Reza Babazadeh

Perishable food cold chains face significant challenges arising from product perishability, temperature-control requirements, high logistics costs, and environmental concerns. The geographical dispersion of customers and the time and temperature requirements of dairy products make it challenging to determine appropriate distribution centre locations and coordinate sourcing and inventory decisions while simultaneously achieving cost efficiency, timely delivery, and reduced carbon emissions. This study aims to optimize the cold chain distribution network of Nada Company, a dairy producer in Riyadh, Saudi Arabia, serving customers in Oman. The study employs AnyLogistix, integrating Greenfield Analysis, Network Optimization, and simulation to determine suitable distribution centre locations, evaluate network performance, and support strategic distribution network decision-making. Greenfield Analysis initially identified three potential locations in Sohar, Salalah, and Samail, while Network Optimization determined that retaining two distribution centres in Sohar and Salalah provided a more efficient configuration. Compared with the Greenfield configuration, the optimized network reduced transportation costs from $2.13 million to $1.84 million and CO₂ emissions from 949,784 to 821,524, while maintaining the same total flow and demand fulfilment. The findings demonstrate that integrating network optimization and simulation can support more efficient and sustainable cold chain network design and evidence-based distribution decisions, providing a practical decision-support framework for perishable dairy products.

Environmental concerns and the increasing scarcity of resources force decision makers in the supply chain to consider reuse and re-production. Closed loop supply chain is a fundamental concept that has attracted the attention of many researchers due to its profitability for businesses as well as its positive environmental and social effects. Closed-loop supply chains and sustainability dimensions are complementary because of their mutual effects. This paper develops a mathematical model to design a sustainable closed-loop supply chain network in the wood–plastic composite industry. Due to the nature of the problem considered, a mixed-integer linear programming method is utilized. The proposed model is a multi-objective model, and the Lp-metric method is used to solve it. The proposed model is applied in a real case in Iran. The proposed model identified 17 optimal provinces for manufacturing centers, 15 for reuse centers, and 9 for reproduction centers. Verification and validation of the proposed model illustrate its capability in real world implications.

With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.

Disposing of plastic waste through burial or burning leads to air pollution issues while also contributing to gas emissions and plastic waste spreading underground into seas via springs. Henceforth, this research aims at reducing plastic waste volume while simultaneously generating clean energy. Hydrogen energy is a promising fuel source that holds great value for humanity. However, achieving clean hydrogen energy poses challenges, including high costs and complex production processes, especially on a national scale. This research focuses on Iran as a country capable of producing this energy, examining the production process along with related challenges and the general supply chain. These challenges encompass selecting appropriate raw materials based on chosen technologies, factory capacities, storage methods, and transportation flow among different provinces of the country. To deal with these challenges, a mixed-integer linear programming model is developed to optimize the hydrogen supply chain and make optimal decisions about the mentioned problems. The supply chain model estimates an average cost—IRR 4 million (approximately USD 8)—per kilogram of hydrogen energy that is available in syngas during the initial period; however, subsequent periods may see costs decrease to IRR 1 million (approximately USD 2), factoring in return-on-investment rates.