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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]

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]

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]

Scheduling nurse duty is one of the problems in health organizations that is quite complicated to solve. Starting from the uncertain number of patients, serious patient illnesses, characteristics of organizational groups, requests for nurses to take time off, and the qualifications and specialization of the nurses themselves are why scheduling in the ER is difficult to optimize. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. Preparing schedules or determining the number of nurses on duty is still done manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. In solving this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal emergency department (IGD) nurse duty scheduling. Genetic algorithms, also called search algorithms, are based on the mechanisms of natural selection and genetics. Genetic algorithms are one of the appropriate methods for solving complex optimization problems. This method is good enough to optimize shift scheduling for the Emergency Room Nursing Service in a Hospital. This Genetic Algorithm can be a solution to multi-criteria and multi-objective problems modeled using biological and evolutionary processes. So, the concept of this method can be applied in optimizing the Nursing Service schedule. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions and being eliminated by mutation because they could not compete with several other strong individuals.

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]

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]

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]

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]

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects. [ABSTRACT FROM AUTHOR]

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-to-predict global business market, especially job-shop production. However, even if there is a properly planned schedule for production, and there is also a technique for scheduling in Reconfigurable Manufacturing System (RMS) but job-shop production will always come out with errors and disruption due to complex and uncertainty happening during the production process, hence fail to fulfil the due-date requirements. This study proposes a generic control strategy for piloting the implementation of a complex scheduling challenge in an RMS. This study is aimed to formulate an optimization-based algorithm with a simulation tool to reduce the throughput time of complex RMS, which can comply with complex product allocations and flexible routings of the system. The predictive-reactive strategy was investigated, in which Genetic Algorithm (GA) and dispatching rules were used for predictive scheduling and reactivity controls. The results showed that the proposed optimization-based algorithm had successfully reduced the throughput time of the system. In this case, the effectiveness and reliability of RMS are increased by combining the simulation with the optimization algorithm. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization). [ABSTRACT FROM AUTHOR]

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

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized overall total tardiness). Computational experiments showed that the heuristic part (DAS-h) of the proposed algorithm significantly enhanced the performance of the GA for large-size problems. The results indicated that the proposed algorithm should only be used for large-scale PMS problems because DAS-h trapped GA in a region of local optima, limiting its capabilities in small- and mainly medium-sized problems. [ABSTRACT FROM AUTHOR]

Linear projects are a special type of repetitive projects. Major disadvantages have been shown when using the critical path method (CPM) for linear project problems. So, to overcome these shortcomings linear scheduling method (LSM) was used to analyze linear projects. There are different types of activities in linear projects. However, existing mathematical models for LSM cannot represent all types of linear project activities. Therefore, this paper presents a mathematical model that can represent all types of activities using matrices. Moreover, the mathematical model is then used to create an optimization model that minimizes the project duration and cost using non-dominated sorting genetic algorithm II (NSGA-II). The two models can help project managers in scheduling real-life linear projects and find optimum linear project schedules. This paper introduces an optimization model for linear projects that utilizes matrices, offering a different approach in the field of linear project optimization. • Different types of activities in linear schedule method (LSM) are identified • New mathematical formation for (LSM) is developed using matrices • Mathematical formation helps in representing all types activities • Mathematical formation was used to build an optimization model • Optimization model solves time cost tradeoff problem for linear project [ABSTRACT FROM AUTHOR]

This research presents a transformative framework to tackle challenges within the construction industry, where efficiency is hindered by limited digitalization and disjointed project phases. The framework seamlessly integrates state-of-the-art technologies, such as Building Information Modeling (BIM), Genetic Algorithms (GAs) for schedule optimization, 5D simulation, and a Business Intelligence (BI) Dashboard. By doing so, it effectively enhances productivity while bridging the gap between the realms of management and engineering. A comprehensive literature review underpins the framework's design, focusing mainly on BIM-based schedule generation and optimization. The approach's validity is demonstrated through a compelling case study, showcasing automated BIM model creation, BIM-Based schedule generation limited to structural elements of bottom-up Cast-in-situ construction method, GAs-driven schedule optimization, 5D project simulation, and the integration of the BI dashboard. This research propels operational efficiency and promotes harmonious integration with pre-existing software systems, thus facilitating smoother adoption and adaptation. [Display omitted] • Innovative framework integrates BIM, genetic scheduling, 5D simulation & BI Dashboard. • Addresses global construction challenges: low productivity, high costs, and schedule overruns. • Comprehensive literature review informs BIM-based schedule optimization framework. • Innovates-centred data for cohesive engineering-management workflow. [ABSTRACT FROM AUTHOR]

