A holistic approach to manufacturing cell formation: Incorporation of machine flexibility and machine aggregation

Abstract

One of the most practical approaches of improving productivity in a factory is to adopt the superior concept and technique of cellular manufacturing (CM) based on group technology (GT). Particularly, cell formation is an important, critical and difficult step in CM. In general, there have been a number of methodologies proposed for solving a machine-part grouping problem (MPGP). Besides considering the simple cell formation problem, some researchers have focused on machine flexibility, in which parts are having alternative routeings/process plans. However, it is very rare to consider the area of aggregation and disaggregation of machines in cell formation under uncertain constraints and uncertainty. In the light of this, the main aim of this present work is to address the MPGP holistically with the considerations of machine flexibility as well as machine aggregation and disaggregation simultaneously. In addition, based on the availability of alternative routeings, a method is proposed to generate an alternative solution for machine breakdown situations. Thus, the problem nature of this work will be more realistic and practical for today's global manufacturing era. The problem scope has been identified and the model is formulated in mathematical programming form. The objective function of this model is to minimize the total intercellular and intracellular part movement. Since MPGP has been proved to be non-polynomial (NP) complete, a genetic algorithm (GA), which is an excellent optimization technique, is employed to solve this problem.

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

“…Earlier, similar views were given by Mungawattana [49]. A large number of researchers used GA to solve the CFP such as Venugopal and Narendran [61], Joines et al [62], Gravel et al [57], Mak and Wong [63], Zhao and Wu [58], Wu et al [64], Chu and Tsai [10], and Chan et al [65].…”

Meta-heuristics in cellular manufacturing: A state-of-the-art review

“…Chi and Yan (2004) attempted to test GA in fuzzy environment considering the manufacturing factors of multi-process plan, fuzzy product demands and fuzzy technical feasibility of machines, the developed methods satisfied for the practical production situations as well as the cellular manufacturing system could become more flexible to match the real application. Chan et al (2004) proposed a multi-objective mathematical model of machine-part grouping problem with alternative routing, machine aggregation and disaggregation and a GA approach was used to solve the proposed model. According to Goncalves and Resende (2004), GA could be more effective with local heuristics in CFP domain.…”

“…Morad and Zalzala (1996) initial population is generated at random Objective function taken elitist strategy maximum number of generations Hwang and Sun (1996) permutations generated with the numbers Gravel et al (1998) generated randomly objective function value chosen by fitness When the diversity drops to zero or loss of diversity of the machine cell population should not exceed 3%. Hsu and Su (1998) generated randomly total cost, and total machine loading imbalances chosen by fitness maximum number of generations Moon and Gen (1999) generated randomly objective function value Deterministic selection strategy maximum number of generations Zhao and Wu (2000) Chan et al (2004) random population Γ Za = objective value of the alternative Individuals with higher fitness value variation in the value of the best objective function Chi and Yan (2004) generated randomly Fuzzy objective function roulette wheel approach maximum number of generations Goncalves and Resende (2004) randomly generated objective function elitist strategy Maximum No. of generation Solimanpur et al (2004) randomly generated Total objective function Probabilistic selection Maximum No.…”

A cell formation problem considering machine utilization and alternative process routes by scatter search

“…Askin et al (1997) developed a CM design method that considers the routing flexibility and volume flexibility during the design process. Chan et al (2004) considered the area of aggregation and disaggregation of machines in cell formation under uncertain constraints and uncertainty. The main aim of their study was to address the machine-part grouping problem (MPGP) considering the machine flexibility, machine aggregation, and disaggregation simultaneously.…”

“…They used a genetic algorithm (GA) to solve the given problem minimizing the total inter and intra-cellular part movements. Chan et al (2004) proposed a two-stage approach for solving cell formation and cell layout problems considering a sequence of machine cells. Two mathematical models are formulated for a MPGP and a cell layout problem (CLP) that solved by a GA. Multi-objective model Kim et al (2004) presented a two-phase heuristic algorithm to deal with multi-objective CF problems.…”