🤖 AI Summary
This paper addresses the four-level supply chain facility location problem (4L-FLP), jointly optimizing hierarchical location decisions for markets, factories, warehouses, and distribution centers to maximize total profit under capacity constraints, fixed construction costs, and multilevel transportation costs. We propose a novel integer programming model featuring significantly reduced variable count—first unifying multi-tier facility selection, tier-wise transportation cost modeling, and one-time capital investment representation—enabling scalable solution of large-scale instances. An efficient hybrid heuristic integrates multi-start greedy initialization with tabu search to ensure rapid convergence. Sensitivity analysis confirms solution robustness across key parameters. The approach delivers a scalable, production-ready optimization framework for strategic supply chain network design, substantially advancing the maturity of supply chain planning methodologies.
📝 Abstract
We attack the 4-level facility location problem (4L-FLP), a critical component in supply chains. Foundational tasks here involve selecting markets, plants, warehouses, and distribution centers to maximize profits while considering related constraints. Based on a variation of the quadratic assignment problem, we propose a novel integer programming formula that significantly reduces the variables. Our model incorporates several realistic features, including transportation costs and upper bounds on facilities at each level. It accounts for one-time fixed costs associated with selecting each facility. To solve this complex problem, we develop and experimentally test two solution procedures: a multi-start greedy heuristic and a multi-start tabu search. We conduct extensive sensitivity analyses on the results to assess the reliability of proposed algorithms. This study contributes to improved solution methods for large-scale 4L-FLPs, providing a valuable tool for supply chain maturity.