An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times


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Delice Y., Aydogan E. K., Soylemez I., Ozcan U.

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, cilt.43, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s12046-018-0969-9
  • Dergi Adı: SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Assembly line balancing, U-type assembly lines, two-sided assembly lines, sequence-dependent set-up times, ant colony optimization, priority rules, GENETIC ALGORITHM, MODEL
  • Kayseri Üniversitesi Adresli: Evet

Özet

Some practical arrangements in assembly lines necessitate set-up times between consecutive tasks. To create more realistic models of operations, set-up times must be considered. In this study, a sequence-dependent set-up times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included set-up times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent set-up times between consecutive tasks and consecutive cycles, called the "forward set-up time'' and the "backward set-up time'', respectively. Additionally, we examine the "crossover set-up time'', which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent set-up times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.