An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate

In this paper, by considering the imprecise or fuzzy nature of the data in real-world problems, jobshop scheduling problems with fuzzy processing time and fuzzy duedate are formulated and a genetic algorithm which is suitable for solving the formulated problems is proposed. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, the formulated fuzzy job-shop scheduling problems are interpreted so as to maximize the minimum agreement index. For solving the formulated fuzzy job-shop scheduling problems, an efficient genetic algorithm is proposed by incorporating the concept of similarity among individuals into the genetic algorithms using the Gannt chart. As illustrative numerical examples, both 6 x 6 and 10 x 10 job-shop scheduling problems with fuzzy duedate and fuzzy processing time are considered. Through the comparative simulations with simulated annealing, the feasibility and effectiveness of the proposed method are demonstrated. (C) 1999 Elsevier Science Ltd. All rights reserved.