Yazar "Mahdavi, Iraj" seçeneğine göre listele

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    A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem
    (Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Aydin, Nadi Serhan; Mahdavi, Iraj
    This study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on bestworst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multiobjective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.
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    A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries
    (Springer, 2021) Alinezhad, Masoud; Mahdavi, Iraj; Hematian, Milad; Tirkolaee, Erfan Babaee
    Nowadays, the intensifcation of a competitive environment in markets in conjunction with sustainability issues has forced organizations to concentrate on designing sustainable closed-loop supply chains. In this study, a sustainable closed-loop supply chain network is confgured under uncertain conditions based on fuzzy theory. The proposed network is a multi-product multi-period problem which is formulated by a bi-objective mixed-integer linear programming model with fuzzy demand and return rate. The objectives are to max imize the supply chain proft and customer satisfaction at the same time. Moreover, the carbon footprint is included in the frst objective function in terms of cost (tax) to afect the total proft and treat the environmental aspect. Fuzzy linear programming and Lp metric method are then applied to deal with the uncertainty and bi-objectiveness of the model, respectively. In order to validate the methodology, a case study problem in the dairy industry is investigated where the proposed Lp-metric is also compared to goal attainment method. The obtained results demonstrate the superiority of Lp-metric against goal attain ment method as well as the applicability and efciency of the proposed methodology to treat a real case study problem. Furthermore, from the management perspective, outsourc ing the production during high-demand periods is highly recommended as an efcient solution.
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    A hybrid biobjective markov chain based optimization model for sustainable aggregate production planning
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Tirkolaee, Erfan Babaee; Aydın, Nadi Serhan; Mahdavi, Iraj
    This research addresses the sustainable aggregate production planning problem by considering the outsourcing option and workforce skill levels as well as taking a Markov process approach for the inventory level. For this purpose, a hybrid biobjective mixed-integer nonlinear programming model featuring a continuous-time Markov chain to accommodate the inventory decision process is developed. The proposed Markov chain approach efficiently describes system dynamics modeling of the production system through a stochastic process. The objective functions are to minimize total cost and total environmental pollution at the same time. To validate the applicability of the methodology and to evaluate the model complexity, three numerical examples are generated based on one of the previous studies in the literature. It is demonstrated that the suggested methodology is able to come up with the final feasible solution based on optimal inventory decisions in less than 65 s. Finally, a number of sensitivity analyses are presented to study the behavior of the objectives under real-world instability and discuss the practical implications and managerial insights. As one of the main findings, it is revealed that the objective functions have no sensitivity to some change intervals of the parameters, which can be analyzed more earnestly by the management in case of the resource allocation process.
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    A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation
    (Elsevier Ltd, 2022) Amirteimoori, Arash; Mahdavi, Iraj; Solimanpur, Maghsud; Ali, Sadia Samar; Tirkolaee, Erfan Babaee
    In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.