Multi-Plant Production and Transportation Planning Based on Data Envelopment Analysis

Fang LIU, Gongbing BI, Jingjing DING, Liang Liang

Abstract


This paper proposes a methodology for developing a coordinated aggregate production plan for manufacturers producing multiple products at multiple plants simultaneously, in a centralized environment via data envelopment analysis (DEA).
Based on demand forecast of the planning horizon, the central decision maker (DM) specifies the optimal combination of input resources required by the optimal output targets for each plant to keep the supply and demand in balance, and the accompanying transportation trips and volumes among distribution centers (DCs) or warehouse facilities. In this paper, we focus on an integrated production-transportation problem since production and transportation are two fundamental ingredients in the whole operation chain. We deal with multiple products manufactured in multiple plants.
The proposed mixed integer DEA models minimize both production costs and transportation costs. The capacity constraint for each plant is enforced by using the production possibility set theory. Finally, we validate our models by a numerical example and sensitivity analysis.


Keywords


Integrated production-transportation planning; Data envelopment analysis

Full Text:

PDF

References


Alemany, M. M .E., Boj, J. J., Mula, J., & Lario, F. (2010). Mathematical programming model for centralized master planning in ceramic tile supply chains. International Journal of Production Research, 48(17), 5053-5074.

Amirteimoori, A., & Kordrostami, S. (2011). Production planning: A DEA-based approach. International Journal of Advanced Manufacturing Technology, 56, 369-376.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.

Barbarosoğlu, G., & Özgür, D. (1999). Hierarchical design of an integrated production and 2-echelon distribution system. European Journal of Operational Research, 118(3), 464-484.

Beasley, J. E. (2003). Allocating fixed costs and resources via data envelopment analysis. European Journal of Operational Research, 147(1), 198-216.

Camm, J. D., Chorman, T. E., Dill, F. A., Evans, J. R., Sweeney, D. J., & Wegryn, G. W. (1997). Restructuring P&G’s supply chain. Interfaces, 27(1), 128-142.

Charnes, A., Cooper, W. W., & Rhodes, E. L. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.

Dhaenens-Flipo, C., & Finke, G. (2001). An integrated model for an industrial production-distribution problem. IIE Transactions, 33(9),705-715.

Du, J., Liang, L., Yao,C., & Bi, G. (2010). DEA-based production planning. Omega, 38(1), 105-112.

Golany, B. (1988). An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of the Operational Research Society, 39(8), 725-734.

Hochbaum, D. S., & Hong, S. P. (1996). On the complexity of the production-transportation problem. SIAM Journal on Optimization, 6(1), 250-264.

Hugos, M. H. (2011). Essentials of supply chain management (3rd ed.). John Wiley & Sons.

Jain, A., & Palekar, S. (2005). Aggregate production planning for a continuous reconfigurable manufacturing process. Computers & Operations Research, 32(5), 1213-1236.

Jayaraman, V., & Pirkul, H. (2001). Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research, 133(2), 394-408.

Kanyalkar, A. P., & Adil, G. K. (2007). An integrated aggregate and detailed planning in a multi-site production environment using linear programming. International Journal of Production Research, 45(22), 5329-5353.

KopaNos, M., Puigjaner, L., & Georgiadis, C. (2012). Simultaneous production and logistics operations planning in semicontinuous food industries. Omega, 40(5), 634-650.

Korhonen, P., & Syrjanen, M. (2004). Resource allocation based on efficiency analysis. Management Science, 50(8), 1134-1144.

KuNo, T., & UtsuNomiya, T. (1997). A pseudo-polynomial primal-dual algorithm for globally solving a production-transportation problem. Journal of Global Optimization, 11(2), 163-180.

KuNo, T., & UtsuNomiya, T. (2000). A lagrangian based branch-and-bound algorithm for production-transportation problems. Journal of Global Optimization, 18(1), 59-73.

Lozano, S., & Villa, G. (2004). Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis, 22, 143-161.

Martin, C. H., Dent, D. C., & Eckhart, J. C. (1993). Integrated production, distribution, and inventory planning at Libbey-Owens-Ford. Interfaces, 23(3),68-78.

Malekmohammadi, N., Lotfi, F. H., & Jaafar, A. B. (2010). Imprecise centralized resource allocation in DEA. Applied Mathematical Sciences, 4(25), 1241-1257.

Papageorgiu, G. (2009). Supply chain optimization for the process industries: Advances and Opportunities. Computers and Chemical Engineering, 33(12),1931-1938.

Simchi-Levi, D., Wu, S. D., & Shen, Z-J. (2004). Handbook of quantitative supply chain analysis: Modeling in the E-business era. Kluwer Academic Publishers.

Tuy, H., Dan, D., & Ghannadan, S. (1993). Strongly polynomial time algorithms for certain concave minimization problems on networks. Operations Research Letters, 14(2), 99-109.

Tuy, H., Ghannadan, S., Migdalas, A., & Varbrand, P. (1996). A strongly polynomial algorithm for a concave production-transportation problem with a fixed number of nonlinear variables. Mathematical Programming, 72(3), 229-258.

Zuo, M., Kuo, W., & McRoberts, K. L. (1991). Application of mathematical programming to a large-scale agricultural production and distribution system. Journal of Operational Research Society, 42, 639-648.




DOI: http://dx.doi.org/10.3968/4536

Refbacks

  • There are currently no refbacks.


Copyright (c)



Reminder

  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

Online Submissionhttp://cscanada.org/index.php/css/submission/wizard

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.
  • We only use four mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases: caooc@hotmail.com; office@cscanada.net; ccc@cscanada.net; ccc@cscanada.org

 Articles published in Canadian Social Science are licensed under Creative Commons Attribution 4.0 (CC-BY).

 

Canadian Social Science Editorial Office

Address: 1020 Bouvier Street, Suite 400, Quebec City, Quebec, G2K 0K9, Canada.
Telephone: 1-514-558 6138 
Website: Http://www.cscanada.net; Http://www.cscanada.org 
E-mail:caooc@hotmail.com; office@cscanada.net

Copyright © Canadian Academy of Oriental and Occidental Culture