基于收益共享契约的网购供应链库存与运输动态联合优化研究

项目名称： 基于收益共享契约的网购供应链库存与运输动态联合优化研究

项目编号： No.71262031

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 李富昌

作者单位： 云南师范大学

项目金额： 37万元

中文摘要： 在网购快速发展、网购成本管理急需完善的大背景下，以收益共享为主要研究视角和分析工具，探讨网购供应链ITIO合作机制和演进规律。在单周期、多周期、滚动周期、有限理性和复杂网络系统条件下，研究网购企业、供应商和TPL基于收益共享契约的库存和运输整合优化途径和策略选择。首先，设计"DEA聚类-嵌套排序-ITIO策略"分析方法，研究收益共享契约纳入网购供应链ITIO的途径，并引入动态分析框架，揭示库存策略、运输策略和收益共享安排的动态变化过程和规律；其次，引入有限理性的演化博弈方法，建立刻画网购供应链收益共享的ITIO演化博弈模型，研究不同初始条件下的均衡实现路径；最后，使用复杂网络上的演化博弈方法，研究网购供应链收益共享契约下ITIO的囚徒困境演化进程，探究演化博弈的稳定均衡。项目研究既为网购供应链成员ITIO合作机制的完善提供决策依据，又从理论上探究网购供应链库存和运输成本节约的契约安排。

中文关键词： 收益共享契约；库存与运输联合优化；网购供应链；演化博弈；动态优化

英文摘要： As online shopping develop rapidly, its cost management urgently need to improve，this project intend to use revenue sharing contract as the main research perspective and analysis tool to investigate the cooperation mechanism and evolution law of ITIO for online shopping supply chain. Under the conditions of single cycle, multi-cycle, bounded rationality and complex network system, we study online shopping enterprise, supplier and TPL's ITIO optimization ways and strategy selection based on revenue sharing contract. First of all, we design "DEA clustering - Nested Sorting-ITIO strategy" analytical method to study the way of introducing revenue sharing contract to ITIO for online shopping supply chain, and use dynamic analytical framework to reveal the dynamic process and law of inventory policy, transportation strategy and revenue sharing arrangement. Secondly, introduce evolutionary game analysis methods with bounded rationality to develop ITIO evolutionary game model to portray revenue sharing of online shopping supply chain, and study the equilibrium achieving path under different initial conditions. Finally, use evolutionary game approach of complex network to study ITIO Prisoner's Dilemma evolution process of online shopping supply chain based on revenue sharing contract, and explore the Evolutionary Stable

英文关键词： Revenue Sharing Contract；Inventory-Transportation Integrated Optimization；Online Shopping Supply Chain；Evolutionary Game；Dynamic Optimization