Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem

•A multi-objective distributed hybrid flow shop scheduling problem is studied.•A MOEA-LS is presented for solving the MDHFSP.•Several multiple neighborhoods local search operators are designed.•An adaptive weight updating mechanism is utilized to update weight set.•The MOEA-LS obtains better results...

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Bibliographic Details
Published in:Expert systems with applications Vol. 183; p. 115453
Main Authors: Shao, Weishi, Shao, Zhongshi, Pi, Dechang
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 30.11.2021
Elsevier BV
Subjects:
Business competition
Earliness and tardiness
Evolutionary algorithms
Factories
Genetic algorithms
Industrial plants
Job shop scheduling
Job shops
Makespan
Multi-objective distributed hybrid flow shop scheduling problem
Multiple neighborhoods local search
Multiple objective analysis
Neighborhoods
Optimization
Searching
Simulated annealing
Workload
Multiple neighborhoods local search
Makespan
Multi-objective distributed hybrid flow shop scheduling problem
Workload
Earliness and tardiness
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A multi-objective distributed hybrid flow shop scheduling problem is studied.•A MOEA-LS is presented for solving the MDHFSP.•Several multiple neighborhoods local search operators are designed.•An adaptive weight updating mechanism is utilized to update weight set.•The MOEA-LS obtains better results than other algorithms. In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with the objectives of minimizing makespan, total weighted earliness and tardiness, and total workload. In the MDHFSP, a set of jobs have to be assigned to several factories, and each factory contains a hybrid flow shop scheduling problem with several parallel machines in each stage. A multi-objective evolutionary algorithm based on multiple neighborhoods local search (MOEA-LS) is proposed to solve the MDHFSP. In the initialization phase, a weighting mechanism is used to decide which position is the best one for each job when constructing a new sequence. Several multiple neighborhoods local search operators based on the three objectives are designed to generate offsprings. Some worse neighboring solutions are replaced by the solutions in the achieve set with a simulated annealing probability. In order to avoid trapping into local optimum, an adaptive weight updating mechanism is utilized when the achieve set has no change. The comprehensive comparison with other classic multi-objective optimization algorithms shows the proposed algorithm is very efficient for the MDHFSP.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115453