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Supplier Selection and Order Allocation Based on Integer Programming

Author

Listed:
  • Hayden Beauchamp

    (Energy Solutions, Richland, WA, USA)

  • Clara Novoa

    (Ingram School of Engineering, Texas State University, San Marcos, TX, USA)

  • Farhad Ameri

    (Department of Engineering Technology, Texas State University, San Marcos, TX, USA)

Abstract

The ability to assess and select new suppliers quickly and efficiently is a critical requirement for improving the agility of manufacturing supply chains. The Digital Manufacturing Market (DMM) is a web-based platform for intelligent supply chain configuration. This research enhances the DMM's performance by developing a column generation method for solving the supplier selection problem. The objective of the proposed method is to maximize the technological competencies of the selected suppliers while meeting their capacity constraints. The column generation method resolves the issue of limited scalability of a traditional linear programming formulation and can be integrated into the DMM. Additionally, using test generated problems, this research evaluates the effect on reducing the threshold distance traveled by semi-finished parts in the work orders. The results show that an economy of distance can be imposed with little effect on average match compatibility.

Suggested Citation

  • Hayden Beauchamp & Clara Novoa & Farhad Ameri, 2015. "Supplier Selection and Order Allocation Based on Integer Programming," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 6(3), pages 60-79, July.
    • Handle: RePEc:igg:joris0:v:6:y:2015:i:3:p:60-79
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    Cited by:

    1. P. Senthil Kumar, 2018. "Linear Programming Approach for Solving Balanced and Unbalanced Intuitionistic Fuzzy Transportation Problems," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 9(2), pages 73-100, April.

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