نوع مقاله : پژوهشی
تازه های تحقیق
عنوان مقاله English
نویسندگان English
n this paper, an integer linear programming model is proposed for designing a pharmaceutical logistics network in two phases of disaster preparedness and response. In the preparedness phase, strategic decisions including location of central, local, and backup warehouses, capacity determination, perishable drug inventory management, and fleet allocation are made. In the response phase, temporary warehouses are activated considering the probability of route failures, and routing and distribution are performed dynamically over multiple periods. The proposed model pursues three objectives: minimizing service time, minimizing facility costs, and maximizing supply chain reliability. A scenario-based robust optimization approach is applied to model uncertainties in parameters such as demand and route statuses. The results show that the model is capable of fully allocating central warehouse inventory and preventing shortages under the highest demand scenario. The high allocation of fleet to the backup warehouse demonstrates the central role of this facility in network resilience. Sensitivity analysis also indicates that investment in warehouse fortification not only increases reliability but also significantly reduces drug purchasing and holding costs. Due to the NP-hard nature of the problem, the model is solved using GAMS for small-scale problems and the Non-dominated Sorting Genetic Algorithm (NSGA-II) for large-scale problems. Comparison of results shows that the metaheuristic algorithm provides acceptable quality Pareto solutions in dimensions where the exact method fails to solve. Conducting a real case study with actual data is suggested as an essential step for future research.
The results show that the model is capable of fully allocating central warehouse inventory and preventing shortages under the highest demand scenario. The high allocation of fleet to the backup warehouse demonstrates the central role of this facility in network resilience. Sensitivity analysis also indicates that investment in warehouse fortification not only increases reliability but also significantly reduces drug purchasing and holding costs. Due to the NP-hard nature of the problem, the model is solved using GAMS for small-scale problems and the Non-dominated Sorting Genetic Algorithm (NSGA-II) for large-scale problems. Comparison of results shows that the metaheuristic algorithm provides acceptable quality Pareto solutions in dimensions where the exact method fails to solve. Conducting a real case study with actual data is suggested as an essential step for future research.
کلیدواژهها English
Copyright © Hamid Moakedi, Fatemeh Ghasemizadeh
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