arXiv subjects

Hamed Samarghandi

Publications and source records attributed to Hamed Samarghandi.

4 recordsLinked to original sources

arXiv2026

Integrating Hydrogen into Ontario's Energy Hub: A Robust, Carbon-Aware Framework for Power-Heat-Transport

Decarbonizing electricity generation, heating, and transportation simultaneously requires integrated planning tools that can coordinate multiple energy production sources and demand points while remaining reliable under uncertainty. This paper develops a carbon-aware and uncertainty-resilient optimization framework for a grid-connected multiple source hub that co-optimizes electricity, heating, cooling, and transport energy services with an explicit hydrogen sub-hub. The proposed model is formulated as an MILP over a 25-year planning horizon (2025-2050). The hub integrates renewable electricity (Photovoltaic and wind), dispatchable resources (including natural-gas-based conversion), storage systems, demand response, and a hydrogen subsystem comprising an electrolyzer and hydrogen storage to supply hydrogen-vehicle demand and provide temporal flexibility. Two policy archetypes are examined: a Carbon Tax (price instrument), and a Net-Zero pathway (quantity instrument). To hedge feasibility-critical operational uncertainty, the deterministic model is extended using budgeted robust optimization and a tunable uncertainty budget. The developed scheme is applied to the province of Ontario, Canada; the results indicate substantial long-term hydrogen expansion, with the electrolyzer capacity increasing from 300~MW (2025) to 3,800~MW (2050) and hydrogen storage from 2,000~MWh to 37,000~MWh (2050), accompanied by sharply higher hydrogen production. Compared with deterministic solutions, robust solutions preserve feasibility under adverse realizations but incur a moderate robustness premium of approximately 6.6-9.0\% in total cost across the policy cases studied, while slightly reducing hydrogen utilization and renewable share and increasing reliance on dispatchable balancing.

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arXiv2023

Increasing Supply Chain Resiliency Through Equilibrium Pricing and Stipulating Transportation Quota Regulation

Supply chain disruption can occur for a variety of reasons, including natural disasters or market dynamics for which resilient strategies should be designed. If the disruption is profound and with dire consequences for the economy, it calls for the regulator's intervention to minimize the impact for the betterment of the society. This paper considers a shipping company with limited capacity which will ship a group of products with heterogeneous transportation and production costs and prices, and investigates the minimum quota regulation on transportation amounts stipulated by the government. An interesting example can happen in North American rail transportation market, where the rail capacity is used for a variety of products and commodities such as oil and grains. Similarly, in Europe supply chain of grains produced in Ukraine is disrupted by the Ukraine war and the blockade of sea transportation routes, which puts pressure on rail transportation capacity of Ukraine and its neighboring countries to the west that needs to be shared for shipping a variety of products including grains, military, and humanitarian supplies. Such situations require a proper execution of government intervention for effective management of the limited transportation capacity to avoid the rippling effects throughout the economy. We propose mathematical models and solutions for the market players and the government in a Canadian case study. Subsequently, the conditions that justify government intervention are identified, and an algorithm to obtain the optimum minimum quotas is presented.

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arXiv2023

On designing a resilient green supply chain to mitigate ripple effect: a two-stage stochastic optimization model

Disasters and disruptions such as the COVID-19 pandemic can significantly interrupt supply chains and industries. To control these disruptions, decision-makers must focus on supply chain resiliency. This paper proposes a multi-stage, multi-period green supply chain design model and six resilience strategies, with downstream and upstream disruptions taken into account to analyze both the ripple and bullwhip effect, respectively. To control the mentioned disruptions and handle the uncertainties of parameter estimations, a two-stage stochastic optimization approach is devised. The objectives are to minimize the total cost of disruption, and $CO_{2}$ emission under the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by sensitivity analysis. The results show the optimum structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decline in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap, and supply chain managers to predict the demand behaviour of essential and non-essential products in the event of disruptions.

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arXiv2022

A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is one of the most important problems in GSCM, which satisfies a firm's environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve the described problem, a multi-objective robust optimization (RO) model, as an effective approach to handle uncertainty, is proposed. A numerical example using randomly generated data, accompanied by the analysis based on the proposed approach is elaborated to validate the presented model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that allowance amount (cap) and allowance prices in the cap-and-trade system impact the firms' costs and the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system.

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