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Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

Cloud computing is a popular paradigm that enables on-demand access to shared resources over the internet. Task scheduling is an important aspect of cloud computing that involves allocating resources to tasks in an efficient manner. The rapid growth of cloud computing has led to an increasing demand for efficient task scheduling algorithms. In cloud computing, task scheduling is critical for achieving high performance and resource utilization, while minimizing costs. However, traditional task scheduling algorithms often struggle to handle the intricacy and fluctuation in cloud computing environments. Therefore, a novel task scheduling framework called Matched Bald Eagle (MABLE) task scheduling framework for Cloud Computing to schedule tasks on Virtual Machines (VMs) in a cloud environment. The proposed framework consists of three major phases: matching, sorting and scheduling. The matching phase identifies the most suitable VMs for each task, while the sorting phase prioritizes the tasks based on their requirements and the types of VMs available. Finally, the scheduling phase uses the Enhanced Bald Eagle optimization (EBEO) algorithm in scheduling tasks on the chosen VMs. The simulated MABLE technique is proposed and its performance is compared with existing methods in terms of load balance, cost, resource utilization and makespan under two different scenarios. The outcomes demonstrate that the MABLE method outperforms existing techniques and is an efficient task scheduling framework for cloud computing. [ABSTRACT FROM AUTHOR]

Wireless sensor networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these networks is the limited lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the network's lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased network lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 11% higher than the best other algorithms. [ABSTRACT FROM AUTHOR]

Data-intensive applications must be run on systems with high-performance processing and enough storage. When compared to conventional distributed systems like the data grid, cloud computing offers these features on a platform that is more flexible, scalable, and inexpensive. Moreover, retrieving data files is crucial to operate these services. Typically, accessing data causes the entire cloud workflow system to experience a bottleneck, thus significantly reducing system performance. Two key strategies that can enhance the efficiency of data-intensive applications are task scheduling and data replication. This research proposes a novel Data replication and scheduling in the cloud. Initially, the workflow management process is performed with 3 phases (1) workflow placement, (2) clustering of tasks, and (3) scheduling and replication. Initially, the workflow placement takes place. Then, the clustering of tasks is performed via an improved K-means algorithm. Finally, the tasks and datasets are replicated during the scheduling and replication phase. Further, the scheduling and replication are performed using the Self Modified Pelican Optimization Algorithm (SM-POA) based on the execution cost, migration cost, storage cost and replication. [ABSTRACT FROM AUTHOR]

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. [ABSTRACT FROM AUTHOR]

Background: Spot‐scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency. Purpose: To address the challenges in generating a deliverable and efficient SPArc plan for a proton therapy system with a massive gantry, we developed a novel SPArc optimization algorithm (SPArcDMPO) by directly incorporating the machine‐specific parameters such as gantry mechanical constraints and proton delivery sequence. Methods: SPArc delivery sequence model (DSMarc) was built based on the machine‐specific parameters of the prototype arc delivery system, IBA ProteusONE®, including mechanical constraint (maximum gantry speed, acceleration, and deceleration) and proton delivery sequence (energy and spot delivery sequence, and irradiation time). SPArcDMPO resamples and adjusts each control point's delivery speed based on the DSMarc calculation through the iterative approach. In SPArcDMPO, users could set a reasonable arc delivery time during the plan optimization, which aims to minimize the gantry momentum changes and improve the delivery efficiency. Ten cases were selected to test SPArcDMPO. Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArcoriginal); (2) SPArcDMPO plan with a user‐pre‐defined delivery time. Additionally, arc delivery sequence was simulated based on the DSMarc and was compared. Treatment delivery time was compared between SPArcoriginal and SPArcDMPO. Dynamic arc delivery time, the static irradiation time, and its corresponding time differential (time differential = dynamic arc delivery time—static irradiation time) were analyzed, respectively. The total gantry velocity change was accumulated throughout the treatment delivery. Results: With a similar plan quality, objective value, number of energy layers, and spots, both SPArcoriginal and SPArcDMPO plans could be delivered continuously within the ± 1 degree tolerance window. However, compared to the SPArcoriginal, the strategy of SPArcDMPO is able to reduce the time differential from 30.55 ± 11.42%(90 ± 32 s) to 14.67 ± 6.97%(42 ± 20 s), p < 0.01. Furthermore, the corresponding total variations of gantry velocity during dynamic arc delivery are mitigated (SPArcoriginal vs. SPArcDMPO) from 14.73 ± 9.14 degree/s to 4.28 ± 2.42 degree/s, p < 0.01. Consequently, the SPArcDMPO plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArcoriginal plans, which could help relieve the mechanical challenge of accelerating or decelerating the massive proton gantry. Conclusions: For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly control the arc treatment speed and momentum changes of the gantry during the plan optimization process. This work paved the way for the routine clinical implementation of proton arc therapy in the treatment planning system. [ABSTRACT FROM AUTHOR]

