Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm
✍ Scribed by Burcin Cakir; Fulya Altiparmak; Berna Dengiz
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 607 KB
- Volume
- 60
- Category
- Article
- ISSN
- 0360-8352
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✦ Synopsis
This paper deals with multi-objective optimization of a single-model stochastic assembly line balancing problem with parallel stations. The objectives are as follows: (1) minimization of the smoothness index and (2) minimization of the design cost. To obtain Pareto-optimal solutions for the problem, we propose a new solution algorithm, based on simulated annealing (SA), called m_SAA. m_SAA implements a multinomial probability mass function approach, tabu list, repair algorithms and a diversification strategy. The effectiveness of m_SAA is investigated comparing its results with those obtained by another SA (using a weight-sum approach) on a suite of 24 test problems. Computational results show that m_SAA with a multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of the quality of Pareto-optimal solutions. Moreover, we investigate the effects of properties (i.e., the tabu list, repair algorithms and diversification strategy) on the performance of m_SAA.