The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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The Decoding Approaches of Genetic Algorithm for Job Shop Scheduling Problem

Job Shop 일정계획 문제 풀이를 위한 유전 알고리즘의 복호화 방법

  • Kim, Jun Woo (Department of Industrial and Management Systems Engineering, Dong-A University)
  • Received : 2016.10.12
  • Accepted : 2016.12.27
  • Published : 2016.12.31
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Abstract

Purpose The conventional solution methods for production scheduling problems typically focus on the active schedules, which result in short makespans. However, the active schedules are more difficult to generate than the semi active schedules. In other words, semi active schedule based search strategy may help to reduce the computational costs associated with production scheduling. In this context, this paper aims to compare the performances of active schedule based and semi active schedule based search methods for production scheduling problems. Design/methodology/approach Two decoding approaches, active schedule decoding and semi active schedule decoding, are introduced in this paper, and they are used to implement genetic algorithms for classical job shop scheduling problem. The permutation representation is adopted by the genetic algorithms, and the decoding approaches are used to obtain a feasible schedule from a sequence of given operations. Findings The semi active schedule based genetic algorithm requires slightly more iterations in order to find the optimal schedule, while its execution time is quite shorter than active schedule based genetic algorithm. Moreover, the operations of semi active schedule decoding is easy to understand and implement. Consequently, this paper concludes that semi active schedule based search methods also can be useful if effective search strategies are given.

Purpose 생산 일정계획 문제의 해법들은 일반적으로 총처리시간이 짧은 active 스케줄에 초점을 맞추어 해를 탐색하는 경우가 많다. 그러나 active 스케줄은 semi active 스케줄에 비해 생성하는 것이 까다롭기 때문에, 일정계획을 생성하는데 소요되는 계산 비용을 감안하면 semi active 스케줄을 적절히 활용하는 것이 도움이 될 수 있다. 이에, 본 논문에서는 동일한 생산 일정계획 문제에 active 스케줄기반 탐색 방법과 semi active 스케줄 기반 탐색 방법을 적용함으로써 이들의 성능을 비교해보고자 하였다. Design/methodology/approach 각 공정들의 작업장 할당 순서를 의미하는 permutation encoding 기반 유전 알고리즘을 고전적인 job shop 일정계획 문제에 적용하기 위해 본 논문에서는 active 스케줄 복호화 및 semi active 스케줄 복호화의 두 가지 복호화 방법을 소개하였으며, 이들은 공정들의 순열로부터 실행가능한 스케줄을 얻는데 사용되었다. Findings semi active 스케줄 기반 유전 알고리즘은 active 스케줄 기반 유전 알고리즘에 비해 최적해를 탐색하는데 소요되는 반복 횟수가 좀 더 많은 경향이 있었으나, 알고리즘 실행 시간을 훨씬 짧았다. 나아가, semi active 스케줄 복호화는 그 절차가 단순하여 이해하고 구현하기 용이하다는 장점이 있었다. 따라서, 효과적인 해 탐색 전략이 주어지는 경우에는 semi active 스케줄에 기반한 해법이 일정계획 문제 풀이에 도움이 될 수도 있을 것으로 보여진다.

Keywords

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