJU registration information as well as real‐world benchmark data. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) electricity pricing has been applied in many countries around the world to encourage manufacturers to reduce their electricity consumption from peak periods to off-peak periods. This paper investigates a new model of Optimizing Electricity costs during Integrated Scheduling of Jobs and Stochastic Preventive Maintenance under time of-use (TOU) electricity pricing scheme in unrelated parallel machine, in which the electricity price varies throughout a day. The problem lies in assigning a group of jobs, the flexible intervals of preventive maintenance to a set of unrelated parallel machines and then scheduling of jobs and flexible preventive maintenance on each separate machine so as to minimize the total electricity cost. We build an improved continuous-time mixed-integer linear programming (MILP) model for the problem. To the best of our knowledge, no papers considering both production scheduling and Stochastic Preventive Maintenance under time of-use (TOU) electricity pricing scheme with minimization total Electricity costs in unrelated parallel machine. To evaluate the performance of this model, computational experiments are presented, and numerical results are given using the software CPLEX and MATLAB with then discussed. [ABSTRACT FROM AUTHOR]

The article presents the cost optimization model for multiunit construction projects. Multiunit projects constitute a special case of repetitive projects. They consist in the realization of many different, when it comes to size, types of residential, commercial, industrial buildings or engineering structures. Due to the specific character of construction works, actual schedules of such projects should not only take into account real costs of construction, but also be subject to specific restrictions, e.g. deadlines for the completion of units imposed by the investor. To solve the NP-hard problem of choosing the order of units' construction there was metaheuristic algorithm of simulated annealing used. The objective function in the presented optimization model was the total value of the project cost determined on the basis of the mathematical programming model, taking into account direct and indirect costs, costs of missing deadlines and costs of work group discontinuities. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying. [ABSTRACT FROM AUTHOR]

Featured Application: This article demonstrates a combination of Mixed-Integer Linear Programming with methods usually applied for short-term control, namely the Model Predictive Control scheme, to achieve decision support for the scheduling of installation activities for offshore wind farms. The general approach applies to several areas of application, where time-dependent uncertainties complicate mid- to long-term planning. The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency. [ABSTRACT FROM AUTHOR]

• We studied the multi-satellite observation and data-downlink scheduling problem. • We proposed a mixed-integer linear programming model for the problem. • Tasks of observation and data transmission are optimized concurrently. • We developed a procedure for generating the satellite available time windows. An Earth observation satellite (EOS) is a sort of low earth orbit (LEO) satellite that is equipped with high resolution cameras for observing various types of target objects scattered across the surface of the Earth. The available time windows of multiple EOSs for observing a given target object and for downloading the acquired image/video data from satellites to ground receiver stations are scarce resources that should to be utilized efficiently. This problem is hereby named as the multi-satellite observation and data-downlink scheduling problem (MSODSP). We developed a two-stage flow shop scheme for the MSODSP in order to optimize the observation scheduling (at stage 1) and data-downlink scheduling (at stage 2) concurrently and get truly optimized results. A mixed-integer linear programming (MILP) model is developed for the MSODSP with three objective functions. The effects of weather uncertainties on the tasks' success are considered in the MILP model, which allow us to conduct a reliability-maximized task arrangement of EOSs. Computational experiments were conducted on the simulated data of a real LEO satellite to verify the proposed MILP model. The results showed that it was able to solve real-world instances of the MSODSP for up to 20 tasks over 8 days. [ABSTRACT FROM AUTHOR]

Recent development methodologies from the industry and the academia for complex real-time systems define a stage in which system functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. In this paper, we present two approaches towards the efficient deployment of realistic and complex real-time systems by considering tree-shaped functional models. A formal approach to compute optimal deployment and a heuristic approach to scale to industry-size systems. The approaches consider placement, partitioning and scheduling, and are based on mixed integer linear programming (MILP) technique. Furthermore, we present a deep evaluation of the proposed deployment approaches to show the benefits and limits of a MILP-based deployment approach. A set of synthetic use-cases as well as a real-life automotive system are used to assess the quality and scalability of our deployment approaches. Considering use-cases, we show an added value with respect to end-to-end latencies optimization when solving the three stages of the deployment problem at the same time. This is done by comparing the quality of the solutions obtained with our techniques to those returned by the existing approaches. [ABSTRACT FROM AUTHOR]

