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A data-driven robust decision-making model for configuring a resilient and responsive relief supply chain under mixed uncertainty
(Springer, 2024) Javan-Molaei, Bahar; Tavakkoli-Moghaddam, Reza; Ghanavati-Nejad, Mohssen; Asghari-Asl, Amin
The crucial role of the Relief Supply Chains (RSCs) in the response phase of disaster management is undeniable. However, the literature shows that the simultaneous consideration of the resilience and responsiveness dimensions in designing the RSCs under mixed uncertainty has been ignored by researchers. In this regard, to cover the mentioned gap, the current study aims to configure an RSC by considering two critically important features namely resilience and responsiveness under mixed uncertainty. For this purpose, this work proposed a multi-stage Decision-Making Framework (DMF). In the first stage, a Multi-Objective Model (MOM) is proposed that minimizes the total cost, maximizes the responsiveness level, and maximizes the resilience of the RSC. In the second stage, to deal with mixed uncertainty, a data-driven robust approach based on the Fuzzy Robust Stochastic (FRS), Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX), and Artificial Neural Networks (ANN) methods is developed. In the third stage, to solve the proposed model, a novel variant of the goal programming method is developed. In general, the main contribution of this study is to develop a novel data-driven DMF to design a resilient-responsive RSC. To show the applicability and efficiency of the developed decision-making method, a real-world case study, the flood that happened in 2019 in Golestan province, Iran, is considered. Eventually, sensitivity analysis, managerial insights, and theoretical implications are presented. According to the achieved results, primary suppliers 1, 3, 5, and 7 and also backup supplier 1 are selected. Also, the results demonstrate that distribution centers 1, 2, 3, and 5 are established. Moreover, the optimal utilization of different transportation modes is specified in the achieved results. The outputs demonstrate that the developed data-driven FRS approach has better performance in comparison with the deterministic and traditional FRS models. Besides, the outputs indicate that the developed solution method has better performance in comparison with the traditional approaches.
A framework for robust glaucoma detection: A confidence-aware deep uncertainty quantification approach with a comprehensive assessment for enhanced clinical decision-making
(Elsevier ltd, 2025) Zarean, Javad; Tajally, AmirReza; Tavakkoli-Moghaddam, Reza; Sajadi, Seyed Mojtaba; Wassan, Niaz
Glaucoma poses a significant threat to public health worldwide, as it can result in irreversible vision loss. Timely identification is vital for halting the progression of visual field deterioration. In recent years, deep neural networks (DNNs) have become increasingly popular in medical imaging due to their ability to identify patterns. As a result, this study introduces a new computer-aided diagnosis (CAD) system based on deep learning (DL) algorithms for glaucoma detection that extracts meaningful features from retinal fundus images (RFIs) and employs uncertainty quantification (UQ) models, including Monte Carlo dropout (MCD), ensemble Bayesian, and ensemble Monte Carlo dropout (EMCD), to generate both point estimates and confidence values for the outputs, thereby capturing the uncertainty associated with the classifications. The proposed framework is validated using well-known clinical datasets, and the reliability of the outputs is evaluated using comprehensive performance metrics such as expected calibration error (ECE), entropy analysis, and a multi-criteria UQ assessment. Experimental results demonstrate the superiority of the ensemble model, with uncertainty accuracies registering at 97.64%, 97.26%, and 98.97% for the "ACRIMA", "RIM-ONE-DL", and "ORIGA" datasets, respectively. Moreover, the proposed algorithms can alert users to the majority of erroneous diagnoses by assigning uncertainty labels, providing valuable insights for clinicians in glaucoma detection. Such tools can assist healthcare professionals in reducing the probability of misdiagnosis and ensuring that patients receive timely and appropriate treatment.
A mathematical model for resource sharing with bilateral contracts in a supply chain with government intervention under a game theory approach
(Materials and energy research center, 2024) Einy-Sarkalleh, G. R.; Tavakkoli-Moghaddam, Reza; Hafezalkotob, A.; Najafi, S. E.
Contracts have been used for coordination in many supply chain alliances among businesses. Because bilateral contracts are significantly more successful and profitable than uni-contracts, In this article, the issues of implementing bilateral contracts are investigated with the approach of game theory and government intervention to increase bilateral interaction between members of co -production and codistribution in the supply chain. By adopting the game theory model between these two members of the chain and intervention government, this research seeks to increase production and distribution by making maximum use of the excess capacity of production and distribution in the chain. In this way, the producer uses his surplus capacity in two ways: one is produced directly by the producer and enters the market by the distributor, and the other is an order that the distributor gives to the producer, which is different from the product that the producer produces. It is produced directly and given by the distributor. The purpose of this research is to investigate and analyze the amounts and profits resulting from the participation of production and distribution with government intervention in the supply chain . According to this research, governments should provide an environment for supply chain members to have more cooperation with each other because, in the case of cooperation among supply chain members, the profits of the chain and the members will increase.