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics. [ABSTRACT FROM AUTHOR]

A number of results have been reported in the literature for a single machine job scheduling problem with resource constraints. We demonstrate that many of these results and some new results follow from an equivalence of this problem and the classical two-machine flow-shop scheduling problem. We further refine computational complexity of the problem with resource constraints by presenting new NP-hardness proofs. [ABSTRACT FROM AUTHOR]

A K-M-N on-orbit service network architecture and its mission scheduling problem are proposed and studied, in which K space service stations and M service spacecraft are scheduled to service for N target spacecraft. The service spacecraft, which are initially docked with the space service station in the geosynchronous region, set off through orbital maneuver to conduct servicing operation for one or multiple target spacecraft upon receiving the service command. A mission scheduling strategy of on-orbit service for the target GEO spacecraft is proposed based on space service stations, in which the service spacecraft are scheduled to serve all target spacecraft with the lowest cost of fuel for the orbital transfers. As the mission scheduling for such a K-M-N GEO on-orbit service network is NP-hard, a hybrid simulated annealing optimization algorithm is put forward based on a mechanism of dynamic reverse processing of encoding and decoding to solve this problem. Four numerical case studies are presented and their results are compared with those from the literature to demonstrate the effectiveness of the approach. A series of different network model simulations and Mann-Kendall tests prove that multispace service stations can significantly reduce total fuel consumption for transfers, and the initial layout is also very important. Besides, the initial distribution of the service spacecraft also has an impact on total fuel. However, the quantitative impact of the service spacecraft is uncertain and needed to be analyzed on a specific basis, sometimes increasing the quantity of service spacecraft can reduce the total fuel, sometimes it has no effect, sometimes it may even cause negative effect when the number of service spacecraft is too large and unreasonable. Practical Applications: On-orbit servicing (OOS), refers to the execution of space operations through the exterior vehicle activities of astronauts, robots or both to extend the mission life of a spacecraft or improve the ability of a spacecraft to perform on-orbit missions. With the development of relevant enabling technologies, OOS will become a business which can be implemented commercially and in a large scale, in which one or multiple space service stations, as the space resource storage warehouse of the satellites in orbit, run in the earth orbit for a long time, and the service spacecraft will provide service to the target spacecraft in turn after completing the rendezvous and docking. In the process of servicing, the service spacecraft will return to the space service station for the replenishment when its resource is insufficient. In this paper, on-orbit service network scheduling based on space service station is studied, which is NP-hard. A hybrid simulated annealing optimization algorithm is put forward based on a mechanism of dynamic reverse processing of encoding and decoding to solve this problem. The researches in this paper are of great significance to the scale design and scheduling of on-orbit services in the future, meanwhile have a certain appeal for the relevant operational research scholars. [ABSTRACT FROM AUTHOR]

Preventive Maintenance (PM) is a periodic maintenance strategy that has great results for devices in extending their lives, increasing productivity, and, most importantly, helping to avoid unexpected breakdowns and their costly consequences. Preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. This study employs hybrid heuristic methods, integrating Genetic Algorithm (GA) and Tabu Search (TS), to address the PMS problem. Notably, the search for an optimal solution remained elusive with GA alone until the inclusion of TS. The resultant optimal solution is achieved swiftly, surpassing the time benchmarks set by conventional methods like integer programming and nonlinear integer programming. A comparison with a published article that used metaheuristics was also applied in order to evaluate the effectiveness of the proposed hybrid approach in terms of solution quality and convergence speed. Moreover, sensitivity analysis underscores the robustness and efficacy of the hybrid approach, consistently yielding optimal solutions across diverse scenarios. The schedule created exceeds standards set by waterworks experts, yielding significant water and electricity surpluses—16.6% and 12.1%, respectively—while simultaneously matching or surpassing total production levels. This method can be used for power plants in private or public sectors to generate an optimal PMS, save money, and avoid water or electricity cuts. In summary, this hybrid approach offers an efficient and effective solution for optimizing PMS, presenting opportunities for enhancement across various industries. [ABSTRACT FROM AUTHOR]