This paper studies the problem of Radiation cOnstrained scheduling of wireless Charging tasKs (ROCK), that is, given wireless charging tasks with required charging energy and charging deadline for rechargeable devices, scheduling the power of wireless chargers to maximize the overall effective charging energy for all rechargeable devices, and further to minimize the total charging time, while guaranteeing electromagnetic radiation (EMR) safety, i.e., no point on the considered 2-D area has EMR intensity exceeding a given threshold. To address ROCK, we first present a centralized algorithm. We transform ROCK from nonlinear problem to linear problem by applying two approaches of area discretization and solution regularization, and then propose a linear programming-based greedy test algorithm to solve it. We also propose a distributed algorithm that is scalable with network size by presenting an area partition scheme and two approaches called area-scaling and EMR-scaling, and prove that it achieves effective charging energy no less than $(1-\varepsilon)$ of that of the optimal solution, and charging time no more than that of the optimal solution. We conduct both simulation and field experiments to validate our theoretical findings. The results show that our algorithm achieves 94.9% of the optimal effective charging energy and requires 47.1% smaller charging time compared with the optimal one when {\varepsilon } \geq 0.2 , and outperforms the other algorithms by at least 350.1% in terms of charging time with even more effective charging energy. [ABSTRACT FROM PUBLISHER]

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters. [ABSTRACT FROM PUBLISHER]

Health organizations are complex to manage due to their dynamic processes and distributed hospital organization. It is therefore necessary for healthcare institutions to focus on this issue to deal with patients’ requirements. Preparing a schedule for patients in the emergency department is a complex task, which requires taking into account numerous rules, related to various aspects: respect the triage process (emergency degrees of patients), respect the availability of resources, etc. In this paper, we present a mixed integer linear programming (MILP) approach to facilitate this task. The objective is to minimize the total waiting time of patient’s in the emergency department. We consider simultaneously four patients’ process: registration and triage, consultation, treatment and hospitalization. The model is characterized by the availability of both human (triage staff, physician, nurse) and material resources (bed) in each process through the stay of patient in the ED except for triage and registration which does not require a bed. To solve this model, we used the commercial solver IBM ILOG CPLEX Optimization Studio. The program has been tested on a set of instances. Numerical results show that the proposed approach can significantly improve the efficiency of emergency department by reducing the total waiting time of patients. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the symbiosis between a truck and multiple drones in a last-mile package delivery scenario, introducing the Scheduling Conflictual Deliveries Problem (SCDP). From the main depot, a truck takes care of transporting a fleet of drones that will be used to deliver packages to customers. The route of the truck is predefined. Each delivery is associated with the energy cost of a drone, a reward that characterizes the priority of the delivery, and an interval between two points of the truck's route: the point from which the drone departs (launch point) and the point at which the drone returns to the truck (rendezvous point). The objective of the SCDP is to find a scheduling for the drones that maximizes the overall reward subject to the drone's battery capacity while ensuring that the same drone performs deliveries whose delivery intervals do not intersect. After showing that SCDP is an NP-hard problem, we devise an Integer Linear Programming (ILP) formulation for it. Furthermore, we devise a pseudo-polynomial time optimal algorithm for the single drone case and additional approximation algorithms for both the single and multiple drones case. Finally, we thoroughly compare the performances of our presented algorithms on different synthetic datasets. [ABSTRACT FROM AUTHOR]