This study presents a comparison between multi-objective optimization methods used to obtain solution for generator maintenance scheduling (GMS) problem. The GMS problem with three objectives which include the total operation cost minimization, system's reliability (gross reserve) maximization and convenience is considered in this study. Convenience objective is represented by the minimization of the violation in the number of units' maintenance outage constraint. To solve the problem of GMS, there is a need for GMS model to represent the requirements of electrical power system and optimization method to implement the model and obtain solution for the GMS. The proposed Pareto ant colony system (PACS) algorithm is based on ant colony system algorithm and Pareto approach. Pareto approach is used to make trade-off between the obtained solutions based on the three objectives. In this study comparison is made based on the results of experiments using the IEEE RTS 32-and 36-unit systems while the demand for the 32- and 36-unit systems is based on IEEE RTS demand systems. In addition, four common multi-objective algorithms based on Pareto approach i.e., the Non-dominated Sorting Genetic Algorithm II, Strength Pareto Evolutionary Algorithm 2, Multi-objective Simulated Annealing, and Multi-objective Particle Swarm Optimization are used in the evaluation of the proposed PACS algorithm. The multi-objective GMS model is implemented by all the algorithms and five performance metrics i.e., the grey relational grade (GRG), coverage, distance to Pareto front, overall Pareto spread and the number of non-dominated solutions are used in the evaluation. The Friedman test is also used to evaluate the algorithms' performance statically, which is made based on GRG metric. The experimental results showed that the proposed PACS algorithm was able to obtain a robust solution by considering different initial operational hours of the units. In term of GRG metric, the PACS algorithm was able to obtain the best results for the 32-unit systems in all the maintenance windows. However, for the 36-unit system, the PACS algorithm secured the second-best results at the early stages of the operation time but outperformed other algorithms during other operation times. For other metrics, overall the PACS algorithm has the best performance in terms of coverage, distance to Pareto front and overall Pareto spread metrics while the NSGAII has the best result in terms of the number of obtained non-dominated solutions. Friedman test implies that the PACS algorithm is significantly better than the other comparative algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time. Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized.

• The issue featured with concurrent job operations - mobile robots scheduling problems in complex FMS environment. • A logical and practical approach for the selection of GA parameters based on fuzzy rules to improve the solution to the complex combinatorial NP-hard scheduling problem. • Adaptively adjustment of genetic operator, i.e., crossover and mutation for better exploration and exploitation activities in searching process. Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate scheduling problems in manufacturing. After a brief introduction to the modelling approach to the scheduling problem, the study focuses on the optimization approach to the scheduling problem. Firstly, the different optimization approaches are categorised and their respective advantages and disadvantages are shown. This is followed by detailed analysis of the characteristics and applicability of each of the commonly used optimization approaches. Finally, a case study is presented. mathematical model is developed with the objective of minimising the maximum completion time for a mixed flow shop scheduling problem, and a genetic algorithm is used to solve the problem. The validity of the model is verified through the case study, which can provide a reasonable scheduling solution for actual manufacturing. This provides a reference for the selection and use of methods for solving scheduling problems in practical production. [ABSTRACT FROM AUTHOR]

To solve the complicated scheduling problem of many observations in earth-observing satellites, we propose an effective multi-objective combinatorial optimization method using a genetic algorithm (GA) to maximize speed, visibility, and the number of adoptions of target points according to the priority. A characteristic of the proposed method is the constraint of “one observation per point.” Another is that conflict between the observation tasks is considered. To determine the conflicts, the attitude maneuver time is calculated by assuming a triangular or trapezoidal linear angular velocity profile in order to reduce the computation time and obtain an actual control time that is as close as possible. The simulation results indicate that the proposed method can adopt more target points than the comparative method without considering the attitude maneuvering time. Moreover, the Nankai-Trough earthquake demonstration shows the effectiveness of disaster monitoring. [ABSTRACT FROM AUTHOR]

The problem of timetabling in universities is an NP-hard problem, which involves a task scheduling activity for certain people based on multiple constraints and a limited number of resources. This paper addresses a complicated issue, that of scheduling academic classes (timetabling). The aim is to find a feasible schedule for the Faculty of Economics, Administration and Business at the University "Stefan cel Mare" in Suceava. The approach focuses on the use of genetic algorithms and has, as its main objective, the minimization of idle time for students who participate in teaching activities. [ABSTRACT FROM AUTHOR]

