{"title":"Multi-matrix Real-coded Genetic Algorithm for Minimising Total Costs in Logistics Chain Network","authors":"Pupong Pongcharoen, Aphirak Khadwilard, Anothai Klakankhai","volume":11,"journal":"International Journal of Economics and Management Engineering","pagesStart":574,"pagesEnd":580,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/15279","abstract":"The importance of supply chain and logistics\nmanagement has been widely recognised. Effective management of\nthe supply chain can reduce costs and lead times and improve\nresponsiveness to changing customer demands. This paper proposes a\nmulti-matrix real-coded Generic Algorithm (MRGA) based\noptimisation tool that minimises total costs associated within supply\nchain logistics. According to finite capacity constraints of all parties\nwithin the chain, Genetic Algorithm (GA) often produces infeasible\nchromosomes during initialisation and evolution processes. In the\nproposed algorithm, chromosome initialisation procedure, crossover\nand mutation operations that always guarantee feasible solutions\nwere embedded. The proposed algorithm was tested using three sizes\nof benchmarking dataset of logistic chain network, which are typical\nof those faced by most global manufacturing companies. A half\nfractional factorial design was carried out to investigate the influence\nof alternative crossover and mutation operators by varying GA\nparameters. The analysis of experimental results suggested that the\nquality of solutions obtained is sensitive to the ways in which the\ngenetic parameters and operators are set.","references":"[1] H. Gunnarsson, M. Ronnqvist, and J. T. Lundgren, \"Supply chain\nmodelling of forest fuel,\" Eur. J. Oper. Res., vol. 158, no. 1, pp. 103-\n123, 2004.\n[2] R.Z. Farahani, and M. Elahipanah, \"A genetic algorithm to optimize the\ntotal cost and service level for just-in-time distribution in a supply\nchain,\" Int. J. 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