Collaborative Optimization Model And Intelligent Algorithm Of The Location–Inventory–Delivery Problem With Joint Replenishment Policy

It is helpful to reduce the total cost by purchasing in the way of large quantities and less specifications for a cross-border e-commerce company, which would contradict with the small size and high diversification in consumption. To alleviate this contradiction, i.e., not only reduce the cost effectively but also meet the demands of the consumers quickly, establishing distribution centers(DCs) and maintaining a reasonable inventory is particularly important. Joint replenishment(JR) policy is effective for cost control, and location, inventory, and delivery(LID) are key decisions for the construction and operations of DCs. An integrated JR–LID model is more practical, but finding an efficient, stable and effective algorithm is very challenging.This thesis presents an improved fruit fly optimization algorithm(IFOA) to solve the LID with JR policy. In the proposed IFOA, fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Secondly, the IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm. At last, this thesis studies a JR-LID model in a three-echelon supply chain network. In this model, the DCs can adopt JR policy which can result in considerable cost savings. The IFOA is utilized to find the number of DCs, locations, replenishment and delivery plan. Results of an example and sensitivity analysis provide useful managerial insights for the operations management of DCs. Moreover, the efficiency and stability of IFOA were verified through solving the extension examples.