Chun-Hsiung Lan1, Hai-Ming Chen2, Chih-Wei Chiu This email address is being protected from spambots. You need JavaScript enabled to view it.2 and Mei-Hsiu Chen2

1Department of Business Administration, Hsing Wu Institute of Technology, Taipei, Taiwan 244, R.O.C.
2Graduate Institute of Management Sciences, Tamkang University, Tamsui, Taiwan 251, R.O.C.


 

Received: February 21, 2011
Accepted: May 16, 2011
Publication Date: June 1, 2012

Download Citation: ||https://doi.org/10.6180/jase.2012.15.2.11  


ABSTRACT


The issue of supply chain management has focused on practical operations and their theoretical applications for comparison with the traditional business aim, which merely pursues the maximal benefit. A business should simultaneously consider the benefits, resources and facilities for all stages in the supply chain system; i.e., consider the creation of an overall value for the system instead of the traditional goal of pursuing maximal benefits in certain focused stages. This study aims to provide a referenced decision-making tool for a decision-maker in the current complex environment and also function as a decision-making tool for focusing on the overall and real-time analyses of multi-interval planning for a supply chain system. This system will investigate the purchase, production, inventory, distribution, and product shortage to deal with customers’ demands for pursuing the maximal profit under a constrained production/inventory and finite distribution capacity. This research not only considers the multi-product order, multi-factory production, multi-material purchase, multi-distribution transportation, quantity discounts, diversecustomer demand, product inventory cost and limited inventory space but also provides the punishment cost of the demand shortage to construct an Integer Nonlinear Programming (INLP) mathematical model for achieving the maximum profit. Moreover, the proposed mathematical model is constructed by using the syntax of Lingo 9.0, wherein the built-in “global solver” is selected as the solution method. A numerical example then follows. This study creates a highly repetitive characteristic because of the application of the packaged software (Lingo 9.0); therefore, the proposed model and the solution method can be treated as valuable.


Keywords: Optimization, Supply Chain Management, Lingo 9.0, Punishment Cost


REFERENCES


  1. [1] Newhart, D. D., Stott, K. L. and Vasko, F. J., “Consolidating Product Sizes to Minimize Inventory Levels for a Multi-Stage Production and Distribution Systems,” Journal of Operational Research Society, Vol. 44, pp. 637644 (1993).
  2. [2] Robinson, E. P. and Satterfield, R. K., “Designing Distribution Systems to Support Vendor Strategies in Supply Chain Management,” Decision Sciences, Vol. 29, pp. 685706 (1998).
  3. [3] Gary, A., “An Application of Designing Products and Processes for Supply Chain Management,” IIE Transactions, Vol. 31, pp. 417429 (1999).
  4. [4] Jolayemi, Joel K., “Optimum Production-Distribution and Transportation Planningin Three-Stage Supply Chains,” International Journal of Business and Management, Vol. 5, pp. 2940 (2010).
  5. [5] Yung, S. K. and Yang, C. C., “Intelligent Multi-Agents for Supply Chain Management,” IEEE SMC’99 Conference Proceedings, pp. 528533 (1999).
  6. [6] Min, H. and Melachrinoudis, E., “The Relocation of a Hybrid Manufacturing/Distribution Facility from Supply Chain Perspectives: A Case Study,” Omega, Vol. 27, pp. 7585 (1999).
  7. [7] Zhou, Z., Cheng, S. and Hua, B., “Supply Chain Optimization of Continuous Process Industries with Sustainability Considerations,” Computers and Chemical Engineering, Vol. 24, pp. 11511158 (2000).
  8. [8] Moncayo-Martnez, Luis A. and Zhang, David Z., “Multi-Objective Ant Colony Optimisation: A MetaHeuristic Approach to Supply Chain Design,” International Journal of Production Economics, Vol. 131, pp. 407420 (2011).
  9. [9] Jayaraman, V. and Pirkul, H., “Planning and Coordination of Production and Distribution Facilities for Multiple Commodities,” European Journal of Operational Research, Vol. 133, pp. 394408 (2001).
  10. [10] Wang, R. C. and Liang, T. F., “Application of Fuzzy Multi-Objective Linear Programming to Aggregate Production Planning,” Computers & Industrial Engineering, Vol. 46, pp. 1741 (2004).
  11. [11] Silva, C. A., et al., “Distributed Supply Chain Management Using Ant Colony Optimization,” European Journal of Operational Research, Vol. 199, pp. 349 358 (2009).
  12. [12] Weng, Z. K., “Modeling Quantity Discount under General Price-Sensitive Demand Functions: Optimal Policies and Relations,” European Journal of Operational Research, Vol. 86, pp. 300314 (1995).
  13. [13] Fazel, F., Fischer, K. P. and Gilbert, E. W., “JIT Purchasing vs. EOQ with a Price Discount: An Analytical Comparison of Inventory Costs,” International Journal of Production Economics, Vol. 54, pp. 101109 (1998).
  14. [14] Viswanathan and Wang, Q., Discount Pricing Decisions in Distribution Channels with Price-Sensitive Research, Vol. 149, pp. 571587 (2003).
  15. [15] Tsai, J. F., “An Optimization Approach for Supply Chain Management Models with Quantity Discount Policy,” European Journal of Operational Research, Vol. 177, pp. 982994 (2007).