Supply chain distribution network system provides an optimal platform for efficient and effective supply chain management. There are trade-offs between demand time efficiency and supply cost effectiveness. In this dissertation, an integrated two-echelon distribution network system consisting of one supplier, multiple distribution centers, and multiple customer zones is formulated under a vendor managed inventory (VMI) setup which simply assumes the vendor (supplier) manages the inventory of the customers and stores them at different distribution centers. The system also integrates the effects of facility location, distribution, and inventory issues and includes conflicting objectives such as cost (for effectiveness), volume fill rate and responsiveness level (for efficiency). With these considerations, we present a Multi-Objective Location-Inventory Problem (MOLIP) which results in a Mixed-Integer Non-Linear Programming (MINLP) formulation.
The MOLIP model consists of two steps. The first step makes the strategic decisions to determine the optimal number, sites and capacity of opening distribution centers (DCs) to be used, as well as the establishment of distribution channels and the amount of products to distribute from the supplier to assigned buyers via DCs. In the second step, the model in turn determines the inventory levels and safety stocks, economic order quantities of different facilities in the tactical level. However, the model is difficult to solve with existing optimization algorithms due to the considerable number of decision variables and constraints resulting from the integration. To obtain feasible and satisfactory solutions to the integrated MOLIP model, a hybrid multi-objective evolutionary approach is presented which is preliminarily based on a well-known NSGA-II evolutionary algorithm with a non-dominated sorting mechanism and an elitism strategy. To facilitate the genetic search and improve the search results, a heuristic method is designed to generate a well-adapted initial population.
To investigate the possibility of the proposed evolutionary approach for MOLIP model, we implemented on three experiments. First, an experimental study using practical data was then illustrated for the efficacy of the proposed approach. The hybrid approach has been successfully applied for providing promising solutions on a base-case problem with 50 buyers and 15 potential DCs. Computational analyses has presented a promise solution in solving such a practical-size problem.
Second, we implemented several scenario analyses to understand the model performance and to illustrate how parameter changes influences its output. The scenario analysis illustrates that excess capacity in the supply chain network design is beneficial for volume fill rate and responsiveness level and has only little expense of total costs. In additions, the results of the scenario analyses implied that the distribution network flexibility and competitiveness level sought by the supply chain managers is warranted. The model proposed in this research is helpful in adjusting the distribution network to these changes.
Finally, we tested and compared our NSGAII-based algorithm with the one based on the improved Strength Pareto Evolutionary Algorithm (SPEA2) by developing a test set of random problem instances of the MOLIP model to understand the efficiency between two approaches. In these test instances, two algorithms obtained similar approximations of their Pareto frontiers but NSGAII algorithm outperformed in terms of the diversity quality of the approximation to the Pareto frontier. However, the SPEA2-based algorithm was more efficient in terms of execution time in small or tight capacity instances. This suggested that the propose hybrid algorithm can be an efficient approach for providing feasible and satisfactory solutions to large-scale difficult-to-solve problems.