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Integrated Supply Chain Optimization Model Using Mixed Integer Linear Programming

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Języki publikacji
EN
Abstrakty
EN
This article presents an integrated approach to optimize the different functions in a supply chain on strategic tactical and operational levels. The integrated supply chain model has been formulated as a cost minimization problem in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport, distribution and environmental protection were adopted as optimization criteria. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the LINGO package. The implementation model and the numerical tests are presented and discussed. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and optimization of the supply chain.
Rocznik
Strony
37--48
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
  • Kielce University of Technology, Poland
autor
  • Kielce University of Technology, Poland
Bibliografia
  • [1] Simchi-Levi, D., Kaminsky, P., Simchi-Levi E. Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill, New York 2003.
  • [2] Shapiro, J.F., Modeling the Supply Chain, Duxbury Press 2001.
  • [3] Huang, G.Q., Lau, J.S.K., Mak, K.L., 2003. The impacts of sharing production information on supply chain dynamics: a review of the literature. International Journal of Production Research 41, 1483–1517.
  • [4] Beamon, B.M., Chen, V.C.P., 2001. Performance analysis of conjoined supply chains. International Journal of Production Research 39, 3195–3218.
  • [5] Kanyalkar, A.P., Adil, G.K., 2005. An integrated aggregate and detailed planning in a multi-site production environment using linear programming. International Journal of Production Research 43, 4431–4454.
  • [6] Perea-lopez, E., Ydstie, B.E., Grossmann, I.E., 2003. A model predictive control strategy for supply chain optimization. Computers and Chemical Engineering 27, 1201–1218.
  • [7] Park, Y.B., 2005. An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research 43, 1205–1224.
  • [8] Jung, H., Jeong, B., Lee, C.G., 2008. An order quantity negotiation model for distributor-driven supply chains. International Journal of Production Economics 111, 147–158.
  • [9] Rizk, N., Martel, A., D’amours, S., 2006. Multi-item dynamic production–distribution planning in process industries with divergent finishing stages. Computers and Operations Research 33, 3600–3623.
  • [10] Selim, H., Am, C., Ozkarahan, I., 2008. Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transportation Research Part E-Logistics and Transportation Review 44, 396–419.
  • [11] Lee, Y.H., Kim, S.H., 2000. Optimal production–distribution planning in supply chain management using a hybrid simulation-analytic approach. Proceedings of the 2000 Winter Simulation Conference 1 and 2, 1252–1259.
  • [12] Chern, C.C., Hsieh, J.S., 2007. A heuristic algorithm for master planning that satisfies multiple objectives. Computers and Operations Research 34, 3491–3513.
  • [13] Jang, Y.J., Jang, S.Y., Chang, B.M., Park, J., 2002. A combined model of network design and production/distribution planning for a supply network. Computers and Industrial Engineering 43, 263–281.
  • [14] Timpe, C.H., Kallrath, J., 2000. Optimal planning in large multi-site production networks. European Journal of Operational Research 126, 422–435.
  • [15] Schrijver, A., Theory of Linear and Integer Programming. ISBN 0-471-98232-6, John Wiley & sons. 1998.
  • [16] Monczka, R.M., Trent, R.J., Handfield, R., 2002. Purchasingand supply chain management, second ed. South-Western Thompson
  • [17] Kim, SooWook, 2007.Organizational structures and the performance of supply chain management. International Journal of Production Economics,Vol: 106, Issue: 2, pp. 323-345.
  • [18] Torabi, S.A., Hassini, E., 2008. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems 159,193–214.
  • [19] www.lindo.com.
  • [20] www.ibm.com.
  • [21] Sitek P., Wikarek J., Cost optimization of supply chain with multimodal transport, Federated Conference on Computer Science and Information Systems (FedCSIS), 2012, pp. 1111-1118.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-519d3cc7-36ee-473a-b5cb-4d1ba7a5f589
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