Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time

Journal of Industrial and Management Optimization, Год журнала: 2024, Номер 20(11), С. 3373 - 3414

Опубликована: Янв. 1, 2024

In this paper, a multi-objective hybrid flowshop scheduling problem (HFSP) with limited waiting time and machine capability constraints is addressed. Given its importance, the implementation of lot streaming division methodologies investigated through design experiment (DoE) setting based on real data extracted from leading tire manufacturer in Gebze, Turkey. By doing so, specific characteristics addressed HFSP can be further explored to provide insights into complexity suggest recommendations for improving operational efficiency such systems resembling it. Based specifications constraints, novel generic optimization model objectives including makespan, average flow time, total workload imbalance formulated. Since studied NP-hard strong sense, several algorithms non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) are proposed according methodologies, i.e., consistent sublots equal sublots. main aim analyze problem, developed compared each other gain remarkable problem. Four different comparison metrics employed assess solution quality terms intensification diversification aspects. Computational results demonstrate that employing sublot methodology leads significant improvements all methodology.

Язык: Английский

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An effective cooperative coevolutionary algorithm with global and local-oriented cooperative mechanisms for multi-objective hybrid flowshop lot-streaming scheduling with limited and flexible sub-lots

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101815 - 101815

Опубликована: Янв. 18, 2025

Язык: Английский

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Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing

Journal of Manufacturing Systems, Год журнала: 2024, Номер 76, С. 188 - 206

Опубликована: Авг. 3, 2024

Язык: Английский

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Integrated scheduling of multi-objective lot-streaming hybrid flowshop with AGV based on deep reinforcement learning

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 29

Опубликована: Июль 9, 2024

Язык: Английский

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Lot-Streaming Workshop Scheduling with Operation Flexibility: Review and Extension

Systems, Год журнала: 2025, Номер 13(4), С. 271 - 271

Опубликована: Апрель 9, 2025

Lot-streaming scheduling methods with operation flexibility have been widely used in aerospace, semiconductor, automotive, pharmaceutical and other manufacturing enterprises. Lot-splitting attracted much more attention from academia industry due to an urgent requirement for effective way improve the productivity of flexible workshop scheduling. During past decade, many works made on different lot-streaming The scope this review focuses journal publications collected Web Science database, among which 80% are high-ranked journals. This paper aims provide a comprehensive survey flexibility. First, jobs discussed objectives as well constraints applications summarized. Then, problem models their solution approaches reviewed. Next, research trends applications, modeling recalled. Finally, potential future directions concluded.

Язык: Английский

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A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 794 - 823

Опубликована: Апрель 24, 2025

Язык: Английский

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A New Bipolar Approach Based on the Rooster Algorithm Developed for Utilization in Optimization Problems

Applied Sciences, Год журнала: 2025, Номер 15(9), С. 4921 - 4921

Опубликована: Апрель 29, 2025

Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic widely used the literature, leading development of novel variants. In this paper, new swarm-based algorithms, called Improved Roosters Algorithm (IRA), Bipolar (BRA), (BIRA), which mainly based on (RA), presented. First, versions RA (IRA, BRA, BIRA) were compared terms performance, revealing that BIRA achieved significantly better results than Then, performance algorithm was with performances Standard Genetic (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), thus, its literature tested. Moreover, also included test show version, BIRA, is more successful previous one (RA). For all comparisons, 20 well-known benchmark functions, 11 CEC2014 17 CEC2018 CEC2020 suite, employed. To validate significance results, Friedman Wilcoxon Signed Rank statistical tests conducted. addition, three commonly problems field engineering real-life scenarios: pressure vessel, gear train, tension/compression spring design. The indicate proposed (BIRA) provides algorithms.

Язык: Английский

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Optimal Task Allocation and Sequencing for Flight Test Based on a Memetic Algorithm With Lexicographic Optimisation

Expert Systems, Год журнала: 2025, Номер 42(2)

Опубликована: Янв. 12, 2025

ABSTRACT The flight test plays an important role in the development of aircraft. Currently, with increasing complexity and higher validation requirements for aircraft, there is a crucial need to generate high‐quality task schedules efficient way. This paper proposes scheduling problem (FTTSP), which involves assigning suitable aircraft executing tasks given order. Generally, duration (FTD) primary optimisation objective schedule, as it has direct impact on costs time enter market. In this study, FTTSP not only considers FTD but also takes into account transfer consumption (TTC). A mixed‐integer linear programming mathematical model first formulated describe characteristics TTC sequential manner. Then, memetic algorithm lexicographic (MALO) proposed, can efficiently obtain solution ensure that most critical metric be fully optimised. MALO, two‐vector encoding logic relationship repair mechanism based binary tree are established. An idle insertion decoding method designed improve utilisation rate. addition selection, crossover mutation operators, local search operator enhance quality. Finally, full‐scale instances generated evaluate algorithm's performance. numerical results demonstrate effectiveness competitiveness MALO generating schedule tasks.

Язык: Английский

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Multi-objective grey wolf optimizer based on reinforcement learning for distributed hybrid flowshop scheduling towards mass personalized manufacturing

Expert Systems with Applications, Год журнала: 2024, Номер 264, С. 125866 - 125866

Опубликована: Ноя. 26, 2024

Язык: Английский

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An Effective Cooperative Coevolutionary Algorithm with Global and Local-Oriented Cooperative Mechanisms for Multi-Objective Hybrid Flowshop Lot-Streaming Scheduling with Limited and Flexible Sub-Lots

Опубликована: Янв. 1, 2024

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Язык: Английский

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