A NOVEL MICRO-FULFILLMENT CENTER LOCATION PROJECT SELECTION APPROACH
DOI:
https://doi.org/10.23055/ijietap.2023.30.3.8631Keywords:
logistics, micro-fulfillment center, 0-1 goal programming, TOPSIS, fuzzy set theoryAbstract
With the development of modern e-commerce and chain stores, logistics capabilities have become the key to success. A logistics company must set up many micro-fulfillment centers (MFCs) in a city or region to provide last-mile logistics services to their customers. So far, there were only a few studies have been proposed on how to select some appropriate MFCs in a city or region. This study proposed a zero-one goal programming (0-1 GP) method that considers multiple objectives to select appropriate MFC location projects in a city or region. This work classifies the critical objectives for selecting MFC location projects into benefit-related objectives and cost-related objectives and then formulates a 0-1 GP model. This model also considers the constraints of limited resources and the interactions between projects. In practice, the proposed 0-1 GP model may be complex because of the number of candidate projects and the level of interactions. This study used the "technique for order preference by similarity to ideal solution" (TOPSIS) method to simplify the proposed 0-1 GP model to a dual-objective function model or a one-objective function model to obtain a compromise solution. A case of a logistics company was used to explain the application of the proposed model and problem-solving process. The proposed method and the traditional 0-1 GP method were also compared. The results reveal that the proposed 0-1 GP model can effectively select the appropriate MFC location projects. The TOPSIS method can simplify the 0-1 GP model into a one-objective function model, and then the problem-solving process can be efficient. Two indicators proposed in the TOPSIS method, the objective value (OV) denotes the value of the objective function, and the fulfillment degree (FD) represents the degree of achievement of the objective function, can help decision makers observe and compare the compromise solutions under different weight situations to make their final decision.
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