Advanced Quantitative Techniques to Solve Center of Gravity Problem in Supply Chain

TL;DR

This paper introduces an advanced optimization tool using MILP and novel heuristics to efficiently identify cost-effective warehouse locations in supply chain networks, reducing overall logistics costs.

Contribution

It presents a new optimization model with heuristics and a user interface to improve warehouse location selection in supply chains, enhancing existing methods.

Findings

The model reduces logistics costs compared to existing networks.

It achieves low computational cost and runtime.

The tool supports user-friendly interaction with the supply chain design process.

Abstract

Activities involving transformation of raw materials, various resources and components into final products and also delivering it to the end customer incur a significant cost during the selection of location of a warehouse that can be easily accessed by various actors of the supply chain. To minimize upstream and downstream transportation costs, the center of gravity (CoG) analysis method is used to find the potential warehouse locations for a given demand network which have an impact on the entire supply chain network. Mixed Integer Linear Programming (MILP), an open source tool is developed for implementing CoG method along with certain service level constraints to find optimal potential locations with the least cost. In this paper, an optimization tool has been designed for a forward logistics network with several novel methods like Customer Location Selection (CLS), Customer Packets along with other business heuristics that optimize and enhance the existing MILP to get the optimal solutions with low computational cost and runtime. Finally, recommending an alternative network of facilities which reduces overall costs compared to the existing network. An user interface has also been developed to make a user friendly interaction with the model. We can conclude that this model can significantly help companies reduce costs during the logistics network design.