Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 2 2026 05 01 A Scenario-Based Stochastic MILP Model for Sustainable Reconfiguration of Smart Grids in the Electricity Supply Chain under Uncertainty 293 315 11852 10.22034/ijsom.2026.111015.3556 EN Sara Mohammadi Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada Mariam Ameli Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran Hamed Davari-Ardakani Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran Journal Article 2026 02 14 Objective: This study develops a sustainable reconfiguration framework for smart grids in electricity supply chain networks under uncertainty. The research aims to integrate strategic redesign and operational decisions while simultaneously addressing economic and environmental objectives.Methods: A two-stage scenario-based stochastic mixed-integer linear programming (MILP) model is developed to minimize total network cost and greenhouse gas emissions under uncertain electricity demand and selling prices. The bi-objective model is solved using the augmented epsilon-constraint (AUGMECON) method to generate Pareto-efficient solutions. Stochastic performance measures, including the Value of the Stochastic Solution (VSS) and Expected Value of Perfect Information (EVPI), are also employed to evaluate the value of considering uncertainty. Results: The results show that explicitly incorporating uncertainty changes the trade-off between economic and environmental objectives and leads to redesign strategies that outperform deterministic solutions under different operating conditions. Electricity demand has a greater influence on network cost than electricity selling price, while the transmission and distribution loss coefficient is identified as the most influential parameter affecting economic performance. The VSS confirms the economic benefit of explicitly considering uncertainty, while the EVPI demonstrates the value of improved information for redesign decisions. Conclusion: The study integrates strategic reconfiguration decisions, including the installation and closure of generation and distributed generation facilities, with operational decisions on electricity generation, pricing, load allocation, and capacity planning within a unified stochastic framework. The proposed approach provides decision support for coordinated and sustainable smart grid reconfiguration under uncertain operating conditions. http://www.ijsom.com/article_11852_ba67e777107290283dcc9adaed9459e1.pdf