库存与运输整合问题的多种算法比较  

A Comparative Study on Algorithms for Inventory-Transportation Integrated Optimization Problem

作　　者：裴英梅[1,2] 叶春明[1] 左翠红[2] 刘立辉[3] 

机构地区：[1]上海理工大学管理学院,上海200093 [2]威海职业学院信息工程系,山东威海264210 [3]鲁东大学交通学院,山东烟台264025

出　　处：《工业工程》2013年第1期105-109,共5页Industrial Engineering Journal

基　　金：教育部人文社会科学规划基金资助项目(10YJA630187);高等学校博士点基金资助项目(20093120110008);上海市重点学科建设资助项目(S30504);上海市研究生教育创新计划资助项目(JWCXSL1021);鲁东大学校基金资助项目(LY2011008)

摘　　要：通过循序渐进地应用拉格朗日乘数法、基于样本的DSSP(Dynamic Slope Scaling Procedure)启发法和基于拉格朗日松弛模型的DSSP启发法等几种算法,分别求解多对多配送系统中的库存与运输整合优化问题,逐渐找到了解决问题的更加有效的方法———基于拉格朗日松弛模型的DSSP启发法。通过比较实验证明了此法在解决库存与运输整合优化问题时能在更少的计算时间里获得更优化的解。The inventory-transportation integrated optimization （ITIO） problem in a distribution network with multiple warehouses and multiple retailers is addressed. For solving this problem, different algorithms are explored. First, a Lagrange multiplier method is used to solve the integrated problem of inventory con-trol and transportation scheduling. Then, to overcome the computationally inefficiency for large-scale prob-lem by the Lagrange multiplier method, a scenario-based dynamic slope scaling procedure （DSSP） heuris-tic is proposed to establish an ITIO model. Lastly, to improve the solution accuracy of the heuristic, the Lagrangian relaxation-based DSSP heuristic is applied to solve the ITIO problem. Comparison is done for problems in a many-to-many distribution network. Results show that the Lagrangian relaxation-based DSSP heuristic outperforms the others in both solution accuracy and computational efficiency.

关 键 词：库存与运输 整合优化 基于拉格朗日松弛模型的DSSP启发法 

分 类 号：F253[经济管理—国民经济]