An order allocation model in logistics service supply chain based on the pre-estimate behaviour and competitive-bidding strategy

摘要

In previous studies on the order allocation of the supply chain, suppliers involved in order allocation are expected to accept orders passively. However, in the actual order allocation process of logistics service supply chain (LSSC), functional logistics service providers (FLSPs) are strategic. They will pre-estimate the order allocation results to decide whether or not to participate in order allocation. Besides, FLSPs will compete for orders by bidding strategy when there are more than one FLSP in order allocation. Therefore, it is necessary to introduce the pre-estimate behaviour and competitive-bidding strategy of FLSPs into the study of order allocation in LSSC. In this article, the pre-estimate behaviour and competitive-bidding strategy are considered and the bidding range of each FLSP is obtained. It is assumed that the logistics service integrator (LSI) allocates the order sequentially to FLSPs from the lowest price to highest price. Then, a multi-objective dynamic programming model with the objectives of the cost of LSI and the order satisfaction of FLSPs is built. Numerical analysis is followed to discuss the effects of some parameters on the order allocation results. Research shows that the quote of a FLSP only depends on its own cost and the highest industry cost but irrelevant to the industry lowest cost when considering competitive-bidding strategy of FLSPs; besides, too low or too high in industry cost affects the performance of order allocation; furthermore, pre-estimate behaviour and competitive-bidding strategy of FLSPs can help reduce the order allocation cost of LSI and improve the performance of LSSC. In the end, an example of Tianjin Baoyun Logistics Company is used to introduce the order allocation process of logistics service when Baoyun considers pre-estimate behaviour and competitive-bidding strategy of FLSPs, which helps to illustrate the application of model conclusions.