Bi-criteria Optimization in Integrated Layout Design of Cellular Manufacturing Systems Using a Genetic Algorithm

Leno, I. Jerin; Sankar, S. Saravana; Raj, M. Victor; Ponnambalam, S. G.

doi:10.1007/978-3-642-27172-4_40

Cited by 3 publications

(1 citation statement)

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“…Jolai, Taghipour and Javadi (2011) employed a binary particle swarm algorithm (PSO) to solve a QAP model for inter-cell and intra-cell layout problems considering uncertain demand of parts and batch sizes using a variable neighborhood search. Leno et al (2011) discussed the multi-objective CLP with unequal size of cells which minimizes the total material handling cost in the first place and subsequently maximizes the distance-weighted closeness factor of cells and solved it using a GA. Arkat, Farahani and Hosseini et al (2011) employed two techniques based on GA to solve an integrated model of cell formation, cell layout and cell scheduling. Similar issues are addressed by Kia et al (2012) by developing a novel non-linear model and solved that using an SA algorithm and compared successfully with the solutions of Lingo software.…”

Section: Introductionmentioning

confidence: 99%

An Immune Genetic algorithm for inter-cell layout problem in cellular manufacturing system

Ghosh

Doloi

Dan

2015

Prod. Eng. Res. Devel.

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The objective function of inter-cell layout problem minimizes the total inter-cellular material handling cost. It is mostly significant with moderate production quantity in Cellular Manufacturing Systems (CMS). This problem is classified as Quadratic Assignment Problem (QAP) which is NP-Hard in nature. Heuristic techniques are extremely effective for such problems. In this paper we have proposed a novel Immune Genetic Algorithm (Immune-GA-RS) to obtain competent inter-cell layout in the vicinity of CMS. It exploits an elitist replacement strategy in order to improve the rate of convergence. The proposed method is successfully tested with 8 datasets which are being widely used for inter-cell layout design problems. Proposed Immune-GA-RS is compared with two variants of the Genetic Algorithms, GA-RS and Alt-GA-RS and 7 other published layout design techniques. Results portray that Immune-GA-RS acquires 11.11% improved solutions with 7.72% reduced CPU time on an average. Further Immune-GA-RS is tested on 36 structured QAP instances available through QAPLIB and shown to outperform other two GA variants while attaining optimal solutions for 33 instances and also shown to outpace five recent QAP based algorithms, SA (S&S-2008), GA (S&S-2010), SC-TS (F&M-2011), IHGA (Mis-2004), GA-(KTF&D-2011) while attaining smaller solution gap for 11 test instances and obtain at least equal or better quality solutions for 24 instances. We conclude our work with a statistical data test to signify the results of Immune-GA-RS.

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Section: Introductionmentioning

confidence: 99%