Balancing and sequencing mixed-model just-in-time U-lines with multiple objectives


Kara Y., Ozcan U. , Peker A.

APPLIED MATHEMATICS AND COMPUTATION, vol.184, no.2, pp.566-588, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 184 Issue: 2
  • Publication Date: 2007
  • Doi Number: 10.1016/j.amc.2006.05.185
  • Title of Journal : APPLIED MATHEMATICS AND COMPUTATION
  • Page Numbers: pp.566-588

Abstract

This study deals with the mixed-model U-lines utilized in just-in-time (JIT) production systems. Successful implementations of mixed-model U-lines requires solutions to two important problems called line balancing and model sequencing. In terms of some balance-dependent performance measures the effectiveness of a mixed-model U-line can be increased by solving line balancing and model sequencing problems simultaneously. However, this may lead to inefficient values of sequence-dependent performance measures. Hence, increasing the effectiveness of a mixed-model U-line requires balancing and sequencing problems that be dealt with multiple objectives. Balancing and sequencing mixed-model U-lines with multiple objectives has not been considered in the literature to date. In this study, a multi-objective approach for balancing and sequencing mixed-model U-lines to simultaneously minimize the absolute deviations of workloads across workstations, part usage rate, and cost of setups is presented. To increase the performance of the proposed algorithm, a newly developed neighbourhood generation method is also employed. Since the performance measures considered in the study are conflicting with each other, the proposed algorithm suggests much flexibility and more realistic results to decision makers. Solution methodology is illustrated using an example and a two-stage comprehensive experimental study is conducted to determine the effective values of algorithm parameters and investigate the relationships between performance measures. Results show that the proposed approach is more realistic than the limited number of existing methodologies. The proposed approach is also extended to consider the stochastic completion times of tasks. (C) 2006 Elsevier Inc. All rights reserved.