A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations

Computers & Industrial Engineering, Год журнала: 2024, Номер 196, С. 110484 - 110484

Опубликована: Авг. 18, 2024

Язык: Английский

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Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 334 - 348

Опубликована: Фев. 26, 2024

Язык: Английский

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Multi-objective fitness landscape-based estimation of distribution algorithm for distributed heterogeneous flexible job shop scheduling problem

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112780 - 112780

Опубликована: Янв. 1, 2025

Язык: Английский

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Co-Evolutionary NSGA-III with deep reinforcement learning for multi-objective distributed flexible job shop scheduling

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110990 - 110990

Опубликована: Фев. 1, 2025

Язык: Английский

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Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110829 - 110829

Опубликована: Янв. 1, 2025

Язык: Английский

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Quantum particle swarm optimization with chaotic encoding schemes for flexible job-shop scheduling problem

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101836 - 101836

Опубликована: Янв. 7, 2025

Язык: Английский

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Production scheduling with multi-robot task allocation in a real industry 4.0 setting

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 13, 2025

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise essential seamless operations. However, research often oversimplifies Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot (MRFJS) with limited buffers. It involves non-identical parallel machines varying capabilities overseeing handling under blocking conditions. case based real scenario, layout restricts each robotic arm's access, requiring strategic buffer placement part transfers. A Mixed-Integer Programming (MILP) model aims minimize makespan, followed new Genetic Algorithm (GA) using Roy Sussman's Alternative Graph. Computational tests various scales data from manufacturing plant demonstrate GA's efficacy in solving complex problems real-world settings. Based data, Proposed (PGA), an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared Basic (BGA), ARD 0.38%. percentage indicates that PGA significantly outperforms problems.

Язык: Английский

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Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 89, С. 102782 - 102782

Опубликована: Май 14, 2024

Язык: Английский

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A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems

Decision Analytics Journal, Год журнала: 2024, Номер 11, С. 100485 - 100485

Опубликована: Май 29, 2024

The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays crucial role in enhancing productivity and efficiency modern manufacturing systems, aimed at optimizing the allocation of jobs to variable set machines. This paper introduces an algorithm tackle FJSSP by minimizing makespan total weighted earliness tardiness under uncertainty. hybrid effectively addresses complexities stochastic multi-objective integrating equilibrium optimizer (EO) as initial solutions generator, Non-dominated sorting genetic II (NSGA-II), simulation techniques. algorithm's effectiveness validated showcasing specific instances delivering decision results for optimal across varying levels Results reveal consistent superiority managing parameters various scales, achieving lower improved Pareto front quality compared existing methods. Particularly notable faster convergence robust performance, statistical Wilcoxon test, which confirms its reliability efficacy handling dynamic situations. These findings underscore potential providing flexible, solutions. proposed unique balance exploitative explorative capabilities within framework enables effective uncertainty FJSSP, offering flexibility customization adaptable environments.

Язык: Английский

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A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling

IEEE Transactions on Evolutionary Computation, Год журнала: 2024, Номер 29(1), С. 232 - 246

Опубликована: Янв. 16, 2024

With the rise of globalization and environmental concerns, distributed scheduling energy-efficient have become crucial topics in informational manufacturing system. Additionally, growing consideration about realistic constraints, such as transportation time finite resources, has made problem increasingly complex. Facing these challenges, special mechanisms are required to improve efficiency solving algorithms. In this paper, a bi-learning evolutionary algorithm (BLEA) is proposed solve flexible job shop with constraints (DEFJSP-T). Firstly, we integrate statistical learning (SL) (EL) framework, while decomposition Pareto dominance methods employed different stages handle conflicting objectives. During SL stage, probability models established statistically search for advantageous substructures on each weight vector, an update mechanism devised exploration. EL genetic operators introduced improved local that takes into account properties realize sufficient exploitation. Finally, according performance SL, novel switching between designed ensure rational allocation computing resources. Extensive experiments conducted test performances BLEA. The comparison shows BLEA superior DEFJSP-T terms effectiveness.

Язык: Английский

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