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]

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating securityaware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains. [ABSTRACT FROM AUTHOR]

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental & Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This paper presents a framework for the efficient management of renewable energies to charge a fleet of electric buses (eBuses). Our framework starts with the prediction of clean energy time windows, i.e., periods of time when the production of clean energy exceeds the demand of the country. Then, the optimization phase schedules charging events to reduce the use of non-clean energy to recharge eBuses while passengers are embarking or disembarking. The proposed framework is capable of overcoming the unstable and chaotic nature of wind power generation to operate the fleet without perturbing the quality of service. Our extensive empirical validation with real instances from Ireland suggests that our solutions can significantly reduce non-clean energy consumed on large data sets. [ABSTRACT FROM AUTHOR]

In this study, we consider a system consisting of n different components with failure interaction. Each component suffers from either minor or major failure: the former is removed via minimal repair, while the latter induces complete failure of the system and requires corrective replacement of the system. This study investigates two scheduling problems of preventive replacement policies for a multi-component system that incorporates minimal repair, shortage, and excess costs. First, we consider the scheduling problem of an age replacement policy, in which the system is replaced at a planned time T or upon the occurrence of any major failure. Next, we consider that the system operates N successive random jobs without interruptions. In this scheduling problem, the system is replaced upon the accomplishment of N jobs or upon the occurrence of any major failure. For each scheduling problem, we derive the optimal scheduling parameter ( T * or N * ) analytically and numerically, according to their existence and uniqueness based on minimizing the mean cost rate function. Finally, a numerical example is designed to validate the theoretical results in this article. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have significant potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system's average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach. Framework for home charging schedule of distinct EVs Compliance with multi-objective power supply efficiency Probabilistic modeling of load demand from real collected data Optimization-driven solution for a test case in Mysore city in India [ABSTRACT FROM AUTHOR]

Mission scheduling is an effective method to increase the value of satellite missions and can greatly improve satellite resource management and quality of service. Based on the priority-based task scheduling model, this paper proposes a CubeSat scheduling method that takes operational reliability into account, considering the impact of scheduling results on reliable operation. In this method, the available energy and the time window are used as scheduling resources, and the average state of charge of the lithium battery and the number of task start-ups are defined as two indices to measure its reliability. To meet the mission requirements and energy availability of photovoltaic (PV) solar panel and battery constraints, the scheduling model is constructed with an objective function that includes mission priority and reliability index. The branch and bound (BB) method and analytical hierarchy process (AHP) method are used to solve the scheduling problem. The example analysis compares different scheduling results and verifies the effectiveness of the proposed scheduling method. Compared with the existing methods, it comprehensively considers the mission value and operational reliability of the CubeSat, improves the energy reserve level of the CubeSat, and reduces the surge current caused by the start-up of tasks. [ABSTRACT FROM AUTHOR]

As the environmental pollution is an important issue and its investigation in various scientific fields seems quite necessary, this study has tried to minimize the pollution emission due to ships' delays during the loading/unloading process in ports. Ships' arrival times vary; hence, they have two port-service-provision periods, one of which is normal and exact and the other is urgent and uncertain. If the ship service is not complete in the first period, its delays and the resulting pollution will increase and affect the minimization objective function with a greater degree of importance. As an innovation, varying service-provision prioritization has been considered through a mathematical function that prioritizes the more severe-condition ships in terms of pollution and schedules them earlier based on the remaining service time. To this end, first the worst-case limit was found for the second uncertain period through simulation, then the problem was modeled mathematically using the simulation output, priority function, problem assumptions, and the ROBUST method and the model was solved by GAMS. Analyses of the results revealed that in a planning horizon, the ROBUST method could provide the optimal scheduling and due date for 40 ships in an average time of 320 s. [ABSTRACT FROM AUTHOR]

This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC), which is equivalent to minimizing the cumulative dosage. We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the ideal concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the 'ideal' concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the 'ideal' concentration profile, using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the 'ideal' concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans. [ABSTRACT FROM AUTHOR]

