Optimization of Sourcing Decisions in Supply Chains


Sourcing models or decision-making procedures in supply chains are either ad hoc or proprietary. In this dissertation a meta-heuristic-based optimization approach is developed and applied to sourcing decisions in several different apparel supply chains. We develop a simulation-optimization methodology based on genetic algorithms (GA). A new performance measure, GMROILS, is developed. The GA with complex evaluations is used to determine optimal sourcing decisions for a seasonal item in an apparel supply chain with GMROILS as the performance measure. The proposed methodology produces very good results. In a larger experimental design number of SKUs, SKU mix error, lead-time, seasonality pattern, min. order quantity/SKU, demand volume error are included. Multivariate regression, ANOVA and neural network analysis are performed on the results and we determine that best GMROILS is achieved generally when number of reorders is maximized. Next, we develop a constrained GA with complex evaluations. First, the constrained GA is used with the same experimental design using GMROI as the objective with a 95% service level (SL) constraint. We find out that the leanest, and most flexible supply chain yields the best performance, and conclude that GMROILS is an appropriate performance measure. The constrained GA was then applied to an industry-supplied case study. The problem is to find the best inventory policy for the supplier of workpants in order to provide a 95% SL to the discount retailer chain while maximizing GMROI. We find the best combination of presentation stock levels and target week of supply for the 420 SKUs that will maximize GMROI while keeping 95% SL, and compare results to the existing policy. Lastly, a multiobjective GA with complex evaluations is developed. Multiobjective GA is used to maximize GMROI and SL at the same time for the seasonal item scenario. A pareto-optimal frontier is generated, which can be used by the decision-maker to make better and more robust decisions. The GA approach with complex evaluations and the new performance measure, GMROILS, performed successfully. Insights into optimal sourcing strategies are obtained.