Abstract
In this chapter we propose a bi-criterion branch-and-bound algorithm for the cell formation problem. We demonstrate the performance of the algorithm by solving problems from the literature and comparing the results with complete enumeration as well as with the results of metaheuristic algorithms from the literature.
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References
Arkat J, Hosseini L, Farahani MH (2011) Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Syst Appl 38(8):9597–9602
Bajestani MA, Rabbani M, Rahimi-Vahed A, Khoshkhou GB (2009) A multi-objective scatter search for a dynamic cell formation problem. Comput Oper Res 36(3):777–794
Boulif M, Atif K (2008) A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies. Int J Approximate Reasoning 47(2):141–165
Dimopoulos C (2004) A review of evolutionary multiobjective optimization applications in the area of production research. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol 2, pp 1487–1494
Dimopoulos C (2007) Explicit consideration of multiple objectives in cellular manufacturing. Eng Optim 39(5):551–565
Fontes DBMM, Gaspar-Cunha A (2010) On multi-objective evolutionary algorithms. In: Zopounidis C, Pardalos PM (eds) Handbook of Multicriteria Analysis, Applied Optimization, vol 103, Springer, Berlin, pp 287–310
Lee SD, Chen YL (1997) A weighted approach for cellular manufacturing design: minimizing intercell movement and balancing workload among duplicated machines. Int J Prod Res 35(4):1125–1146
Lei D, Wu Z (2006) Tabu search for multiple-criteria manufacturing cell design. Int J Adv Manuf Tech 28:950–956
Malakooti B, Yang Z (2002) Multiple criteria approach and generation of efficient alternatives for machine-part family formationin group technology. IIE Trans 34:837–846
Mansouri SA, Husseini SM, Newman S (2000) A review of the modern approaches to multi-criteria cell design. Int J Prod Res 38(5):1201–1218
Neto ARP, Filho EVG (2010) A simulation-based evolutionary multiobjective approach to manufacturing cell formation. Comput Ind Eng 59(1):64–74
Paulavičius R, Žilinskas J, Grothey A (2010) Investigation of selection strategies in branch and bound algorithm with simplicial partitions and combination of Lipschitz bounds. Optim Lett 4:173–183
Su CT, Hsu CM (1998) Multi-objective machine-part cell formation through parallel simulated annealing. Int J Prod Res 36(8):2185–2207
Tavakkoli-Moghaddam R, Ranjbar-Bourani M, Amin G, Siadat A (2012) A cell formation problem considering machine utilization and alternative process routes by scatter search. J Intell Manuf 23:1127–1139
Venugopal V, Narendran T (1992) A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Comput Ind Eng 22(4):469–480
Žilinskas A, Žilinskas J (2009) Branch and bound algorithm for multidimensional scaling with city-block metric. J Global Optim 43:357–372
Žilinskas J, Goldengorin B, Pardalos PM (2013) Branch and bound algorithm for bi-criterion cell formation problems. Comput Oper Res xx:Submitted
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Goldengorin, B., Krushinsky, D., Pardalos, P.M. (2013). Two Models and Algorithms for Bi-Criterion Cell Formation. In: Cell Formation in Industrial Engineering. Springer Optimization and Its Applications, vol 79. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8002-0_7
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DOI: https://doi.org/10.1007/978-1-4614-8002-0_7
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