Mathematical programming model of cost optimization for supply chain from perspective of logistics provider

• Autorzy: Sitek P. , Wikarek J.
• Języki publikacji: EN

Abstrakty

EN

The article presents the problem of optimizing the supply chain from the perspective of a logistics provider and includes a mathematical model of multilevel cost optimization for a supply chain in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport and distribution were adopted as an optimization criterion. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the environment of LINGO ver. 12 package. The implementation details, the basics of LINGO as well as the results of 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. In addition, the article presents the current state of logistics outsourcing.

Słowa kluczowe

EN

supply chain; MILP; mixed integer linear programming; optimization; 3PL-Third Party Logistic; multimodal transport

PL

logistyka; łańcuch dostaw; koszty; optymalizacja; transport multimodalny

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2012
• Tom: Vol. 3, No. 2
• Strony: 49--61
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Sitek P.

autor

Wikarek J.

• Kielce University of Technology, Institute of Management Control Systems, Al. 1000-lecia PP 7, 25-314 Kielce, Poland, phone: [phone],

Bibliografia

• [1] Simchi-Levi D., Kaminsky P., Simchi-Levi E., Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, McGraw-Hill, ISBN 978-0-07-119896-7, New York 2003.
• [2] Shapiro J.F., Modeling the Supply Chain, ISBN 978-0-534-37741, Duxbury Press, 2001.
• [3] Huang G.Q., Lau J.S.K., Mak K.L., The impacts of sharing production information on supply chain dynamics: a review of the literature, International Journal of Production Research, 41, 1483-1517, 2003.
• [4] Beamon B.M., Chen V.C.P., Performance analysis of conjoined supply chains, International Journal of Production Research, 39, 3195-3218, 2001.
• [5] Kanyalkar A.P., Adil G.K., An integrated aggregate and detailed planning in a multi-site production environment using linear programming, International Journal of Production Research, 43, 4431-4454, 2005.
• [6] Perea-lopez E., Ydstie B.E., Grossmann I.E., A model predictive control strategy for supply chain optimization, Computers and Chemical Engineering, 27, 1201-1218, 2003.
• [7] Park Y.B., An integrated approach for production and distribution planning in supply chain management, International Journal of Production Research, 43, 1205-1224, 2005.
• [8] Jung H., Jeong B., Lee C.G., An order quantity negotiation model for distributor-driven supply chains, International Journal of Production Economics, 111, 147-158, 2008.
• [9] Rizk N., Martel A., Dàmours S., Multi-item dynamic production-distribution planning in process industries with divergent finishing stages, Computers and Operations Research, 33, 3600-3623, 2006.
• [10] Selim H., Am C., Ozkarahan I., Collaborative production-distribution planning in supply chain: a fuzzy goal programming approach, Transportation Research Part E-Logistics and Transportation Review, 44, 396-419, 2008.
• [11] Lee Y.H., Kim S.H., 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, 2000.
• [12] Chern C.C., Hsieh J.S., A heuristic algorithm for master planning that satisfies multiple objectives. Computers and Operations Research, 34, 3491-3513, 2007.
• [13] Jang Y.J., Jang S.Y., Chang B.M., Park J., A combined model of network design and production/distribution planning for a supply network, Computers and Industrial Engineering, 43, 263-281, 2002.
• [14] Timpe C.H., Kallrath J., Optimal planning in large multi-site production networks, European Journal of Operational Research, 126, 422-435, 2000.
• [15] Schrijver A., Theory of Linear and Integer Programming, ISBN 0-471-98232-6, John Wiley & Sons, 1998.
• [16] Ho H., Lim C., The logistics players - From 1PL to 5PL, Morgan Stanley: China Logistics, 8-9, 2001.
• [17] Jianming Yao, Decision optimization analysis on supply chain resource integration in fourth party logistics, Journal of Manufacturing Systems, 29, 121-129, 2010.
• [18] Chern C.C., Hsieh J.S., A heuristic algorithm for master planning that satisfies multiple objectives, Computers and Operations Research, 34, 3491-3513, 2007.
• [19] Torabi S.A., Hassini E., An interactive possibilistic programming approach for multiple objective supply chain master planning, Fuzzy Sets and Systems, 159, 193-214, 2008.
• [20] Schrijver A., Theory of Linear and Integer Programming, ISBN 0-471-98232-6, John Wiley & Sons, 1998.
• [21] www.lindo.com.
• [22] www.ibm.com.
• [23] Hokey Min, Gengui Zhou, Supply chain modeling: past, present and future, Computers and Industrial Engineering, 43, 231-249, 2002.