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International Business Management
Year: 2016  |  Volume: 10  |  Issue: 19  |  Page No.: 4708 - 4712

A Knowledge-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

Muhammad Ridwan Andi Purnomo    

Abstract: This study presents application of an improved Genetic Algorithm (GA) for solving Flexible Job Shop Scheduling Problem (FJSP). Flexible job Shop Production System (FJPS) is the extension of classical job shop production system. In the FJPS, a job has fixed operations sequence and every operation could be processed by one of machines in a Work Station (WS). The processing time could be different if the job is processed by different machine in same WS. FJPS are commonly found in furniture or semi-conductor industries. In term of scheduling, problem in FJSP is distribution of jobs and their schedule in every machine. Such problem is a hard combinatorial problem and one of the algorithm that could be used to solve the problem is GA. However, based on preliminary study, a conventional GA could not perform effective searching process when being used to solve FJSP. In this study, a conventional GA would be improved by using a knowledge-based system which extracted from a FJPS. Further, the improved GA is called as Knowledge-Based GA (KB-GA). A case study shows that the proposed KB-GA could conduct effective searching process and has superior performance compared to a conventional GA.

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