Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 239, P. 122434 - 122434

Published: Nov. 4, 2023

Language: Английский

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A distributed permutation flow-shop considering sustainability criteria and real-time scheduling

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 39, P. 100598 - 100598

Published: March 12, 2024

Recent advancements in production scheduling have arisen response to the need for adaptation dynamic environments. This paper addresses challenge of real-time within context sustainable production. We redefine distributed permutation flow-shop problem using an online mixed-integer programming model. The proposed model prioritizes minimizing makespan while simultaneously constraining energy consumption, reducing number lost working days and increasing job opportunities permissible limits. Our approach considers machines operating different modes, ranging from manual automatic, employs two strategies: predictive-reactive proactive-reactive scheduling. evaluate rescheduling policies: continuous event-driven. To demonstrate model's applicability, we present a case study auto workpiece manage complexity through various reformulations heuristics, such as Lagrangian relaxation Benders decomposition initial optimization well four problem-specific heuristics considerations. For solving large-scale instances, employ simulated annealing tabu search metaheuristic algorithms. findings underscore benefits strategy efficiency event-driven policy. By addressing challenges integrating sustainability criteria, this contributes valuable insights into

Language: Английский

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Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 40, P. 100620 - 100620

Published: May 3, 2024

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 112780 - 112780

Published: Jan. 1, 2025

Language: Английский

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A knowledge-guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 128, P. 107458 - 107458

Published: Nov. 15, 2023

Language: Английский

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A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 29

Published: May 30, 2024

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and importance production processes. It is normally assumed that each machine typically operated by one at any time; therefore, shop-floor managers need to decide on most efficient assignments for machines workers. However, processing time variable uncertain due fluctuating environment caused unsteady operating conditions learning effect Meanwhile, they also balance workload while meeting efficiency. Thus a dual resource-constrained FJSP worker's fuzzy (F-DRCFJSP-WL) investigated simultaneously minimise makespan, total workloads maximum workload. Subsequently, reinforcement enhanced multi-objective memetic algorithm based decomposition (RL-MOMA/D) proposed solving F-DRCFJSP-WL. For RL-MOMA/D, Q-learning incorporated into perform neighbourhood search further strengthen exploitation capability algorithm. Finally, comprehensive experiments extensive test instances case study aircraft overhaul are conducted demonstrate effectiveness superiority method.

Language: Английский

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A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107818 - 107818

Published: Jan. 9, 2024

Language: Английский

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Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108775 - 108775

Published: June 12, 2024

Language: Английский

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An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 130, P. 107762 - 107762

Published: Dec. 26, 2023

Language: Английский

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Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Complex System Modeling and Simulation, Journal Year: 2024, Volume and Issue: 4(1), P. 82 - 108

Published: March 1, 2024

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop problem with sequence-dependent (EADHFSP-ST) that simultaneously optimizes makespan energy consumption. We develop a mixed integer linear programming model to describe this present two-stage adaptive memetic algorithm (TAMA) surprisingly popular mechanism. First, initialization strategy is designed based on two optimization objectives ensure convergence diversity solutions. Second, multiple population co-evolutionary approaches are proposed for global search escape from traditional cross-randomization balance exploration exploitation. Third, considering (MA) framework less efficient due randomness selection local operators, TAMA searches. The first stage accumulates more experience updating (SPA) guide second operator ensures convergence. gets rid designs an elite archive diversity. Fourth, five problem-specific operators designed, non-critical path deceleration right-shift strategies efficiency. Finally, evaluate performance algorithm, experiments performed benchmark 45 instances. experimental results show can solve effectively.

Language: Английский

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