An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals

An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals

Liping Zhang, Xinyu Li, Long Wen, Guohui Zhang
Copyright: © 2013 |Pages: 15
DOI: 10.4018/ijssci.2013010105
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Abstract

Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structures are presented in the local search. Moreover, the performance measures investigated respectively are: minimization of the makespan and minimization of the mean tardiness. Finally, several experiments have been designed to test and evaluated the performance of the memetic algorithm. The experimental results show that the proposed algorithm is efficient to solve the flexible job shop scheduling problem in dynamic environment.
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Problem Definition

This paper is to evaluate a memetic algorithm for flexible job shop scheduling problem in dynamic environment. This problem can be stated as follows [12]; [13]:

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