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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Advances in teaching–learning-based optimization algorithm: A comprehensive survey(ICIC2022)

Neurocomputing, Journal Year: 2023, Volume and Issue: 561, P. 126898 - 126898

Published: Oct. 5, 2023

Language: Английский

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Scheduling stochastic distributed flexible job shops using an multi-objective evolutionary algorithm with simulation evaluation

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: June 4, 2024

The trend of reverse globalisation prompts manufacturing enterprises to adopt distributed structures with multiple factories for improving production efficiency, meeting customer requirements, and responding disturbance events. This study focuses on scheduling a flexible job shop random processing time achieve minimal makespan total tardiness. First, stochastic programming model is established formulate the concerned problems. Second, in accordance natures two objectives randomness, an evolutionary algorithm incorporating evaluation method designed. In it, population-based external archive-based search processes are developed searching candidate solutions, integrates simulation discrete event calculate objective values acquired solutions. Finally, mathematical optimisation solver, CPLEX, employed validate approach. A set cases solved verify performance proposed method. comparisons discussions show superiority handling problems under study.

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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A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals

IEEE Transactions on Automation Science and Engineering, Journal Year: 2024, Volume and Issue: 22, P. 2501 - 2513

Published: April 1, 2024

The Distributed Job-shop Scheduling Problem (DJSP) is a significant issue in both academic and industrial fields. In real-world production, uncertain disturbances such as job arrivals are inevitable. the paper, DJSP with addressed Multi-action Deep Reinforcement Learning (MDRL) method. Firstly, multi-action Markov Decision Process (MDP) formulated, where hierarchical space combining operation set factory proposed. reward function related to machine idle time. Additionally, state transition also elaborately designed, which includes four typical cases based on arrival times. Then, scheduling policy two decision networks proposed, Graph Neural Network (GNN) applied extract intrinsic information of scheme. A Proximal Policy Optimization (PPO) actor-critic frameworks designed train model achieve intelligent decision-making action selections. Extensive experiments conducted 1350 instances. comparison among 17 composite rules, 3 closely-rated DRL methods, 2 metaheuristics has proven outperformance proposed MDRL. application MDRL an automotive engine manufacturing company demonstrated its engineering value field. Note Practitioners —The common challenge faced by equipment manufacturers, specifically electronic device industry. These manufacturers located different areas have varying facility configurations trajectories. To address this challenge, learning-based method can be for daily production tasks. This divides into subproblems, namely assigning sequencing, uses solve them. uncertainty caused arrivals, rescheduling process update mechanism carefully designed. GNN used feature extraction at each point, it feeds extracted features make optimal selection. ability self-learning self-adapting, effectiveness been through test Its practical scenarios company. future, adopted more complex distributed problems that constraints transportation costs breakdowns.

Language: Английский

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A Q-learning-based improved multi-objective genetic algorithm for solving distributed heterogeneous assembly flexible job shop scheduling problems with transfers

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 79, P. 398 - 418

Published: Feb. 8, 2025

Language: Английский

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A Q-Learning Evolutionary Algorithm for Solving the Distributed Mixed No-Idle Permutation Flowshop Scheduling Problem

Symmetry, Journal Year: 2025, Volume and Issue: 17(2), P. 276 - 276

Published: Feb. 11, 2025

In this paper, a Distributed Mixed No-Idle Permutation Flowshop Scheduling Problem with Sequence-Dependent Setup Times (DMNIPFSP/SDST) is studied. Firstly, multi-objective optimization model completion time (makespan), Total Energy Consumption (TEC), and Tardiness (TT) as objectives established. Based on problem characteristics characteristics, Q-Learning Evolutionary Algorithm (QLEA) proposed. Secondly, in order to improve the quality diversity of initial solution, two improved initialization strategies are solved (In distributed system, symmetry design adopted ensure that load each workstation relatively balanced different periods, avoid resource waste or bottleneck, achieve goal no idle.), novel population updating mechanism designed balance ability global exploration local development algorithm. At same time, variable neighborhood search based used refine non-dominated thus guiding evolution. Finally, simulation results show method has good performance solving DMNIPFSP/SDST can provide economic benefits for enterprises.

Language: Английский

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IBJA: An improved binary DJaya algorithm for feature selection

Journal of Computational Science, Journal Year: 2023, Volume and Issue: 75, P. 102201 - 102201

Published: Dec. 14, 2023

Language: Английский

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Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 73, P. 170 - 191

Published: Feb. 8, 2024

Language: Английский

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A discrete artificial bee colony algorithm and its application in flexible flow shop scheduling with assembly and machine deterioration effect

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111593 - 111593

Published: April 16, 2024

Language: Английский

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