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

Cakir B., ALTIPARMAK BAYKOÇ F., DENGİZ B.

COMPUTERS & INDUSTRIAL ENGINEERING, cilt.60, sa.3, ss.376-384, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 3
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.cie.2010.08.013
  • Dergi Adı: COMPUTERS & INDUSTRIAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.376-384
  • Anahtar Kelimeler: Stochastic assembly line balancing, Parallel stations, Multi-objective optimization, Simulated annealing, PARALLEL WORKSTATIONS, GENETIC ALGORITHM, HEURISTIC METHOD, MODEL, STATIONS, SINGLE, METHODOLOGY, SYSTEMS, DESIGN, SOLVE
  • Gazi Üniversitesi Adresli: Evet

Özet

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. (C) 2010 Elsevier Ltd. All rights reserved.