A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

This work focuses on multi-objective scheduling problems of automated manufacturing systems. Such an automated manufacturing system has limited resources and flexibility of processing routes of jobs, and hence is prone to deadlock. Its scheduling problem includes both deadlock avoidance and performa...

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Vydáno v:Advances in mechanical engineering Ročník 12; číslo 1
Hlavní autoři: Zan, Xin, Wu, Zepeng, Guo, Cheng, Yu, Zhenhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.01.2020
Sage Publications Ltd
SAGE Publishing
Témata:
Automation
Classification
Feasibility
Genetic algorithms
Manufacturing
Multiple objective analysis
Optimization
Petri nets
Production scheduling
Scheduling
Sorting algorithms
genetic algorithm
scheduling
Automated manufacturing system
multi-objective optimization
Petri net
ISSN:1687-8132, 1687-8140
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Shrnutí:This work focuses on multi-objective scheduling problems of automated manufacturing systems. Such an automated manufacturing system has limited resources and flexibility of processing routes of jobs, and hence is prone to deadlock. Its scheduling problem includes both deadlock avoidance and performance optimization. A new Pareto-based genetic algorithm is proposed to solve multi-objective scheduling problems of automated manufacturing systems. In automated manufacturing systems, scheduling not only sets up a routing for each job but also provides a feasible sequence of job operations. Possible solutions are expressed as individuals containing information of processing routes and the operation sequence of all jobs. The feasibility of individuals is checked by the Petri net model of an automated manufacturing system and its deadlock controller, and infeasible individuals are amended into feasible ones. The proposed algorithm has been tested with different instances and compared to the modified non-dominated sorting genetic algorithm II. The experiment results show the feasibility and effectiveness of the proposed algorithm.
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ISSN:1687-8132
1687-8140
DOI:10.1177/1687814019885294