The resource-constrained project scheduling problem (RCPSP) is a basic problem in project management. The net present value (NPV) of discounted cash flow is used as a criterion to evaluate the financial aspects of RCPSP in many studies. But while most existing studies focused on only the contractor’s NPV, this article addresses a practical extension of RCPSP, called the payment scheduling negotiation problem (PSNP), which considers both the interests of the contractor and the client. To maximize NPVs of both sides and achieve a win–win solution, these two participants negotiate together to determine an activity schedule and a payment plan for the project. The challenges arise in three aspects: 1) the client’s NPV and the contractor’s NPV are two conflicting objectives; 2) both participants have special preferences in decision making; and 3) the RCPSP is nondeterministic polynomial-time hard (NP-Hard). To overcome these challenges, this article proposes a new approach with the following features. First, the problem is reformulated as a biobjective optimization problem with preferences. Second, to address the different preferences of the client and the contractor, a strategy of multilevel region interest is presented. Third, this strategy is integrated in the nondominated sorting genetic algorithm II (NSGA-II) to solve the PSNP efficiently. In the experiment, the proposed algorithm is compared with both the double-level optimization approach and the multiobjective optimization approach. The experimental results validate that the proposed method can focus on searching in the region of interest (ROI) and provide more satisfactory solutions. [ABSTRACT FROM AUTHOR]

Existing SCS (space-constrained scheduling) studies fall short of minimizing the effect of the stacking of trades that decline productivity due to an increase in resources within a physically limited work area. This article presents a space-constrained scheduling optimization (i.e., SSO) method for minimizing the stacking of trades. It imports schedule information from the project database, extracts IFC files of construction site area from the BIM model, defines the occupation density function of each activity to track the level of stacking of trades, and identifies the optimal solution (i.e., the optimal set of pairs of execution pattern alternatives and start times of activities) by implementing genetic algorithm (GA) optimization analysis. The study is of value to practitioners because SSO provides an easy-to-use computerized tool that reduces the lengthy computations relative to data processing and GAs. Test cases verify the validity of the computational method. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers. Design/methodology/approach: A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts' opinions. Results are compared by dynamic genetic algorithm and optimum solution results. Findings: Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA's performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms. Research limitations/implications: This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks' emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools. Originality/value: Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing. [ABSTRACT FROM AUTHOR]

Cooperative three-dimensional (3D) printing (C3DP)--a representative realization of cooperative manufacturing (CM)--is a novel approach that utilizes multiple mobile 3D printing robots for additive manufacturing (AM). It makes the make-span much shorter compared with traditional 3D printing due to parallel printing. In C3DP, collision-free scheduling is critical to the realization of cooperation and parallel operation among mobile printers. In the extant literature, there is a lack of methods to schedule multi-robot C3DP with limited resources. This study addresses this gap with two methods. The first method, dynamic dependency list algorithm (DDLA), uses a constraint-satisfaction approach to eliminate solutions that could result in collisions between robots and collisions between robots with already-printed materials. The second method, modified genetic algorithm (GA), uses chromosomes to represent chunk assignments and utilizes GA operators, such as the crossover and mutation, to generate diverse print schedules while maintaining the dependencies between chunks. Three case studies, including two large rectangular bars in different scales and a foldable sport utility vehicle (SUV), are used to demonstrate the effectiveness and performance of the two methods. The results show that both methods can effectively generate valid print schedules using a specified number of robots while attempting to minimize the make-span. The results also show that both methods generate a print schedule with equal print time for the first two case studies with homogeneous chunks. In contrast, the modified GA outperforms the DDLA in the third case study, where the chunks are heterogeneous in volume and require different times to print. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew production rates. Design/methodology/approach: The model computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization module that utilizes genetic algorithms to search for and identify optimal crew deployment plans that provide optimal trade-offs between project duration and crew deployment plan cost. Findings: A real-life example of street renovation is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the stochastic scheduling of crew deployments in repetitive construction projects. Originality/value: The original contribution of this research is creating a novel multiobjective stochastic scheduling optimization model for both serial and nonserial repetitive construction projects that is capable of identifying an optimal crew deployment plan that simultaneously minimizes project duration and crew deployment cost. [ABSTRACT FROM AUTHOR]