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Green high-performance concrete is widely adopted in the construction industry to reduce building energy consumption and enhance quality. Prompt delivery of green high-performance concrete is vital for successful project completion because the processes relating to green high-performance concrete are the critical path in most cases, namely that the project delay will occur if the works involving green high-performance concrete are delayed caused by late delivery of green high-performance concrete. This research proposes an online scheduling model, which takes into account the idle time of green high-performance concrete in manufacturing, that is the time from release until start, in order to improve prompt delivery of green high-performance concrete. Formally, there are irrelevant jobs arriving online over time and the fundamental knowledge of each job J j is not declared until it is released at time r j . Our objective is to minimize the total completion time plus the total soft penalty cost of all jobs, where "soft penalty" cost of job J j is α (S j - r j) with soft penalty coefficient α > 0 , S j and r j are starting processing time and release date of job J j , respectively. For this problem, by applying the technique "Peeling Onion", it is explicitly derived that the classic online algorithm DSPT is best possible with competitive ratio 2 + α . [ABSTRACT FROM AUTHOR]

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multiobjective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA - II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms' potential can be further investigated for hybrid flow shop scheduling problems. [ABSTRACT FROM AUTHOR]

Control of Water Distribution Networks (WDNs) is a well-researched domain due to its societal relevance. WDNs can be operated in the continuous or discrete mode using appropriate continuous and discrete (ON/OFF) valves, respectively. However, the variation in performance of the network due to the different modes of operation has not been quantified. In this work, we quantify the performance of the network in terms of the time required for completing the operation and study the variations in the system performance. In the first part of the work, we present the analysis based on simulations of a real-world network. A scheduling problem is formulated for a class of WDNs, and the problem is solved under different scenarios to identify the variation in operational time for the network. The effort required for changing the schedule to meet variations in demand is also discussed. In the next part, we present the theoretical analysis for a specific class of networks. An upper bound on the variation in operational time is derived for this class of networks. [ABSTRACT FROM AUTHOR]

The gate-based model is one of the leading quantum computing paradigms for representing quantum circuits. Within this paradigm, a quantum algorithm is expressed in terms of a set of quantum gates that are executed on the quantum hardware over time, subject to a number of constraints whose satisfaction must be guaranteed before running the circuit, to allow for feasible execution. The need to guarantee the previous feasibility condition gives rise to the Quantum Circuit Compilation Problem (QCCP). The QCCP has been demonstrated to be NP-Complete, and can be considered as a Planning and Scheduling problem. In this paper, we consider quantum compilation instances deriving from the general Quantum Approximation Optimization Algorithm (QAOA), applied to the MaxCut problem, devised to be executed on Noisy Intermediate Scale Quantum (NISQ) hardware architectures. More specifically, in addition to the basic QCCP version, we also tackle other variants of the same problem such as the QCCP-X (QCCP with crosstalk constraints), the QCCP-V (QCCP with variable qubit state initialization), as well as the QCCP-VX that includes both previous variants. All problem variants are solved using genetic algorithms. We perform an experimental study across a conventional set of instances taken from the literature, and show that the proposed genetic algorithm, termed G A VX , outperforms previous approaches in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheduling problem for patient visits with two objectives: minimizing patient waiting time and travel time. It also presents a novel encoding method for Genetic Algorithms (GA) that is well-suited for this problem. Experiments demonstrate that the proposed encoding method reduces optimization iterations by 17% compared to conventional methods, and the GA can decrease waiting time by up to 58.2% and travel time by up to 89.3% for specific examples. The novel scheduling problem and the encoding method are two main contributions of this work. [ABSTRACT FROM AUTHOR]

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements. [ABSTRACT FROM AUTHOR]

The modernization of forest harvesting operations has significantly increased the cost of machine ownership and has turned forest harvesting into a capital-intensive process. To increase productivity and profitability, some companies have acquired multi-task harvesting machines. While many previous papers focused on optimizing the harvest scheduling to reduce the costs of harvesting, the assignment of multi-task machines was not considered in their models. In this work, an optimization model is developed for the detailed scheduling of harvesting activities on multiple cut blocks using multi-task machines. This model is a continuation of previous work on detailed harvest scheduling. It prescribes the start time and the end time of operations of each machine at each cut block, the number of machines to be assigned for each harvesting activity at each cut block, the cut block that the machine should move to after completing its operation at a cut block, and the type of activity it should perform. It is applied to a case study of a forest company in Canada. According to the results, the total harvesting cost decreased by Can$ 25,000 when multi-task machines were used compared to exclusive machines, due to less machine movement and the need for fewer machines. [ABSTRACT FROM AUTHOR]

