Yazar "Tirkolaee, E.B." seçeneğine göre listele
Listeleniyor 1 - 6 / 6
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Circular closed-loop supply chain network design considering 3D printing and PET bottle waste(Springer Science and Business Media B.V., 2024) Rajabi-Kafshgar, A.; Seyedi, I.; Tirkolaee, E.B.One of the most critical pillars of Industry 4.0 (I4.0) is Additive Manufacturing (AM) or 3D Printing technology. This transformative technology has garnered substantial attention due to its capacity to streamline processes, save time, and enhance product quality. Simultaneously, environmental concerns are mounting, with the growing accumulation of plastic bottle waste, offering a potential source of recycled material for 3D printing. To thoroughly harness the potential of AM and address the challenge of plastic bottle waste, a robust supply chain network is essential. Such a network not only facilitates the reintegration of plastic bottle waste and 3D printing byproducts into the value chain but also delivers significant environmental, social, and economic benefits, aligning with the tenets of sustainable development and circular economy. To tackle this complex challenge, a Mixed-Integer Linear Programming (MILP) mathematical model is offered to configure a Closed-Loop Supply Chain (CLSC) network with a strong emphasis on circularity. Environmental considerations are integral, and the primary objective is to minimize the overall cost of the network. Three well-known metaheuristics of Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are employed to treat the problem which are also efficiently adjusted by the Taguchi design technique. The efficacy of our solution methods is appraised across various problem instances. The findings reveal that the developed model, in conjunction with the fine-tuned metaheuristics, successfully optimizes the configuration of the desired circular CLSC network. In conclusion, this research represents a significant step toward the establishment of a circular supply chain that combines the strengths of 3D printing technology and the repurposing of plastic bottle waste. This innovative approach holds promise for not only reducing waste and enhancing sustainability but also fostering economic and social well-being. © The Author(s) 2024.Öğe Integrated design of a sustainable waste management system with co-modal transportation network: A robust bi-level decision support system(Elsevier Ltd, 2024) Tirkolaee, E.B.; Simic, V.; Ghobakhloo, M.; Foroughi, B.; Asadi, S.; Iranmanesh, M.Efficient waste management practices play a critical role in addressing the acute challenges of environmental protection, public health and resource conservation. A well-designed system guarantees that waste is efficiently collected, treated and disposed while minimizing negative impacts on ecosystems and human well-being. This work presents a robust bi-level decision support system to establish a sustainable waste management system using a co-modal transportation network to treat municipal solid waste timely and efficiently. Consequently, two integrated multi-objective mathematical models are developed to formulate the problem. Configuring the municipal solid waste network in the first level of the suggested decision support system, the transportation network is designed in the second level taking into account non-identical modes. The objectives are to minimize total cost and total emission in both levels, while maximization of total job creation is also addressed in the first level. Robust optimization method and weighted goal programming method are then utilized to accommodate the developed decision support system against uncertainty and multi-objectiveness, respectively. To validate the efficiency of these methods, they are assessed against possibilistic linear programming technique and Lp-metric approach with the help of simple additive weighting (SAW) method, respectively. Eventually, several numerical examples are generated based on the benchmarks given in the literature, which are then tackled using CPLEX solve to appraise the applicability and complexity of the developed methodology. The findings reveal the efficacy of the decision support system in terms of finding solutions in less than 448 s on average. Finally, sensitivity analyses are performed to draw out useful practical implications and decision aids. © 2024 Elsevier LtdÖğe A Multi-objective Optimization Model for Sustainable-Robust Aggregate Production Planning Problem(Springer Science and Business Media Deutschland GmbH, 2023) Tirkolaee, E.B.; Aydın, N.S.; Mahdavi, I.; Çelik, B.Aggregate production planning (APP) is known as a demand-driven production planning activity using aggregate plans for manufacturing processes. It tries to match supply and demand within a medium-term time horizon. In this work, a sustainable-robust APP problem is modeled through a multi-objective mixed-integer linear programming (MOMILP) model. The objective functions are formulated in a way to simultaneously minimize total cost, minimize total environmental impacts and maximize service level. Then, robust optimization (RO) technique is used to treat the demand uncertainty within the problem. To treat the multi-objectiveness and find the optimal solution, an improved multi-choice goal programing (IMCGP) method is introduced as an extension to the classical goal programming approach (GP). Next, several numerical examples are generated in different scales to assess the validity and applicability of the suggested methodology under deterministic as well as uncertain conditions. Eventually, a set of sensitivity analyses are implemented to assess the behavior of objective functions against real-world uncertainty in model parameters. It is demonstrated that the proposed methodology is capable of modeling, solving and analyzing the sustainable-robust APP problem efficiently. As one of the main findings, although the 1st and 2nd objective functions are sensitive to conservatism level, the 3rd objective function remains neutral. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe A novel parallel heuristic method to design a sustainable medical waste management system(Elsevier Ltd, 2024) Amirteimoori, A.; Tirkolaee, E.B.; Amirteimoori, A.; Khakbaz, A.; Simic, V.Efficient management of waste generated in healthcare systems is crucial to minimize its environmental impact and ensure public health. Sustainable medical waste management (MWM) systems require careful network design, which can be achieved through efficient optimization techniques. This work develops a mixed-integer linear programming (MILP) to formulate the problem, a two-step MILP (TSMILP) to generate quality lower bounds, and a novel parallel heuristic algorithm to configure a sustainable waste management system including waste generation centers (WGCs), waste treatment centers (WTCs), waste recycling centers (WRCs), waste disposal centers (WDCs) and waste incineration centers (WICs). Such a hybrid methodology has not been yet offered in the literature wherein the aim is to address strategic (establishment of facilities), tactical (employment of transportation system), and operational decisions (transportation planning) optimally in large networks. As reflected in the literature, there is a huge gap in efficiency and application of combinatorial optimization, and parallel computing in sustainable MWM systems, where the suggested MILPs' solvers are not technically capable of discovering quality solutions in reasonable runtimes on large-sized instances. Thus, we suggest a novel heuristic equipped with parallel computing to share the complexity of the problem, with all the CPU cores to shorten runtime. Comparing the results generated by the parallel heuristic with those of the sequential heuristic, the MILP, and the TSMILP on three sets of benchmark instances using Nemenyi's post-hoc procedure for Friedman's test, it is inferred that the parallel heuristic is so effective in coping with the problem, and produces high-quality solutions, especially on the large-sized set. Finally, sensitivity analysis is adopted to analyze the effects of parameters on the objective values and provide useful managerial insights. © 2024 Elsevier LtdÖğe Optimization for Construction Supply Chain Management(Springer International Publishing, 2021) Golpîra, H.; Tirkolaee, E.B.Due to the special importance of procurement management in construction projects, this study provides an overview of the Construction Supply Chain (CSC) concept. Since the trend of academicians and practitioners to the CSC has increased and no review of the recent research with emphasis on mathematical modeling and optimization approaches has been done so far, the contribution of this research is unique and interesting. The main contribution of the research is that not only the past research trend in the field of CSC is considered from the point of view of mathematical modeling and optimization, but also the existing research gaps are extracted and the existing potentials for future research are introduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.Öğe Predicting Water Quality With Non-stationarity: Event-Triggered Deep Fuzzy Neural Network(Institute of Electrical and Electronics Engineers Inc., 2024) Wang, G.; Chen, H.; Han, H.; Bi, J.; Qiao, J.; Tirkolaee, E.B.Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively. IEEE
