| 摘要: | 研究期間：10308~10407;Faced with rapid changes and a competitive environment, in order for enterprises to maintain their competitiveness, they must ensure a stable supply of products to satisfy fluctuations in market demand. If a good replenishment strategy is built with the Vendor Management Inventory (VMI), it will not only lead to efficient inventory management, but will also reduce the overall system cost and increase operation efficiency. In practice, inventory management is carried out with related computer-aided inventory management software. However, the inventory level is controlled manually by experienced planning personnel. Consequently, the planning results may not reflect overall optimality. Many studies related to inventory management problems have been carried out in the past, involving a wide range of applications. VMI problems have also been discussed in literature. However, to simplify the studied problems, a suitable inventory level is generally set up in advance. Consequently, the proposed models or methods cannot use the overall resources effectively without system optimization. Besides, in the past fixed average travel times have been utilized with regard to the vehicle routing problem, meaning that stochastic disturbances arising from variations in vehicle travel times in actual operations were neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during daily operations, the original planned schedule could be disturbed enough to lose its optimality. Therefore, in this study, based on the system optimization perspective, we develop a deterministic-demand upper-and-lower inventory level control model, a stochastic-demand upper-and-lower inventory level control model, and a daily vehicle delivery scheduling model under stochastic travel times, with the objective of minimizing the total cost. The models are expected to be useful tools for the planner to decide on an optimal upper-and-lower inventory level and a vehicle delivery schedule. After a preliminary evaluation, the scope of this research is expected to be large, so we propose a three-year project. In the first year, we will construct a deterministic-demand upper-and-lower inventory level control model for one supplier to many retailers, considering deterministic daily demands. In the second year, we will construct a stochastic-demand upper-and-lower inventory level control model for one supplier to many retailers, considering stochastic daily demands. In the third year, we will construct a daily vehicle delivery scheduling model under stochastic travel times for one supplier to many retailers. We will employ the mathematical programming and network flow techniques to develop all the models with suitable objective functions and constraints based on the problem characteristics to comply with real operating requirements. All the models are expected to be formulated as integer network flow problems with side constraints or integer multiple commodity network flow problems, which are characterized as NP-hard so cannot be optimally solved within a reasonable time for large-scale problems. To efficiently solve the realistically large problems that occur in practice, we will develop a solution algorithm for each model by adopting a problem decomposition strategy, coupled with the use of the CPLEX software. Finally, to evaluate the models and the solution algorithms in practice, we will perform case studies and sensitivity analyses. Conclusions and suggestions will then be given. |
