نوع مقاله : پژوهشی
تازه های تحقیق
عنوان مقاله English
نویسندگان English
This study presents a sequential optimization–simulation framework for evaluating the resilience of a polypropylene-based plastic-products supply chain under a severe supply disruption. A multi-period mixed-integer linear programming (MILP) model is developed over a 24-month horizon, with the primary supplier becoming unavailable from period four in the base stress scenario. The model selects among the reconstruction of the disrupted primary supplier, the activation of a backup supplier, the pre-positioning of plant-level strategic inventory, and a combined policy consisting of backup sourcing and strategic inventory. In addition to an unrestricted run for endogenous strategy selection, the model is solved under six predefined configurations: normal operations, no recovery, seven-month reconstruction, backup supplier, strategic inventory, and combined strategy, enabling comparison of their planned performance. The strategic decisions from each configuration are then transferred to a discrete-event simulation model developed in AnyLogistix, without imposing the deterministic flows generated by the MILP model. The simulation evaluates realized performance under stochastic demand, transportation times, an ( ) inventory policy, implementation delays, and dynamic inventory behavior. Ten independent replications are conducted for each scenario, and differences in mean profit are assessed using Welch’s one-way ANOVA and the Games–Howell post hoc test. The unrestricted MILP model selects the backup supplier, yielding a planned profit of 1,134.091 billion tomans and a 100% service level. Simulation results show that the absence of a recovery strategy reduces the mean profit by 72.14% relative to normal operations. The combined strategy achieves the best financial performance among recovery policies, with a mean profit of 926.680 billion tomans, recovering 74.29% of the lost profit. These findings show that the strategy with the highest planned performance may not yield the best realized outcome; combining optimization-based policy selection with dynamic simulation therefore provides a complementary basis for comparing planned and realized performance.
کلیدواژهها English
Copyright © Ali Ashaehshoar, Mohammad Hosein Tavakkoli, Mohammad Sheikhalishahi
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.
