Solving Manufacturing Cell Design Problems by Using a Bat Algorithm Approach
Manufacturing Cell Design is a problem that consist in distributing machines in cells, in such a way productivity is improved. The idea is that a product, build up by using different parts, has the least amount of travel on its manufacturing process. To s
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ntificia Universidad Cat´ olica de Valpara´ıso, Valpara´ıso, Chile {ricardo.soto,broderick.crawford,ignacio.araya}@ucv.cl, [email protected], [email protected], [email protected] 2 Universidad Aut´ onoma de Chile, Santiago, Chile 3 Universidad Cientifica del Sur, Lima, Peru 4 Universidad Central de Chile, Santiago, Chile 5 Facultad de Ingenier´ıa y Tecnolog´ıa, Universidad San Sebasti´ an, Bellavista 7, 8420524 Santiago, Chile [email protected]
Abstract. Manufacturing Cell Design is a problem that consist in distributing machines in cells, in such a way productivity is improved. The idea is that a product, build up by using different parts, has the least amount of travel on its manufacturing process. To solve the MCDP we use the Bat Algorithm, a metaheuristic inspired by a feature of the microbats, the echolocation. This feature allows an automatic exploration and exploitation balance, by controlling the rate of volume and emission pulses during the search. Our approach has been tested by using a wellknown set of benchmark instances, reaching optimal values for most of them. Keywords: Bio-inspired systems · Bat algorithm Manufacturing Cell Design Problems
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Metaheuristic
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Introduction
The Manufacturing Cell Design Problem (MCDP) consist in grouping components under the following statement: “Similar things should be manufactured in the same way”. Then, the design of an optimal production plant is achieved through the organization of the different machines that process parts of a given product in production cells. The goal of the MCDP consist in minimize movements and exchange of material between these cells. Different metaheuristics have been used for cell formation. Aljaber et al. [1] made use of Tabu Search. Wu et al. [7] presented a Simulated Annealing (SA) approach. Dur´ an et al. [4] combined Particle Swarm Optimization (PSO), which consists of particles that move through a space of solutions and that are accelerated in time, with a data mining technique. Venugopal and Narendran [10] proposed using the Genetic Algorithms (GA). Gupta et al. [5] also used c Springer International Publishing Switzerland 2016 Y. Tan et al. (Eds.): ICSI 2016, Part I, LNCS 9712, pp. 184–191, 2016. DOI: 10.1007/978-3-319-41000-5 18
Solving MCDPs by using a Bat Algorithm Approach
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GA, but focusing on a different multi-objective optimization, consisting in the simultaneous minimization of the total number of movements between cells and load variation between them. It is also possible to find hybrid techniques in the problem resolution. Such is the case of Wu et al. [11], who combined SA with GA. James et al. [6] introduced a hybrid solution that combines local search and GA. Nsakanda et al. [8] proposed a solution methodology based on a combination of GA and large-scale optimization techniques. Soto et al. [9], utilized Constraint Programming (CP) and Boolean Satisfiability Technology (SAT) for the resolution of the problem, developing the problem by applying five different solvers, two of which are CP solve
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