Benders Decomposition Approach on Adjustable Robust Counterpart Optimization Model for Multi-objective Supply Chain Problems in Sugar Distribution

This paper discusses the Benders decomposition approach on the multi-objective adjustable robust counterpart optimization model with polyhedral uncertainty set for sugar distribution supply chain problems. It focused on the optimization modeling of sugar distribution among producers, local food hubs, and consumers in sub-districts. This problem is considered a multi-objective mixed integer linear programming problem with two objective functions: to maximize demand fulfillment and minimize logistics costs. Uncertain data was collected from real-life scenarios and analyzed using the adjustable robust counterpart methodology with polyhedral uncertainty set assumption. The uncertain data consisted of the adjustable and non-adjustable variables. This research was carried out in northern Bandung City, Indonesia. The result shows that the evaluation of the two-stage supply chain optimization model with adjustable robust counterpart methodology is effectively solved using the benders decomposition approach.