A Multi-Objective Optimization Model for Dynamic Facility Location in Rail Supply Chain Network Design

Abstract

This study tackles dynamic facility location in rail supply chain networks, optimizing rail terminal placement to efficiently meet fluctuating freight demands. Static and dynamic networks are modeled using an iterated local search (ILS) algorithm. In static mode, ILS determines terminal numbers, locations, and client assignments. In dynamic mode, it adapts to client changes, demand variations, and network failures while maintaining survivability. Experiments across various network sizes demonstrate superior performance over CLSC and TSCFL benchmarks in cost reduction and resilience. This work integrates rail-specific dynamics, multi-level survivability under disruptions, and real-time adaptation—addressing gaps in sustainable and resilient rail network design.