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

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 distributed flexible job shop with ra...

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Bibliographic Details
Published in:International journal of production research Vol. 63; no. 1; pp. 86 - 103
Main Authors: Fu, Yaping, Gao, Kaizhou, Wang, Ling, Huang, Min, Liang, Yun-Chia, Dong, Hongyu
Format: Journal Article
Language:English
Published: London Taylor & Francis 02.01.2025
Taylor & Francis LLC
Subjects:
discrete event simulation
Discrete event systems
Distributed manufacturing
Evolutionary algorithms
flexible job shop scheduling
Genetic algorithms
Globalization
Manufacturing
Mathematical programming
multi-objective optimisation
Optimization
Production scheduling
Randomness
SDG 9: Industry, innovation and infrastructure
Simulation
Stochastic models
Stochastic programming
stochastic simulation
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary: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 distributed flexible job shop with random job processing time to achieve minimal makespan and minimal total tardiness. First, a stochastic programming model is established to formulate the concerned problems. Second, in accordance with the natures of two objectives and randomness, an evolutionary algorithm incorporating an evaluation method is designed. In it, population-based and external archive-based search processes are developed for searching candidate solutions, and the evaluation method integrates stochastic simulation and discrete event simulation to calculate objective values of acquired solutions. Finally, a mathematical optimisation solver, CPLEX, is employed to validate the developed model and optimisation approach. A set of cases is solved to verify the performance of the proposed method. The comparisons and discussions show the superiority of the proposed method for handling the problems under study.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2024.2356628