To ensure safe, effective, and efficient marine operations, the optimization of maritime communication workflows with a task-oriented scheduling framework is of the utmost importance. Navigation, vessel traffic management, emergency response, and cargo operations are all made possible by maritime communication, which necessitates seamless information sharing between ships, ports, coast guards, and regulatory bodies. However, traditional communication methods face challenges in adapting to the dynamic and distributed nature of maritime activities. This study suggests a novel approach for overcoming these difficulties that combines task-oriented scheduling and resource-aware cloud environments to enhance marine communication operations. Utilizing cloud computing offers a scalable, adaptable infrastructure that can manage various computational and communication needs. Even during busy times, effective data processing, improved decision making, and improved communication are made possible by utilizing the cloud. The intelligent allocation and prioritization of communication activities using a task-oriented scheduling framework ensures that urgent messages receive prompt attention while maximizing resource utilization. The proposed approach attempts to improve marine communication workflows' task prioritization, scalability, and resource optimization. In order to show the effectiveness of the proposed approach, simulations were performed in CloudSim. The performance evaluation parameters, i.e., throughput, latency, execution cost, and energy consumption, have been evaluated. Simulation results reflect the efficacy and practical usability of the framework in various maritime communication configurations. By making marine communication methods more durable, dependable, and adaptable to the changing needs of the maritime industry, this study advances maritime communication techniques. The findings of this research have the potential to revolutionize maritime communication, leading to safer, more efficient, and more resilient maritime operations on a large scale. [ABSTRACT FROM AUTHOR]

In project scheduling, the durations of activities are often uncertain. Delays may cause a massive disorganization if a large number of activities must be rescheduled. In "The Anchor-Robust Project Scheduling Problem," Bendotti, Chrétienne, Fouilhoux, and Pass-Lanneau propose a novel criterion for solution stability in project scheduling under processing times uncertainty. They define anchored jobs as jobs whose starting times can be guaranteed in a baseline schedule. Finding a project schedule with bounded makespan and a max-weight set of anchors is shown to be an NP-hard robust two-stage problem. Taking advantage of the combinatorial structure of project scheduling and budgeted uncertainty, the authors obtain a compact MIP formulation for the problem. Numerical results show that the obtained MIP outperforms standard techniques from the literature. They also showcase the practical interest of anchored jobs in project scheduling. In project scheduling with uncertain processing times, the decision maker often needs to compute a baseline schedule in advance while guaranteeing that some jobs will not be rescheduled later. Standard robust approaches either produce a schedule with a very large makespan or offer no guarantee on starting times of the jobs. The concept of anchor-robustness is introduced as a middle ground between these approaches. A subset of jobs is said to be anchored if the starting times of its jobs in the baseline schedule can be guaranteed. The Anchor-Robust Project Scheduling Problem (AnchRobPSP) is proposed as a robust two-stage problem to find a baseline schedule of bounded makespan and a max-weight subset of anchored jobs. AnchRobPSP is considered for several uncertainty sets, such as box or budgeted uncertainty sets. Dedicated graph models are presented. In particular, the existence of a compact mixed integer programming reformulation for budgeted uncertainty is proven. AnchRobPSP is shown to be NP-hard even for budgeted uncertainty. Polynomial and pseudopolynomial algorithms are devised for box uncertainty and special cases of budgeted uncertainty. According to numerical results, the proposed approaches solve large-scale instances and outperform classical affine decisions rules for AnchRobPSP. Insights on the price of anchor-robustness are given based on computations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers' tasks concerns allocating clients' tasks to providers' resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey's maximum surviving value and the predator's minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey's maximum surviving value and the predator's minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) is a promising technique for scheduling and planning storage systems in microgrids, which are small-scale power networks that can operate independently or in coordination with the main grid. RL can enhance the utilization of local renewable energy sources and reduce the operational costs of microgrids. In this comprehensive study and review the state-of-the-art applications of RL for Microgrid Energy Management (MEM), focusing on battery storage systems is discussed. This work also identify the main challenges, limitations, and future directions in this domain. Furthermore, this article present a novel benchmark algorithm that compares the performance of RL with mixed-integer linear programming (MILP), a widely used optimization technique. This complete work provides a valuable insight into the current status and future prospects of RL for MEM. [ABSTRACT FROM AUTHOR]

