A Study On Logistics Service Supplier Selection And Task Allocation

Logistics Service Supply Chain is a new service supply chain, in which the integrated logistics service supplier is the core enterprise. Integrated logistics service provider integrates logistics service resources to provide personalized, integrated and comprehensive logistics services for the client. The Logistics Service Supply Chain is an important trend of the contemporary development of the logistics industry, and has received a wide attention. But many key issues are far from being fully researched and resolved.From the point of view of the integrated logistics services suppliers, this article researches supplier selection and task allocation in the environment of Logistics Service Supply Chain.In the part of the logistics service provider selection, the model of supplier selection based on the Logistics Service Supply Chain is constructed based on the law of entropy and AHP. Firstly,the basic process of the functional logistics service providers selection is given. And then we take into account the logistics service ability, the logistics service quality, corporate quality, and logistics service costs and other factors, we build a comprehensive evaluation index system of logistics service suppliers. Take into account the traditional evaluation methods are too subjective, this article introduces the concept of entropy weight, and then combine the subjective weight and the objective weight to assess the logistics service providers more objectively,and verify the validity of the model through a numerical example. Finally, calculate the weights of the four suppliers to pave the way for the task allocation content.In the part of the task allocation, The model of logistics service task allocation is given based on multi-objective programming theory,and the decision model jointly considers service price,service quality and supplier satisfaction under the environment of supply chain. In order to enable the decision-maker to assign different weights to various criteria, the method of fuzzy-decision making is applied. And a numerical example shows the impact of decision preference, supplier satisfaction and other correlation factors.