نوع مقاله : پژوهشی
تازه های تحقیق
عنوان مقاله English
نویسندگان English
This study addresses eco-economic multi-objective modeling to optimize cross-docking operations in the steel supply chain. Moving beyond conventional literature that often relies on homogeneous fleets and single-product assumptions, a key innovation here is the incorporation of product and vehicle diversity through specific indexed modeling, which captures complex, real-world industrial constraints. The model is solved using an efficient hybrid metaheuristic approach, where NSGA-II performs global exploration while Simulated Annealing (SA) is embedded as a local search operator to refine solutions within each generation. Computational experiments demonstrate a zero percent optimality gap in small dimensions, with the hybrid approach maintaining high efficiency for large-scale industrial cases. Furthermore, sensitivity analysis using real data from the Isfahan Mobarakeh Steel Company provides actionable insights for managers: it identifies critical operational bottlenecks (loading, unloading, and internal transfer durations) and enables data-driven trade-offs between logistical makes pan and greenhouse gas emissions, establishing the model as a robust decision-support tool for sustainable heavy industry operations.
کلیدواژهها English
Copyright © Mohammadreza Aghajafar Mahallati, Mohammad Hossein Darvish Motevalli, Seyyed Mostafa Mousavi, Majid Motamedi
License
This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.
