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Author: search.filters.author.Mohammad KhalilzadehSubject: search.filters.subject.Uncertainty

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    A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment
    (Growing Science, 2020-01-01)
    Project scheduling models with resource constraints and multi-mode activities aims to create a schedule for carrying out activities considering precedence constraints and available resources in order to minimize the project duration. In the real world, we face uncertainty related to projects, where there are no historical data, hence, we should rely on the experts' judgements to estimate activity durations. For this purpose, in this paper, the 99-simulation method is used to deal with uncertainty. The exact mathematical programming model is presented in this paper and the hybrid algorithm based on Genetic Algorithm is used to solve this type of project scheduling problem which finds the near-optimal solution in a short computational time. Finally, the effectiveness of the proposed model is examined with a numerical example.
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    A multi-objective model for cooperative delivery of customer orders using multiple trucks and UAVs considering weather conditions
    (Elsevier BV, 2024-12-16)
    The increasing demand for fast, eco-friendly delivery services driven by e-commerce growth has led to innovative logistics solutions. Hybrid delivery systems combining trucks and Unmanned Aerial Vehicles (UAVs) are emerging as innovative approaches to meet these demands. This study develops a comprehensive mathematical model to optimize such systems, addressing key challenges such as UAV limitations (short range, cargo weight, and energy constraints) and the influence of weather conditions (wind speed, wind direction , and temperature). A significant contribution of this work is the simultaneous consideration of weather factors on both truck energy consumption and UAV flight performance , enabling realistic and adaptive logistics planning. In the proposed system, UAVs operate alongside trucks, returning for recharging after completing their assigned deliveries, which enhances operational feasibility. The model is designed to minimize two objectives: delivery time and cost. Small problem instances are solved using CPLEX solver for validation, while larger instances are tackled using NSGA-II and MOPSO meta-heuristic algorithms. Sensitivity analyses further explore the impact of weather parameters on system performance , offering valuable insights into its adaptability under uncertain conditions. Results demonstrate the model's effectiveness and the computational efficiency of the algorithms in handling complex, real-world scenarios, contributing to sustainable and intelligent logistics solutions.
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    A fuzzy bi-objective mathematical model for perishable medical goods supply chain network considering crisis situations: An empirical study
    (SAGE Publications Ltd, 2024-01-01)
    In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment. In this paper, a three-level supply chain including suppliers, intermediate warehouses, and final customers is developed for perishable medical items. The uncertainty of customer demand for service and the spent time in the intermediate warehouses are considered using the exponential distribution functions. Also, it is assumed that the life-cycle of perishable medical goods follow the Weibull distribution function. The model attempts to minimize the total costs of the supply chain and total presence time of perishable items in the whole chain. The LP-Metric method is employed for solving small-sized problems. Due to the NP-Hardness of the problem, the modified Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are utilized as 2 well-known and efficient meta-heuristic algorithms for solving large-sized problems. The findings indicate that the meta-heuristic algorithms are efficient in achieving close to the optimal solution for large-size problems in a reasonable time. Also, the results demonstrate that NSGA-II outperforms MOPSO in terms of the high quality solution. Finally, the applicability of the model to real-world problems is demonstrated using a real case study. This paper can assist the planners and decision-makers of perishable drugs supply chain networks in crisis conditions with on-time supplying and distributing the required emergency items.