Search Results (6)

This study addresses the sustainable supply chain network design (SSCND) problem by integrating economic and environmental dimensions through a multi-objective, multi-echelon stochastic mathematical model. The proposed model focuses on simultaneously optimizing total cost, carbon emissions, water footprint, and renewable energy utilization. Strategic solar energy investment alongside facility location and sizing decisions are considered under uncertain conditions. Initially, a multi-product stochastic model is developed and solved utilizing the augmented epsilon constraint (AUGMECON2) method to obtain Pareto-optimal solutions for small-scale instances. For validation purposes, the exact solutions obtained using AUGMECON2 were used as the benchmark for the small-scale instance, while the proposed hybrid NSGA-II algorithm generated near-optimal solutions with deviations of 0.30%, 1.53%, 0.03%, and 0.0006% for total cost, carbon emissions, renewable energy use, and water footprint, respectively. Compared with the cost-oriented solution, the renewable energy-focused solution increased total cost by 76.33% while reducing the water footprint by 6.36% and carbon emissions by 3.57%. For medium- and large-scale instances, where exact solutions became computationally impractical, the hybrid NSGA-II algorithm remained applicable and generated feasible Pareto solutions within 59.05 s and 309.62 s, respectively. Overall, the presented framework provides a scalable decision-support tool for sustainable supply chain planning under uncertainty. Full article

This paper proposes a multi-objective optimal scheduling model for integrated energy systems (IESs) that incorporates a tiered carbon emissions trading mechanism and load-side demand response (LDR) to promote sustainability. First, a reward–penalty-based tiered carbon cost model is embedded within the IES scheduling framework, internalizing carbon constraints and providing differentiated carbon price signals for emission reduction. Second, a refined demand response model is introduced, categorizing electrical and thermal loads to enhance flexibility in system operation. The demand response strategy allows for temporal load shifting and load reduction, optimizing the overall energy management. Third, the augmented epsilon-constraint method (AUGMECON) is employed to minimize both total operating costs and carbon emissions. Scenario-based simulations are conducted to evaluate system performance under different configurations: the integrated carbon trading and LDR model, a carbon-trading-only approach, and a baseline scenario. The results show that the proposed model achieves the best performance, reducing operating costs by 13.6% and carbon emissions by 7.0% compared to the baseline. Additionally, the combined approach improves renewable energy utilization and reduces reliance on high-carbon energy sources, demonstrating the effectiveness of integrating carbon trading and demand response strategies for low-carbon and sustainable energy system management. Full article

The multi-objective sustainable closed-loop supply chain network studied involves characteristics that produce high complexity due to the interaction of downstream and upstream strategic, tactical, and operational decisions, as well as sustainability elements. For this reason, a matheuristic algorithm, the Kernel search, is presented to solve large instances of the problem. After the algorithm parameter tuning, several instances are solved. A comparison with an augmented epsilon-constraint method is conducted in terms of speed and quality. The results show that the Kernel search matheuristic outperforms in the selected metrics, achieving an average improvement of 72% in computational time and from 0.47% to 28.18% in quality metrics. The solutions obtained deliver Pareto fronts in terms of economic, environmental, and social objectives. Full article

Background: Each disaster has its specific resource requirements, varying based on its size, location, and the affected region’s socio-economic level. Pre-disaster planning and post-disaster dynamic resource allocation including material and human resources is essential. Methods: To address the resource allocation challenges in disaster response, a multi-objective two-stage stochastic programming model is developed for search and rescue and first aid activities. The model aims to minimize the total unmet human demand, the number of resources transferred between regions, and the total unmet material demand. The proposed model was solved for a real case of an expected earthquake in Istanbul’s Kartal district. The augmented epsilon constraint 2 algorithm was employed using the CPLEX solver. A sensitivity analysis was made. Results: Most of the unmet demand occurs in the first period. After that period, the unmet demand decreases with interregional transfers and additional resources. The model is robust to scenario probability and penalty value changes in the objectives. Conclusions: This is the first study that simultaneously and dynamically allocates renewable and non-renewable material resources and human resources, including the official rescue units and volunteers, for disaster response. Volunteers’ inclusion in teams considering their training and quitting behavior are unique aspects of the study. Full article

Having sustainable and flexible features is crucial for manufacturing companies considering the increasing competition in the globalized world. This study considers three aspects of sustainability, namely economic, social, and environmental factors, in the design of flexible manufacturing cells. Three different multi-objective integer mathematical programming models were developed with the objective of minimizing the costs associated with carbon emissions, inter-cellular movements, machine processing, machine replacement, worker training, and additional salary (bonus). Simultaneously, these models aim to minimize the carbon emission amount of the cells within the environmental dimension scope. The developed models are a goal programming model, an epsilon constraint method, and an augmented epsilon constraint (AUGMECON) method. In these models, alternative routes of parts are considered while assigning parts to machines. The results are obtained using the LINGO 20.0 optimization program with a developed illustrative example. The obtained results are tested and compared by performing sensitivity analyses. The sensitivity analyses include examinations of the effects of changes in part demands, machine capacity values, carbon limit value, and the maximum number of workers in cells. Full article

International laws and increasing consumer awareness have led to drastic changes in traditional supply chain network designs. Moreover, because of environmental and social requirements, traditional supply chain networks have changed to sustainable supply chain networks. On the other hand, reverse logistics can be effective in terms of environmental and economic aspects, so the design of the supply chain network as a closed loop is necessary. In addition, customers have a demand for different products with different quality levels. Considering different types of customers with a variety of consumption trends can be a challenging issue, and is addressed in this study. The main contributions of this research are considering different quality levels for products as well as different tendencies of customers towards environmental issues. In this study, a sustainable closed-loop supply chain model is designed that seeks to balance economic, environmental, and social responsibilities. In this paper, costs and customer demands for different types of products at different quality levels are considered under uncertain conditions using a robust possibilistic programming method. The proposed multi-objective model is solved using the Augmented Epsilon Constraint (AEC) method that provides an efficient set of solutions for all decision-making levels. The results show that the robust possibilistic programming method is more effective in dealing with uncertainties than the possibilistic programming method. Full article