With the use of computers in every field, the usage intensity of processors is increasing day by day. In order to cope with this intensity, processors need to be constantly updated and improved. Various scheduling algorithms are being developed every day in order to continuously improve and accelerate the processors and to cope with the processing intensity. The main purpose of the scheduling algorithms is to increase the efficiency of the processor. For this, the processor needs to make a planning between the pending processes. Planning can be done by giving different priorities or using different methods. In order to increase the efficiency of the processor, in this paper, existing algorithms were examined. Round Robin Algorithm was improved using Genetic Algorithm. It is aimed to find the optimum values by selecting the quantum value according to the pending processes and their parameters. When the developed new method compared with the existing standard algorithms, it is observed that the waiting time and the turnaround time were shorter than the other algorithms. [ABSTRACT FROM AUTHOR]

Procuring the correct materials at the right time and for the lowest cost is essential in construction. This fact underlines the importance of an optimized material procurement schedule integrated with the construction schedule/program. However, few studies examine the development of a material procurement schedule by integrating the construction schedule and optimizing material costs as well as any material shortage impact. In addition, budget constraints and maximum storage capacity are rarely captured in existing models for material procurement optimization. To address these shortcomings, an optimization model is developed using the nondominated sorting genetic algorithm II (NSGA-II) and executed in MATLAB R2017a. This model was implemented in a construction project that developed a material delivery schedule based on the budget constraints and maximum storage capacity for five majorly used building materials, resulting in a costs saving of 31.17% and a reduction in shortage impact of 83.47% compared with the actual delivery schedule. The developed model also incorporates the minimum order quantity and standard shipping size as well as avoiding surplus materials, leading to sustainable procurement of materials. Construction practitioners can use this model for procuring materials when facing budget constraints in a construction project with the lowest cost and least shortage and without excess or insufficient purchasing. The model will aid in completing the project within the stipulated time and budgeted cost. [ABSTRACT FROM AUTHOR]

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction. [ABSTRACT FROM AUTHOR]

Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system. [ABSTRACT FROM AUTHOR]

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

To address problems involving the poor matching ability of supply and demand information and outdated scheduling methods in agricultural machinery operation service, in this study, we proposed a harvester operation scheduling model and algorithm for an order-oriented multi-machine collaborative operation within a region. First, we analysed the order-oriented multi-machine collaborative operation within the region and the characteristics of agricultural machinery operation scheduling, examined the revenue of a mechanized harvesting operation and the components of each cost, and constructed a harvester operation scheduling model with the operation income as the optimization goal. Second, we proposed a simulated annealing genetic algorithm-based harvester operation scheduling algorithm and analysed the validity and stability of the algorithm through experimental simulations. The results showed that the proposed harvester operation scheduling model effectively integrated the operating cost, transfer cost, waiting time cost, and operation delay cost of the harvester, and the accuracy of the harvester operation scheduling model was improved; the harvester operation scheduling algorithm based on simulated annealing genetic algorithm (SAGA) was able to obtain a global near-optimal solution of high quality and stability with high computational efficiency. [ABSTRACT FROM AUTHOR]

The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient. [ABSTRACT FROM AUTHOR]

This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a major requirement for production enterprises, especially for energy intensive production sectors such as casting. Despite the significant energy-efficiency potential through optimized planning and the acknowledged application potential for sophisticated simulation-based methods, digital tools for practical planning applications are still lacking. The authors develop a planning method featuring a hybrid (discrete-continuous) simulation-based multi-criteria optimization (a multi-stage hybrid heuristic and metaheuristic method) for a metal casting manufacturer and apply it to a heat treatment process, that requires order batching and sequencing/scheduling on parallel machines, considering complex restrictions. The results show a ~10% global goal optimization potential, including traditional business goals and energy efficiency, with a ~6% energy optimization. A basic feasibility demonstration of applying the method to synchronize energy demand with fluctuating supply by considering flexible energy prices is conducted. The method is designed to be included in the planning loop of metal casting companies: receiving orders, machine availability, temperature data and (optional) current energy market price-data as input and returning an optimized plan to the production-IT systems for implementation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the outdoor campaign allocation problem (OCAP), which asks for the distribution of campaign items to billboards considering a number of constraints. In particular, for a metropolitan city with a large number of billboards, the problem becomes challenging. We propose a genetic algorithm-based method to allocate campaign items effectively, and we compare our results with those of nonlinear integer programming and greedy approaches. Real-world data sets are collected with the given constraints of the price class ratios of billboards located in İstanbul and the budgets of the given campaigns. The methods are evaluated in terms of the efficiency of the constructed plans and the construction time of the planning. The results reveal that the genetic algorithm-based approach gives close to optimal results in the shortest scheduling time for the OCAP, and it scales linearly with the increasing data sizes. [ABSTRACT FROM AUTHOR]

