Yazar "Tavakkoli-Moghaddam, Reza" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • Küçük Resim Yok
    Öğe
    Designing a two-stage model for a sustainable closed-loop electric vehicle battery supply chain network: A scenario-based stochastic programming approach
    (Pergamon-Elsevier Science Ltd, 2024) Saeedi, Mehran; Parhazeh, Sina; Tavakkoli-Moghaddam, Reza; Khalili-Fard, Alireza
    Transportation is a fundamental requirement of modern life. Vehicles powered by fossil fuels are highly polluting. This study develops a two-stage stochastic programming model to establish a sustainable closed-loop supply chain for Electric Vehicle (EV) batteries. The model considers economic, environmental, and social criteria, including cost, energy consumption, carbon emissions, and job creation. The epsilon-constraint method and three multi-objective meta-heuristic algorithms are utilized to solve problems. Implementing this model in a case study of an EV battery supply chain aids managerial decision-making for optimal center establishment, flow determination, and inventory setting. Finally, essential parameters are analyzed, and several important managerial insights are prepared. The results suggest that investing in used battery collection significantly reduces costs and carbon emissions.
  • Küçük Resim Yok
    Öğe
    A hybrid machine learning model based on ensemble methods for devices fault prediction in the wood industry
    (Pergamon-Elsevier Science Ltd, 2024) Dahesh, Arezoo; Tavakkoli-Moghaddam, Reza; Wassan, Niaz; Tajally, AmirReza; Daneshi, Zahra; Erfani-Jazi, Aseman
    In manufacturing industries, including the wood industry, devices, and equipment are considered the basic elements and the main capital for production. That is why managers are trying to maintain and use these devices and equipment optimally. On the other hand, repurchasing device parts or repairing equipment in case of major damage can cause more damage than planned costs. Therefore, a model that can determine the fault class based on the signs seen in the equipment would prevent major damage to the device and save on repair costs. In this regard, using the registered features for equipment and with the help of machine learning algorithms, a model can be created that can classify devices in the appropriate class based on their observed features. The present study uses nine machine learning algorithms to make this model, trains each model on three sets of selected features, and finally compares them. It is worth mentioning that after evaluating the models, based on the features selected from the embedded techniques, permutation feature importance methods, and genetic algorithm, the best models are considered as categorical boosting with the training and testing accuracy of 0.895 and 0.909, random forest with the training and testing accuracy of 0.905 and 0.893, and extreme gradient boosting with the training and testing accuracy of 0.884 and 0.885.
  • Küçük Resim Yok
    Öğe
    On the availability and changeover cases of the general lot-sizing and scheduling problem with maintenance modelling: a Lagrangian-based heuristic approach
    (Springer Heidelberg, 2024) Alimian, Mahyar; Ghezavati, Vahidreza; Tavakkoli-Moghaddam, Reza; Ramezanian, Reza
    The present paper proposes a novel concept to integrate maintenance modelling with an integrated lot-sizing and scheduling problem. The maintenance aspect of the problem is studied as age-based maintenance, while the production section is modeled as the General Lot-sizing and Scheduling Problem. The mathematical model aims to minimize the total integrated cost of the manufacturing system by determining the sequence of the products with their optimal lot-size, inventory, and shortage levels in close relation to the specified preventive maintenance plan and the availability of the system. Based on the unique structure of the proposed model, a heuristic solution approach is developed, which includes the Lagrangian relaxation algorithm, decomposition, and valid equalities. The computational result justifies the procedure of the proposed solution method and approves its efficiency in terms of cost and solution time for the range of small to large-scale instances. Furthermore, it is discussed that not only does the integrated model decrease the total cost of the manufacturing system, but it also increases the average availability of the system and improves the feasibility of the production plan. Finally, an extended model is developed to tackle the conflicts of the production and maintenance sub-problems via the bi-objective formulation.
  • Küçük Resim Yok
    Öğe
    Optimizing COVID-19 medical waste management using goal and robust possibilistic programming
    (Pergamon-Elsevier Science Ltd, 2024) Karimi, Hamed; Wassan, Niaz; Ehsani, Behdad; Tavakkoli-Moghaddam, Reza; Ghodratnama, Ali
    During the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline -based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real -case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.
  • Küçük Resim Yok
    Öğe
    A roommate problem and room allocation in dormitories using mathematical modeling and multi-attribute decision-making techniques
    (Emerald Group Publishing Ltd, 2024) Khalili-Fard, Alireza; Tavakkoli-Moghaddam, Reza; Abdali, Nasser; Alipour-Vaezi, Mohammad; Bozorgi-Amiri, Ali
    PurposeIn recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.Design/methodology/approachIn this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.FindingsThe results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method's applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.Originality/valueThis novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.