A survey of exact algorithms for the simple assembly line balancing problem
Management Science
An efficient heuristic for solving stochastic assembly line balancing problems
Computers and Industrial Engineering
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Balancing of mixed-model two-sided assembly lines
Computers and Industrial Engineering
Assembly line balancing with station paralleling
Computers and Industrial Engineering
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A solution procedure for type E simple assembly line balancing problem
Computers and Industrial Engineering
Enhanced mixed integer programming model for a transfer line design problem
Computers and Industrial Engineering
Computers and Industrial Engineering
Assembly line balancing under uncertainty: Robust optimization models and exact solution method
Computers and Industrial Engineering
Multi-objective optimization of stochastic disassembly line balancing with station paralleling
Computers and Industrial Engineering
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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.