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An Population Management Genetic algorithm on coordinated scheduling problem between suppliers and manufacture  

Yang, Byoung-Hak (Department of Industrial Engineering, Kyungwon University)
Badiru, Adedeji B. (Air Force Institute of Technology)
Publication Information
Journal of the Korea Safety Management & Science / v.11, no.3, 2009 , pp. 131-138 More about this Journal
Abstract
This paper considers a coordinated scheduling problem between multi-suppliers and an manufacture. When the supplier has insufficient inventory to meet the manufacture's order, the supplier may use the expedited production and the expedited transportation. In this case, we consider a scheduling problem to minimize the total cost of suppliers and manufacture. We suggest an population management genetic algorithm with local search and crossover (GALPC). By the computational experiments comparing with general genetic algorithm, the objective value of GALPC is reduced by 8% and the calculation time of GALPC is reduced by 70%.
Keywords
Population management genetic algorithm; Local Search; Scheduling;
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1 Boudia, M., C. Prins. 'A memetic algorithm with dynamic population management for an integrated production-distribution problem.', European Journal of operational Research 195 (2009) 703-715   DOI   ScienceOn
2 Moon, C., YH. Lee, CS. Jeong, YS. Yun. 'Integrated process planning and scheduling in a supply chain.', Computers & Industrial Engineering 54 (2008) 1048-1061   DOI   ScienceOn
3 Bensaou, M. 'Portfolios of Buyer-Supplier Relationships.',Sloan Management Review(1999) 35-43
4 Yang, B., AB. Badiru, S. Saripalli. 'A Facility Resource Scheduling for two stage supply chain.', Journal of the Korean Institute of Plant Engineering 10(1) (2005) 67-78
5 Huggins, EL., TL. Olsen. 'Supply chain management with guaranteed delivery.', Management Science 49(9) (2003) 1154-1167   DOI   ScienceOn
6 Lee, YH. CH. Jeong, C. Moon. 'Advanced planning and Scheduling with outsourcing in manufacturing supply chain.', Computers & Industrial Engineering 43 (2002) 351-374   DOI   ScienceOn
7 Moon, C, Y. Seo. 'Evolutionary algorithm for advanced process planning and scheduling in a multi-plant.',Computers & Industrial Engineering 48 (2005) 311-325   DOI   ScienceOn
8 Yimer, AS., K. Demirli. 'A genetic approach to two-phase optimization of dynamic supply chain scheduling.', Computers & Industrial Engineering (2009), Corrected Proof
9 So¨rensen, K., M. Sevaux. 'MA|PM: Memetic algorithms with population management.', Computers and Operations Research 33 (2006) 1214–1225   DOI   ScienceOn