A Scenario-Based Multi-Objective Optimization Model for Resilient Internal Transportation in Steel Supply Chains: A Case Study of Sirjan Jahan Steel Company

Abstract

Efficient internal transportation, weightbridge allocation, and stockpile management are critical yet challenging in steel manufacturing due to high material flow volumes. This study develops a scenario-based multi-objective mixed-integer programming model tailored to Sirjan Jahan Steel Company to determine optimal stockpile roles for pellets, sponge iron, and billets, alongside material flow allocation and weightbridge capacity expansion, while evaluating system resilience under five discrete disruption scenarios, including conveyor failures and supply interruptions. The first objective minimizes total operational costs, covering internal haulage, conveyor systems, stockpile setup, weighing infrastructure, holding, and shortage penalties. The second objective minimizes total truck turnaround time, encompassing queuing, service, and on-site travel, using an M/M/c queuing model to capture weighbridge and loading/unloading bottlenecks. The augmented ε-constraint method generates the Pareto frontier, with TOPSIS applied for final solution selection. The optimal plan activates stockpiles 1, 2, 3, and 12 for pellets; stockpiles 5, 6, and 7 for sponge iron; and stockpile 4 for billets, with two new weighbridges in the pellet section and two in the outbound section. The plan maintains a resilient pellet inventory of 99,611 tons to mitigate severe disruption scenarios. Compared to the current baseline—incorporating a 200,000-ton-per-month conveyor and penalty costs—the model reduces expected annual costs from $6.16M to $5.30M, achieving an annual saving of $0.85M (13.87%). The optimal plan yields average turnaround times of 46.5 and 71 minutes per truck for sponge iron and billet, respectively.