Integer Programming for Discrete Optimization of the Agile Supply Chain Configuration Problem
Date
2013-06
Authors
Beauchamp, Hayden D.
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Abstract
In order to keep manufacturing operations in lockstep with current market trends, businesses must continue to incorporate agility into their supply chains. This includes the ability to assess and select new suppliers quickly. The Digital Manufacturing Market (DMM) and Manufacturing Service Description Language (MSDL) have been devised previously as the necessary IT components for improving the intelligence of the supply chain configuration process. The objective of this research is to enhance the performance of the DMM's search engine by incorporating combinatorial optimization techniques. In particular, this research is aimed at creating an integer programming formulation to efficiently and effectively solve the supply chain configuration problem by maximizing the technological competencies of the assigned suppliers, while meeting capacity and distance constraints. The column generation approach is adopted to resolve the issue of limited scalability of the traditional LP formulation. Vendor cuts are proposed as a method to constrain distance inside the supply chain network. The proposed column generation formulation successfully enables transition from a computationally prohibitive methodology to a fully scalable model that maintains functionality at very large sizes. The results also show that it is possible to achieve an economy of distance with little effect on match compatibility.
Description
Keywords
Discrete optimization, Integer programming, Supplier selection problem, Generalized assignment problem, Column generation
Citation
Beauchamp, H. D. (2013). <i>Integer programming for discrete optimization of the agile supply chain configuration problem</i> (Unpublished thesis). Texas State University-San Marcos, San Marcos, Texas.