Distributed Co-Evolutionary Memetic Algorithm for Distributed Hybrid Differentiation Flowshop Scheduling Problem

This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-stage distributed flowshop factories; 2) job-...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 26; číslo 5; s. 1043 - 1057
Hlavní autoři: Zhang, Guanghui, Liu, Bo, Wang, Ling, Yu, Dengxiu, Xing, Keyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Assembly
Data integration
Design optimization
Differential evolution (DE)
differentiation flowshop scheduling
distributed evolutionary algorithm (DEA)
distributed production scheduling
Evolutionary algorithms
Flowcharts
Heuristic methods
Job shop scheduling
Knowledge management
Manufacturing
memetic algorithm (MA)
Memetics
Metaheuristics
Modules
Product differentiation
Production
Production facilities
Scheduling
ISSN:1089-778X, 1941-0026
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Shrnutí:This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-stage distributed flowshop factories; 2) job-to-product assembly based on specified assembly plan on a second-stage single machine; and 3) product differentiation according to customization on one of the third-stage dedicated machines. Considering the characteristics of multistage and diversified processing technologies of the problem, building new powerful evolutionary algorithm (EA) for DHDFSP is expected. To achieve this, we propose a general EA framework called distributed co-evolutionary memetic algorithm (DCMA). It includes four basic modules: 1) dual population (POP)-based global exploration; 2) elite archive (EAR)-oriented local exploitation; 3) elite knowledge transfer (EKT) among POPs and EAR; and 4) adaptive POP restart. EKT is a general model for information fusion among search agents due to its problem independence. In execution, four modules cooperate with each other and search agents co-evolve in a distributed way. This DCMA evolutionary framework provides some guidance in algorithm construction of different optimization problems. Furthermore, we design each module based on problem knowledge and follow the DCMA framework to propose a specific DCMA metaheuristic for coping with DHDFSP. Computational experiments validate the effectiveness of the DCMA evolutionary framework and its special designs, and show that the proposed DCMA metaheuristic outperforms the compared algorithms.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3150771