A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial schedu...

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Main Authors: Lei Wang, Chaomin Luo, Jingcao Cai
Format: Article
Language:English
Published: Hindawi-Wiley 2017-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2017/1527858
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spelling doaj-23d035d23e26446c985fba082aa4b7912020-11-24T23:34:59ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/15278581527858A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic AlgorithmLei Wang0Chaomin Luo1Jingcao Cai2School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaDepartment of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI 48221, USASchool of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaIn real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.http://dx.doi.org/10.1155/2017/1527858
collection DOAJ
language English
format Article
sources DOAJ
author Lei Wang
Chaomin Luo
Jingcao Cai
spellingShingle Lei Wang
Chaomin Luo
Jingcao Cai
A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
Journal of Advanced Transportation
author_facet Lei Wang
Chaomin Luo
Jingcao Cai
author_sort Lei Wang
title A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
title_short A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
title_full A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
title_fullStr A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
title_full_unstemmed A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm
title_sort variable interval rescheduling strategy for dynamic flexible job shop scheduling problem by improved genetic algorithm
publisher Hindawi-Wiley
series Journal of Advanced Transportation
issn 0197-6729
2042-3195
publishDate 2017-01-01
description In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.
url http://dx.doi.org/10.1155/2017/1527858
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