Thousands of aging education buildings in the US are in urgent need of maintenance to ensure their operational performance. The planning and scheduling of these maintenance activities need to be optimized to minimize their total maintenance costs and reduce the challenging deferred maintenance problems confronting education institutions in the US. This paper presents the development of a novel model for optimizing the planning of maintenance activities in education buildings. The computations of the developed model are implemented utilizing an optimization module that identifies an optimal maintenance schedule for minimizing total maintenance costs; a scheduling module that calculates the start day, start hour, finish day, and finish hour of all maintenance activities; and a cost estimating module that estimates the total maintenance cost for each generated schedule. An application example of three education buildings that include plumbing, HVAC, and electrical maintenance activities was analyzed to illustrate the use of the model and evaluate its performance by comparing its results to those generated by widely used existing methods for planning maintenance activities of education buildings. The outcome of this analysis confirms the model was capable of outperforming existing methods in reducing the total maintenance cost by more than 12%. The main contributions of the developed novel model to the body of knowledge include its innovative methodology for identifying optimal ranking of maintenance activities, selecting optimal overtime use and crew size for all maintenance activities, generating an optimal maintenance schedule that complies with all practical constraints such as the availability of classrooms during their nonoperational hours, and minimizing total maintenance cost of all scheduled activities. [ABSTRACT FROM AUTHOR]

This paper addresses machines and automated guided vehicles (AGVs) simultaneous scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) to produce the best optimal sequences for the minimization of makespan (MKSN). This problem is highly complex to solve because it involves the selection of machines for job operations (jb-ons), the sequencing of jb-ons on the machines, the assignment of AGVs, and associated trips such as AGVs' deadheaded trip and loaded trip times to jb-ons. This paper offers a nonlinear mixed integer programming (MIP) formulation for modeling the problem and the symbiotic organisms search algorithm (SOSA) for solving the problem. For verification, a manufacturing company's industrial problem is employed. The results show that SOSA outperforms the existing methods and the Jaya algorithm, and using alternate machines for the operations can reduce the MKSN. [ABSTRACT FROM AUTHOR]

The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article. [ABSTRACT FROM AUTHOR]

Purpose: This study presented the experience of improving the nucleic acid sample collection and transportation service in response to the epidemic. The main purpose is that through intelligent path planning, combined with the time scheduling of sample points, the process of obtaining results to determine the state of COVID-19 patients could be speeding up. Design/methodology/approach: The research optimized the process, including finding an optimal path to traverse all sample points in the hospital area via intelligent path planning method and standardizing the operation through the time sequence scheduling of each round of support staff to collect and send samples in the hospital area, so as to ensure the shortest time in each round. And the study examines these real-time experiments through retrospective examination. Findings: The real-time experiments' data showed that the proposed path planning and scheduling model could provide a reliable reference for improving the efficiency of hospital logistics. Testing is a very important part of diagnosis and prompt results are essential. It shows the possibility of applying the shortest-path algorithms to optimize sample collection processes in the hospital and presents the case study that gives the expected outcomes of such a process. Originality/value: The value of the study lies in the abstraction of a very practical and urgent problem into a TSP. Combining the ant colony algorithm with the genetic algorithm (ACAGA), the performance of path planning is improved. Under the intervention and guidance, the efficiency of hospital regional logistics planning was greatly improved, which may be of greater benefit to critical patients who must go through fever clinic during the epidemic. By detailing how to more rapidly obtain results through engineering method, the paper contributes ideas and plans for practitioners to use. The experience and lessons learned from Tongji Hospital are expected to provide guidance for supporting service measures in national public health infrastructure management and valuable reference for the development of hospitals in other countries or regions. [ABSTRACT FROM AUTHOR]

Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like pre-processing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively. [ABSTRACT FROM AUTHOR]

Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians' operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center's attributes and patients' input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements. [ABSTRACT FROM AUTHOR]

This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions. [ABSTRACT FROM AUTHOR]

The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario. [ABSTRACT FROM AUTHOR]

