A Robust Optimizing Reverse Logistics Model for Beef Products Using Multi Depot Vehicle Routing Problem

Abstract

Beef is a perishable product and requires special handling. Demand for beef also fluctuates quite high and is heavily influenced by various religious events and traditions in Indonesia. Under these conditions, for various reasons, beef products are returned from customers to distributors. An increase in the number of products returned from customers leads to high costs and the risk of product damage. The research created an optimization model for product distribution and product recall from customers with minimal costs and risks. The research applied a clustering method using the Density-Based Spatial Clustering (DBSCAN) algorithm to determine the density of customers’ locations and the number of orders. Optimization of distance and distribution and withdrawal costs applied Multi Depot Vehicle Routing Problem (MDVRP) and Mixed Integer Linear Programming (MILP) mathematical modeling. The results indicate three customer clusters with one noise, with the most potential customers in cluster 1. From this condition, product delivery optimization is based on the distance and number of shipments from the two central warehouses. Optimization uses of MDVRP and MILP to model and make company-owned trucks more profitable at high rental truck replacement costs. The research produces a robust model for changes in the truck number and capacity based on sensitivity analysis.