Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm

• Published in: Computers & industrial engineering Vol. 60; no. 3; pp. 376 - 384
• Main Authors: Cakir, Burcin, Altiparmak, Fulya, Dengiz, Berna
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.04.2011; Pergamon Press Inc
• Subjects: Algorithms; Assembly lines; Heuristic; Lists; Mathematical analysis; Mathematical models; Minimization; Multi-objective optimization; Optimization; Optimization algorithms; Parallel stations; Pareto optimum; Simulated annealing; Stochastic assembly line balancing; Stochastic models; Strategy; Studies; Parallel stations; Multi-objective optimization; Stochastic assembly line balancing; Simulated annealing
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2010.08.013

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.