库存与运输整合问题的多种算法比较

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 control and transportation scheduling. Then, to overcome the computationally inefficiency for large-scale problem by the Lagrange multiplier method, a scenario-based dynamic slope scaling procedure (DSSP) heuristic 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.