Optimisation of multi-level logistics network for agricultural products under community group buying model

AbstractThis paper introduces a rapidly developing new online retail model, community group buying, and proposes a three-level agricultural logistics network optimisation model. Under the community group buysing model, it is necessary to use different types of vehicles for transportation of agricultural products with different temperature control requirements. The study establishes a multi-objective mixed integer programming model with the objectives of shortest transportation time and minimum total cost, taking into account the freshness penalty cost incurred during transport. The multi-objective problem is transformed into a single objective by normalisation and weighting methods. According to the calculation for the actual case, this paper solves the problems of community group buying grid warehouse location, multiple vehicles use strategy, loading capacity, transportation path optimisation and self-pickup station demand allocation. In addition, through sensitivity analysis, the management insights of community group buying enterprises are obtained: (1) The community group buying enterprises should use brokers of the community group buying model to enhance customer stickiness; (2) The enterprises should focus on developing business in less developed regions; (3) The enterprises need to adjust the proportion of time efficiency and logistics costs according to the real situation.KEYWORDS: Community group buyingagricultural logistics networkmulti-objective programmingmulti-commodity and multi-vehiclefreshness penalty AcknowledgementsThe authors would like to express their gratitude to the industry expert members and M Company of China, which is engaged in community group buying business, for their valuable support during the research period. The authors sincerely thank the editors and anonymous readers for providing valuable suggestions and comments to improve the quality of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementSome or all data or models that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Fangshan District Bureau of Commerce of Beijing Municipality of China [Grant Number B23M100030].