This article presents an optimization methodology to schedule the replacement of power transformers (PT) into a fleet. The objective is the minimization of the summation of the risk indices of the PT. Each PT risk index is calculated from the estimation of the life used and the strategic importance of the unit. The PT life used is estimated as a relationship of the solid insulation polymerization degree, where aging processes due to oxidation, hydrolysis, and pyrolysis are considered from the calendar date when the unit starts its operation until different future scenarios. For the calculation of the PT strategic importance, financial, security, environmental, and network performance aspects are considered. Then, using the optimization model, together with the CAPEX and the available budget over a planning time, a strategy for optimally replacing the critical units is determined. The model was applied for a group of 102 units, demonstrating its applicability and effectiveness. The developed methodology serves to support the manager of these assets in making decisions in the long term. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is currently one of the prime choices in the computing infrastructure landscape. In addition to advantages such as the pay‐per‐use bill model and resource elasticity, there are technical benefits regarding heterogeneity and large‐scale configuration. Alongside the classical need for performance, for example, time, space, and energy, there is an interest in the financial cost that might come from budget constraints. Based on scalability considerations and the pricing model of traditional public clouds, a reasonable optimization strategy output could be the most suitable configuration of virtual machines to run a specific workload. From the perspective of runtime and monetary cost optimizations, we provide the adaptation of a Hadoop applications execution cost model extracted from the literature aiming at Spark applications modeled with the MapReduce paradigm. We evaluate our optimizer model executing an improved version of the Diff Sequences Spark application to perform SARS‐CoV‐2 coronavirus pairwise sequence comparisons using the AWS EC2's virtual machine instances. The experimental results with our model outperformed 80% of the random resource selection scenarios. By only employing spot worker nodes exposed to revocation scenarios rather than on‐demand workers, we obtained an average monetary cost reduction of 35.66% with a slight runtime increase of 3.36%. [ABSTRACT FROM AUTHOR]

Purpose: The airlines cancel their flights frequently because of factors that they do not have any control over. Spare aircraft can potentially address some of the issues caused by cancelled flights. This paper aims to offer an exploratory study into the financial and operational viabilities of spare aircraft for airlines. Design/methodology/approach: Mathematical models are proposed to evaluate the financial and operational metrics under different scenarios. The models are applied to Delta, Spirit and Southwest Airlines with different business models. All data are extracted from US Bureau of Transport Statistics, Cirium Diio Mi and CAPA databases. The IBM Cplex solver was used to execute the binary linear program models. Findings: The research revealed that factors such as airline network size, hub and spoke structure and average weekly flight cancellations are crucial in establishing the need for spare aircraft. For the number of weekly cancellations, there exist break-even values that reasonably justify spare aircraft. Practical implications: Models can be customized and applied to other modes of transportations. Originality/value: This study is the first to consider the use of spare aircraft in airlines from both financial and operational perspectives within the scope of the mathematical model. The analyses identify financial break-even points for a number of spare aircraft and their home base locations for three airlines. Operational utilization of spare aircraft is studied and contrasted with financial metrics. [ABSTRACT FROM AUTHOR]

This work presents an innovative constrained programming model for solving a flexible job-shop scheduling problem with assemblies and limited buffer capacity based on a real case from the shipbuilding industry. Unlike the existing literature, this problem incorporates the manufacturing and assembly of blocks from subblocks to the final ship erection, while considering the limited buffer capacity due to the size of blocks, which has been often overlooked. The objectives considered are the minimization of the makespan and tardiness based on ship erection due dates. To demonstrate the model's effectiveness, it is initially validated using various scheduling problems from the literature. Then, the model is applied to progressively challenging instances of the shipbuilding problem presented in this work. Finally, the optimization results are validated and analyzed using a comprehensive simulation model. Overall, this work contributes to reducing the gap between academia and industry by providing evidence of the convenience of the application of constrained programming models combined with simulation models on industrial-size scheduling problems within reasonable computational time. Moreover, the paper emphasizes originality by addressing unexplored aspects of shipbuilding scheduling problems and highlights potential future research, providing a robust foundation for further advancements in the field. [ABSTRACT FROM AUTHOR]

Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE and hybridizing it with the neighborhood search method. The proposed method improves the performance of differential evolution by applying appropriate solution representation as well as effective selection, crossover, and mutation operators. Likewise, the neighborhood search algorithm is applied to improve the extracted Pareto-front and speed up the evolution process. The effectiveness and performance of the developed method are assessed over well-known test problems collected from the related literature. Meanwhile, the values of spacing and hyper-volume metrics are calculated. Moreover, the Wilcoxon signed method is applied to carry out pairwise statistical tests over the obtained results. The obtained results for the makespan, reliability, and flow-time of 50, 18, and 41, respectively, by the proposed hybrid algorithm in the study confirmed that the developed algorithm outperforms all proposed methods considering the performance and quality of objective values. [ABSTRACT FROM AUTHOR]