Multi-recipe and multi-stage material handling (M3H) processes are widely employed by various industries where complex multi-product manufacturing and assembly tasks are required. Cyclic hoist scheduling (CHS) could significantly improve the productivity of an M3H process. In this paper, a novel CHS methodology has been developed, which considers employing multiple sub-cycles to efficiently deal with job duality issues associated with multi-capacity processing units. The methodology contains three modeling and solving stages. In Stage I, a mixed-integer linear programing (MILP) model is developed to obtain the optimal solution of a sub-cycle CHS problem with the tolerance of job duality. In Stage II, the obtained solution will be examined to see if the job duality exists. Once a job duality issue is identified, another MILP model in Stage III will be performed to schedule an additional sub-cycle CHS problem, which targets the minimum slot usage discrepancy caused by the identified job duality. After that, the combined CHS solutions from the previous and additional sub-cycles will be fed back to Stage II for the job duality examination again. Iteratively checking and rescheduling between Stages II and III, job duality issues will be eventually eliminated and a full scheduling cycle coupling multiple sub-cycles will be accomplished. The methodology can significantly increase the CHS optimality for M3H processes, which are demonstrated by two case studies. [ABSTRACT FROM AUTHOR]

Chemotherapy infusion treatments for cancer have significant and unpredictable variability in duration. This variability can have negative impact on operations – both patient wait time and staff overtime – if not managed well. From an appointment scheduling optimization perspective, this problem has a unique structure because a single server (a nurse) attends to multiple customers (patients) at one time. Based on our observations at the University of Michigan Comprehensive Cancer Center (UMCCC) and collaborations with clinicians there, we present a two-stage stochastic integer program for designing patient appointment schedules under uncertainty in treatment times. The objective is to minimize a trade-off between expected patient wait times and expected total time required to treat patients. We show that solving this optimization problem exactly requires a prohibitive computational time, so we develop a heuristic algorithm to find approximate solutions. We also present an approach to compute lower bounds on the optimal objective value that we use to analyze the performance of our algorithm. Computational experiments based on real-world data are presented and used to draw managerial insights. [ABSTRACT FROM PUBLISHER]

Resource management for large-scale high performance computing systems pose difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable of handling at least millions of tasks and thousands of machines. These large computing systems consume vast amounts of electricity leading to high operating costs. System administrators try to simultaneously reduce operating costs and offer state-of-the-art performance; however, these are often conflicting objectives. Highly scalable algorithms are necessary to schedule tasks efficiently and to help system administrators gain insight into energy/performance trade-offs of the system. System administrators can examine this trade-off space to quantify how much a difference in the performance level will cost in electricity, or analyze how much performance can be expected within an energy budget. In this study, we design a novel linear programming based resource allocation algorithm for a heterogeneous computing system to efficiently compute high quality solutions for simultaneously minimizing energy and makespan. These solutions are used to bound the Pareto front to easily trade-off energy and performance. The new algorithms are highly scalable in both solution quality and computation time compared to existing algorithms, especially as the problem size increases. [ABSTRACT FROM PUBLISHER]

We study the resource allocation and management issues related to heterogeneous wireless systems made up of several radio access technologies (RATs) that collectively provide a unified wireless network to a diverse set of users through co-ordination managed by a centralized global resource controller (GRC). We assume that the user devices are multimodal, which makes it possible for each device to use any available access point (AP)/base station (BS) of a RAT at any given time. Through detailed protocol level simulations performed in ns-2, we show an increase in spectral efficiency of up to 99 percent and an increase in short-term fairness of up to 28.5 percent for two greedy sort-based user device-to-AP/BS association algorithms implemented at the GRC compared to a distributed solution used in practice today where each user makes his/her own association decision. While the increase in overhead due to re-associations for a centralized solution grows only slightly (by up to 4.1 percent) compared to a distributed solution, we find the performance increase in spectral efficiency and short-term fairness attributes come at the cost of an order of magnitude increase (of up to 794 percent) in energy consumption. [ABSTRACT FROM PUBLISHER]

In this letter, we introduce an application-aware approach for resource block scheduling with carrier aggregation in long-term-evolution advanced (LTE-advanced) cellular networks. In our approach, users are partitioned in different groups based on the carriers coverage area. In each group of users, users equipments (UE)s are assigned resource blocks (RB)s from all in band carriers. We use a utility proportional fairness (PF) approach in the utility percentage of the application running on the UE. Each user is guaranteed a minimum quality of service (QoS) with a priority criterion that is based on the type of application running on the UE. We prove that our scheduling policy exists, and therefore, the optimal solution is tractable. Simulation results are provided to compare the performance of the proposed RB scheduling approach with other scheduling policies. [ABSTRACT FROM PUBLISHER]

Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]