Öğretme-Öğrenme algoritmasını kullanarak iki yönlü karışık modelli montaj hattı dengeleme

Öğretme-Öğrenme-Tabanlı Eniyileme (ÖÖTE) algoritması, diğer popülasyon-tabanlı algoritmalar kadar etkin olduğu ortaya konmuş, popülasyon-tabanlı bir eniyileme algoritmasıdır. Bu makalenin temel amacı, ÖÖTE algoritmasını kullanarak iki yönlü karışık modelli montaj hattı dengeleme problemini ilk defa çözmektir. Yakın zamanda, stokastik iki yönlü tek modelli montaj hattı dengeleme problemini çözmek için [1]’de melez öğretme-öğrenme-tabanlı eniyileme (MÖÖTE) algoritması önerilmiştir. [1]’de MÖÖTE algoritması en iyi bilinen 10 farklı meta-sezgisel algoritma ile karşılaştırılmıştır. Yapılan testler MÖÖTE algoritmasının diğer algoritmalara göre daha üstün bir performans sergilediğini ortaya koymuştur. Bu makalede ayrıca, MÖÖTE algoritması iki yönlü karışık modelli montaj hattı dengeleme problemini çözmek için adapte edilmiş ve algoritmanın performansı test edilmiştir. Bu çalışmanın amacı önceden tanımlanmış çevrim süresinde karşılıklı eşleşen istasyon sayısını ve toplam istasyon sayısını en aza indirmektir. Literatürden alınan test problem grupları üzerinden kapsamlı bir deneysel çalışma gerçekleştirilmiştir ve algoritmaların performansları var olan yaklaşımlarla karşılaştırılmıştır. Deneysel çalışmalar ÖÖTE algoritmasının karşılaştırılan diğer en iyi bilinen sezgisel algoritmalara karşı göze çarpan bir potansiyele sahip olduğunu ve problemin çözümünde MÖÖTE algoritmasının bilinen en iyi sezgisel algoritmalar kadar iyi performans sergilediğini ortaya koymuştur.

Balancing of mixed-model two-sided assembly lines using teaching-learning based optimization algorithm

The Teaching-Learning Based Optimization (TLBO) algorithm is a population-based optimization technique that has been shown to be competitive against other population-based algorithms. The main purpose of this paper is to solve the balancing problem of mixed-model two-sided assembly lines by using TLBO algorithm first time in the literature. Most recently, hybrid teaching-learning-based optimization (HTLBO) algorithm is proposed by [1] for solving the balancing of stochastic simple two-sided assembly line problem. The HTBLO algorithm is compared with the well-known 10 different meta-heuristic algorithms in the literature in [1]. The tests performed underlined that HTLBO algorithm presented more outstanding performance when compared to other algorithms. In this paper, HTLBO algorithm is also adapted for solving the problem of balancing mixed-model two-sided assembly line and its performance is analysed. The objective function of this study is to minimize the number of mated-stations and total number of stations for a predefined cycle time. A comprehensive computational study is conducted on a set of test problems that are taken from the literature and the performance of the algorithms are compared with existing approaches. Experimental results show that TLBO algorithm has a noticeable potential against to the best-known heuristic algorithms and HTLBO algorithm results show that it performs well as far as the best-known heuristic algorithms for the problem in the literature.

