The joint procurement model and algorithm for small and medium enterprises. (May 2021)

• Record Type: Journal Article
• Authors: Yuyang, Tan; Wenchao, Zhao; Chunxiang, Guo
• Is Part Of: Computers & industrial engineering. Volume 155(2021)
• Publication Date: 2021-05
• Subjects: Joint procurement -- Small and medium-sized enterprises -- Minimum order quantity -- Multi-objective model -- The improved genetic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107179
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We propose a new joint procurement model and algorithm for SMEs. We consider multiple realistic factors in the proposed model. We design an improved genetic algorithm to solve the multi-objective model. We conduct a numerical experiment and sensitivity analysis to examine the efficiency of the proposed model and algorithm. Abstract: Due to the disadvantages of economic scale and industry status, the purchase order of a small and medium enterprise (SME) cannot often reach the minimum order quantity set by a strong supplier, and it is difficult for an SME to make vertical purchase cooperation with strong suppliers. Therefore, a joint procurement model is proposed in the horizontal cooperation among SMEs. The model considers multiple objective and multiple realistic constraints. To solve the proposed multi-objective model, the improved genetic algorithm is designed to determine the optimal purchased quantity in SMEs. Finally, a numerical experiment is conducted to validate the feasibility of the proposed model and algorithm. As the results illustrated, the joint procurement model and the improved genetic algorithm are efficient for SMEs under the weak procurement environment. The gaps among the optimal Pareto solutions are relatively small in contrast to the initial solution. Besides, a sensitivity analysis is also performed to prove that the algorithm is effectively solving the real-world joint procurement problem.