Abstract
Article history: Received 18 December 2010 Received in revised form 14 February 2011 Accepted 15 February 2011 Available online 15 February 2011 Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time. © 2011 Growing Science Ltd. All rights reserved
Highlights
Cellular manufacturing system (CMS) is an effective system to produce part-families, economically
We propose a new method where the processing time associated with a part is decreased through learning effect in cellular manufacturing systems
A multi-objective group scheduling problem has been provided for the cellular manufacturing system with the consideration of the learning effect
Summary
Cellular manufacturing system (CMS) is an effective system to produce part-families, economically. Rα) for the scheduling problem of a single machine which assumes that the processing time of a part in its exponential model depends on its position He showed that this problem can be solved to minimize the total completion time of the parts by the rule of shortest processing time (Biskup, 2008). Chen et al (2006) applied the exponential learning model developed earlier for two-machine flow shop scheduling problem in order to minimize the weighted total completion times and the maximum tardiness, and provided a branch and bound algorithm to solve the resulted problem. Ku and Yung (2007) studied a case where the setup time depends on sequence with a position-based learning effect in one machine scheduling problem They found that this problem can be solved by the shortest processing time rule, in order to minimize total completion time and minimize makespan. Due date every parts-family is equal to the maximum due date for the parts of it part-family
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