In recent years, the number of stakeholders of construction projects has significantly increased; this has required the simultaneous achievement of competing objectives, such as reductions in the time, cost, resources, and environmental impact of a project, for example. In order to achieve a balance between these objectives, several multiple-objective construction scheduling models have been reported in the literature. However, several challenges have been encountered, due to the complexities of modelling and visualizing more than three objectives simultaneously. Some of these challenges are addressed in this work via the development of a many-objective scheduling model (MOSM) based on a non-dominated sorting genetic algorithm for the simultaneous optimization of four objectives: time, cost, resources and environmental impact. Coordinate plots are used to visualize the trade-offs made between all four of these objectives. A weighted sum is introduced that offers the project team the freedom to choose an optimal solution, depending on the specific priorities of the project, and the practical application of the model is demonstrated via a case study. Our MOSM allows the optimal outcome to be achieved in construction projects with multiple objectives. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a qualitative framework for the selection of the most appropriate optimization algorithm for the multi-objective trade-off problem (MOTP) in construction projects based on the predefined performance parameters. Design/methodology/approach: A total of 6 optimization algorithms and 13 performance parameters were identified through literature review. The experts were asked to indicate their preferences between each pair of optimization algorithms and performance parameters. A multi-criteria decision-making tool, namely, consistent fuzzy preference relation was applied to analyze the responses of the experts. The results from the analysis were applied to evaluate their relative weights which were used to provide a ranking to the algorithms. Findings: This study provided a qualitative framework which can be used to identify the most appropriate optimization algorithm for the MOTP beforehand. The outcome suggested that non-dominated sorting genetic algorithm (NSGA) was the most appropriate algorithm whereas linear programming was found to be the least appropriate for MOTPs. Originality/value: The devised framework may provide a useful insight for the construction practitioners to choose an effective optimization algorithm tool for preparing an efficient project schedule aiming toward the desired optimal improvement in achieving the various objectives. Identification of the absolute best optimization algorithm is very difficult to attain due to various problems such as the inherent complexities and intricacies of the algorithm and different class of problems. However, the devised framework offers a primary insight into the selection of the most appropriate alternative among the available algorithms. [ABSTRACT FROM AUTHOR]

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback - high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.

This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach: The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings: For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value: Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments. [ABSTRACT FROM AUTHOR]

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional 'small' 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 'small' instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage. [ABSTRACT FROM AUTHOR]

Compiling quantum circuits on target quantum hardware architectures is one of the key issues in the development of quantum algorithms, and the related problem is known as the Quantum Circuit Compilation Problem (QCCP). This paper presents a genetic algorithm for solving QCCP instances for Quantum Approximate Optimization Algorithms (QAOA). In particular, such instances represent quantum circuits for the resolution of both MaxCut and Graph-Coloring combinatorial problems. The presented algorithm represents a significant improvement over an already existing genetic algorithm called Decomposition Based Genetic Algorithm (DBGA), and is characterized by a completely new coding scheme that allows to reduce the number of SWAP gates introduced in the decoding step, consequently reducing the circuit depth. After providing a description of the problem, this paper presents the newly produced genetic algorithm (termed DBGA-X) in detail, especially focusing on the new coding/decoding scheme. Subsequently, a set of results will be presented that demonstrate the superior performance of the new method compared with the results obtained from recent literature against the same benchmark. In addition, new benchmarks characterized by larger quantum architectures and by a higher number of compilation passes are proposed in this paper, to the aim of testing the scalability of the proposed method in more realistic scenarios. [Display omitted] • A genetic algorithm to tackle the Quantum Circuit Compilation Problem is described. • The new coding scheme is one of the key points of the genetic algorithm. • The resulting algorithm outperforms the state-of-the-art in standard benchmarks. • We propose a new benchmark with larger and harder instances than previous ones. [ABSTRACT FROM AUTHOR]

There are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may not be the optimal for all kinds of applications. The following research presents a metaheuristic based on a Genetic Algorithm to solve problems of type Flow Shop Scheduling with the objective of minimizing the completion time of all jobs, known in literature as makespan or Cmax. This problem is typical of combinatorial optimization and can be found in manufacturing environment, where there are conventional machines-tools and different types of pieces which share the same route. A set of crossover and selection operators are implemented methods for the pro posed Genetic Algorithm once its main factors are calibrated, the size of the population, number of generations, mutation and crossover factors, statistical study is performed in order to determine the combinations of these parameters that has a greater influence. Finally, the combination of parameters whit the best performance is tested with problems of different levels of complexity in order to obtain satisfactory results in terms of solutions quality.