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  • Tang Q, Li Z, Zhang L, Zhang C. “Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm”. Computers & Operations Research, 82, 102-113, 2017.
  • Scholl A, Becker C. “State-of-the-art exact and heuristic solution procedures for simple assembly line balancing”. European Journal of Operational Research, 168(3), 666-693, 2006.
  • Salveson ME. “The assembly line balancing problem”. Journal of Industrial Engineering, 6, 18-25, 1955.
  • Karp RM. Reducibility Among Combinatorial Problems. Editors: Miller RE, Thatcher JW. Complexity of computer applications, 85-104, New York, NY, USA, Plenum Press, 1972.
  • Erel E, Gokcen H. “Shortest-route formulation of mixed-model assembly line balancing problem”. European Journal of Operational Research, 116(1), 194-204, 1999.
  • Becker C, Scholl A. “A survey on problems and methods in generalized assembly line balancing”. European Journal of Operational Research, 168, 694-715, 2006.
  • Boysen N, Fliedner M, Scholl A. “Assembly line balancing: Which model to use when?”. International Journal of Production Economics, 111, 509-528, 2008.
  • Rekiek B, Delchambre A. Assembly Line Design: The Balancing of Mixed Model Hybrid Assembly Lines with Genetic Algorithms. London, England, Springer, 2006.
  • Scholl A. Balancing and Sequencing of Assembly Lines. Heidelberg, Germany, Physica-Verlag, 1999.
  • Scholl A, Klein R. “ULINO: Optimally balancing U-shaped JIT assembly lines”. International Journal of Production Research, 7(4), 721-736, 1999.
  • Simaria AS, Vilarinho PM. “2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines”. Computers & Industrial Engineering, 56(2), 489-506, 2009.
  • Bartholdi JJ. “Balancing two-sided assembly lines: A case study”. International Journal of Production Research, 31, 2447-2461, 1993.
  • Lee TO, Kim Y, Kim YK. “Two-sided assembly line balancing to maximize work relatedness and slackness”. Computers & Industrial Engineering, 40, 273-292, 2001.
  • Wu EF, Jin Y, Bao JS, Hu XF. “A branch-and-bound algorithm for two-sided assembly line balancing”. International Journal of Advanced Manufacturing Technology, 39, 1009-1015, 2008.
  • Özcan U, Toklu B. “Balancing of mixed-model two-sided assembly lines”. Computers & Industrial Engineering, 57(1), 217-227, 2009.
  • Ghosh S, Gagnon RJ. “A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems”. International Journal of Production Research, 27, 637-670, 1989.
  • Erel E, Sarin SC. “A survey of the assembly line balancing procedures”. Production Planning and Control, 9, 414-434, 1998.
  • Batini D, Faccio M, Ferrari E, Persona A, Sgarbossa F. “Design configuration for a mixed-model assembly system in case of low product demand”. The International Journal of Advanced Manufacturing Technology, 34, 188-200, 2007.
  • Li Z, Kucukkoc I, Nilakantan JM. “Comprehensive review and evaluation of heuristics and meta-heuristics for two-sided assembly line balancing problem”. Computers & Operations Research, 84, 146-161, 2017.
  • Rabbani M, Moghaddam M, Manavizadeh N. “Balancing of mixed-model two-sided assembly lines with multiple U-shaped layout”. The International Journal of Advanced Manufacturing Technology, 59(9-12), 1191-1210, 2012.
  • Chutima P, Chimklai P. “Multi-objective two-sided mixed model assembly line balancing using particle swarm optimisation with negative knowledge”. Computers & Industrial Engineering, 62, 39-55, 2012.
  • Yuan B, Zhang C, Shao X, Jiang Z. “An effective hybrid honey bee mating optimization algorithm for balancing mixed-model two-sided assembly lines”. Computers & Operations Research, 53, 32-41, 2015.
  • Li D, Zhang C, Tian G, Shao X, Li Z. “Multiobjective program and hybrid imperialist competitive algorithm for the mixed-model two-sided assembly lines subject to multiple constraints”. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 99, 1-11, 2016.
  • Delice Y, Aydoğan EK, Özcan U, İlkay MS. “A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing”. Journal of Intelligent Manufacturing, 28, 23-36, 2017.
  • Rao RV, Vakharia DP, Savsani VJ. “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems”. Computer-Aided Design, 43(3), 303-315, 2011.
  • Rao RV, Savsani VJ, Vakharia DP. “Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems”. Information Sciences, 183, 1-15, 2012.
  • Niknam T, Azizipanah-Abarghooee R, Narimani MR. “A new multi-objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems”. Engineering Applications of Artificial Intelligence, 25(8), 1577-1588, 2012.
  • Rao RV, Savsani VJ. Mechanical Design Optimization using Advanced Optimization Techniques. London, England, Springer-Verlag, 2012.
  • Rao RV, Patel V. “Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm”. Applied Mathematical Modelling, 37(3), 1147-1162, 2013.
  • Tuncel G, Aydin D. “Two-sided assembly line balancing using teaching-learning based optimization algorithm”. Computers & Industrial Engineering, 74, 291-299, 2014.
  • Kim YK, Kim Y, Kim YJ. “Two-sided assembly line balancing: A genetic algorithm approach”. Production Planning and Control, 11(1), 44-53, 2000.
  • Özcan U, Toklu B. “A tabu search algorithm for two-sided assembly line balancing”. The International Journal of Advanced Manufacturing Technology, 43, 822-829, 2009.
  • Baykasoglu A, Dereli T. “Two-sided assembly line balancing using an ant-colony-based heuristic”. The International Journal of Advanced Manufacturing Technology, 36, 582-588, 2008.
  • Özbakir L, Tapkan P. “Bee colony intelligence in zone constrained two-sided assembly line balancing problem”. Expert Systems with Applications, 38(9), 11947-11957, 2011.
  • Yuan B, Zhang C, Shao X. “A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints”. Journal of Intelligent Manufacturing, 26(1), 159-168, 2015.
  • Khorasanian D, Hejazi SR, Moslehi G. “Two-sided assembly line balancing considering the relationships between tasks”. Computers & Industrial Engineering, 66(4), 1096-1105, 2013.
  • Li D, Zhang C, Shao X, Lin W. “A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints”. Journal of Intelligent Manufacturing, 27, 725-739, 2016.
  • Mladenovic N, Hansen P. “Variable neighborhood search”. Computers & Operations Research, 24, 1097-1100, 1997.
  • Macaskill JLC. “Production-line balances for mixed model lines”. Management Science, 19, 423-434, 1972.
  • Tonge FM. “Summary of a heuristic line balancing procedure”. Management Science, 7(1), 21-42, 1960.
  • Helgeson WB, Birnie DP. “Assembly line balancing using the ranked positional weight technique”. Journal of Industrial Engineering, 12(6), 394-398, 1961.
  • Hamzadayi A, Yildiz G. “Modeling and solving static m identical parallel machines scheduling problem with a common server and sequence dependent setup times”. Computers & Industrial Engineering, 106, 287-298, 2017.
  • Montgomery DC. Design and Analysis of Experiments. New York, NY, USA, John Wiley & Sons, 2000.
Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi-Cover
  • ISSN: 1300-7009
  • Başlangıç: 1995
  • Yayıncı: PAMUKKALE ÜNİVERSİTESİ