Optimization of a Joint Economic Lot Size Model for a First-tier Supplier with Sequential Processes Using a Genetic Algorithm
Keywords:Integrated supply chain, First-tier supplier, Lot size, Nonlinear programming, Genetic algorithm
As corporate competition intensifies in the 21st century, optimal in the integrated supply chain is more important than optimization of individual company. Our research examines a series of integrated supply chain systems comprising a single raw material supplier, a first-tier supplier with multiple processes, and a single original equipment manufacturer in the Korean automotive industry. Unlike other papers, we have studied the situation in which the first-tier supplier has an assembly process. We have also analyzed the situation in which the demand for semi-finished products occurs in the first-tier supplier process or in which semi-finished products are purchased from subcontractors and put into production. The objective function is to minimize the sum of production costs, inventory holding costs, ordering costs, and setup costs in the integrated supply chain. To solve this problem, we formulated nonlinear programming, and developed a genetic algorithm. The results showed that using a dynamic lot size is cheaper than using a fixed lot size. In addition, the lower the setup cost, the smaller the lot size, and when a certain level is reached, the lot size will be the same even if the cost changes.
(1) GOYAL, S. K. An integrated inventory model for a single supplier-single customer problem. The International Journal of Production Research, 1977, 15.1: 107-111.
(2) BANERJEE, Avijit. A joint economic‐lot‐size model for purchaser and vendor. Decision Sciences, 1986, 17.3: 292-311.
(3) HILL, Roger M. The optimal production and shipment policy for the single-vendor single buyer integrated production-inventory problem. International Journal of Production Research, 1999, 37.11: 2463-2475.
(4) TALEIZADEH, Ata Allah; NIAKI, Seyed Taghi Akhavan; BARZINPOUR, Farnaz. Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: a harmony search algorithm. Applied Mathematics and Computation, 2011, 217.22: 9234-9253.
(5) LEE, Wenyih. A joint economic lot size model for raw material ordering, manufacturing setup, and finished goods delivering. Omega, 2005, 33.2: 163-174.
(6) MUNSON, Charles L.; ROSENBLATT, Meir J. Coordinating a three-level supply chain with quantity discounts. IIE Transactions, 2001, 33.5: 371-384.
(7) WANG, Shaojun; SARKER, Bhaba R. Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy. European Journal of Operational Research, 2006, 172.1: 179-200.
(8) KHOUJA, Moutaz. Optimizing inventory decisions in a multi-stage multi-customer supply chain. Transportation Research Part E: Logistics and Transportation Review, 2003, 39.3: 193-208.
(9) JABER, M. Y.; GOYAL, S. K. Coordinating a three-level supply chain with multiple suppliers, a vendor and multiple buyers. International Journal of Production Economics, 2008, 116.1: 95-103.
(10) BEN-DAYA, M.; AL-NASSAR, A. An integrated inventory production system in a three-layer supply chain. Production Planning and Control, 2008, 19.2: 97-104.
(11) ADEINAT, Hamza; VENTURA, José A. Integrated pricing and lot-sizing decisions in a serial supply chain. Applied Mathematical Modelling, 2018, 54: 429-445.
(12) LIM, I. J.; PARK, K. S.; KIM, J. H. Determining Optimal Lot Size for Batch Processes Using TOPSIS. Management Education Research, 2015, 30: 163-185.