A matheuristic approach for an integrated lot-sizing and scheduling problem with a period-based learning effect
(Elsevier ltd, 2025) Rohaninejad, Mohammad; Vahedi-Nouri, Behdin; Tavakkoli-Moghaddam, Reza; Hanzálek, Zdeněk
This research investigates a multi-product capacitated lot-sizing and scheduling problem incorporating a novel learning effect, namely the period-based learning effect. This is inspired by a real case in a core analysis laboratory under a job shop setting. Accordingly, a Mixed-Integer Linear Programming (MILP) model is extended based on the big-bucket formulation, optimizing the total tardiness and overtime costs. Given the complexity of the problem, a cutting plane method is employed to simplify the model. Afterward, three matheuristic methods based on the rolling horizon approach are devised, incorporating two lower bounds and a local search heuristic. Furthermore, a post-processing approach is implemented to incorporate lot-streaming possibility. Computational experiments demonstrate: 1) the simplified model performs effectively in terms of both solution quality and computational time; and 2) although the model encounters challenges with large-scale instances, the proposed matheuristic methods achieve satisfactory outcomes; and 3) it can be inferred that the complexity of the models and solution methods are independent of the learning effect; however, the value of learning effect may impact the performance of the lower bounds; 4) in manufacturing settings, where the lot-streaming is possible, incorporating post-processing can drastically improve the objective function; 5) the impact of the period-based learning effect in the results is significant, and the model's sensitivity to time-based parameters (e.g., learning rate) is more than cost-based ones (e.g., tardiness cost).
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.
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.
Lagrangian relaxation method for solving a new time-dependent production - distribution planning model
(Pergamon-elsevier science, 2024) Rezaali, Zahra; Ghodratnama, Ali; Amiri-Aref, Mehdi; Tavakkoli-Moghaddam, Reza; Wassan, Niaz
In today 's competitive business environment, organizations must decide how to handle the processing of their logistics equipment economically. One of the vital logistical concerns is distribution planning that is especially crucial depending on the facilities and goods being used. When it comes to perishable goods, this problem assumes double the significance. The position of the warehouse and the route of the vehicles make up the distribution planning problem. These two problems are considered concurrently and solved in the location-routing mathematical model. This paper aims to provide a production and distribution strategy to serve clients and consumers better. This research attempts to produce as efficiently as possible while providing prompt customer service, which is crucial in today 's corporate environment. This study uses three-level supply chains for perishable goods to create a supply chain network that minimizes costs. In this case, time-dependent demands refer to requests that may be made when the vehicle will arrive. Places and routes in this area are designed to meet all needs. In general, it is desirable to have factories and distribution centers in known locations, know the service 's opening and closing hours, and know how to manage the flow of materials and goods as they are stored in distribution centers and for retailers (clients). Additionally, it is desirable to route vehicle that connects the various levels of the supply chain and ensures that vehicles travel on schedule overall. First, the supply chain model represented as non-linear programming is transformed into linear programming to solve it using the CPLEX solver of GAMS commercial software and the Lagrangian relaxation (LR) method. Then, this model is verified using numerical examples and related parameters to see how it impacts the variables and the objective function 's result. The results show the capability of the LR method.
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.
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.
Preemptive and non-preemptive multi-skill multi-mode resource-constrained project scheduling problems considering sustainability and energy consumption: a comprehensive mathematical model
(Academic press, 2024) Shahabi-Shahmiri, Reza; Tavakkoli-Moghaddam, Reza; Dolgui, Alexandre; Mirnezami, Seyed-Ali; Ghasemi, Mohammad; Ahmadi, Mahsa
Modern project managers cope with significant challenges to schedule and control projects considering dynamic environments, frequent uncertainties, strict project deadlines, and stricter sustainable requirements above all. Sustainability taking into account resource utilization has been recently associated with project management. Hence, this paper presents a new mixed-integer linear programming (MILP) model with two objectives for a resource-constrained project scheduling problem (RCPSP) with multiple skills and multiple modes, assuming preemptive and non-preemptive activities in an uncertain environment. Given the importance of sustainable developments in projects, the considered objectives are to maximize job opportunities and minimize project duration, resource costs, and total energy consumption. To deal with the model, an AUGNMECON2VIKOR algorithm is utilized to create Pareto solutions. In this model, project activities can be crashed by allocating extra resources. Furthermore, multi-skill resources are used to perform project activities. This study also investigates the impact of these resources on project scheduling. To deal with uncertain circumstances, a fuzzy chance-constrained programming method is employed to develop a robust possibilistic programming model. With respect to the increasing significance of sustainability in project management, this study pioneers the examination of the impact of sustainable factors on project scheduling. Finally, the proposed formulation is validated using instances from the well-known PSPLIB and MMLIB test sets. Finally, a comparison is drawn between the presented solution method considering AUGMECON2VIKOR and AUGMECON2.
Robust electroencephalogram-based biometric identification against GAN-generated artificial signals using a novel end-to-end attention-based CNN-LSTM neural network
(Springer, 2025) Zarean, Javad; Tajally, AmirReza; Tavakkoli-Moghaddam, Reza; Kia, Reza
Electroencephalogram (EEG) signals, which exhibit dynamic properties and discriminative information among individuals, have recently been employed to develop human biometric identification and authentication systems. Despite the increasing interest in EEG-based human identification, the state-of-the-art still needs high-accuracy and easy-to-use systems in real-life applications. To improve the accuracy, robustness, and user-friendliness of EEG-based human identification systems, this paper presents a novel attention-based convolutional-long short-term memory network for EEG-based human biometric identification (ABCL-EHBI), which is robust against artificial EEG signals generated by generative adversarial networks (GANs). The proposed system uses an attention mechanism along with convolutional neural networks (CNNs) and long short-term memories (LSTMs) layers, leading to more effective exploitation of the raw EEG signals' spatial and temporal discriminative characteristics compared to a simple CNN-LSTM (CL) system. The system was evaluated and validated using the PhysioNet motor imagery dataset, which incorporates EEG signals of 109 individuals performing six various tasks. Experimental results show that the proposed approach achieves F1-Score accuracy of 99.65, 99.64, and 99.55 under the condition of using 64, 14, and only 9 EEG channels, respectively, which is better than the performance of EEG-based human identification in the previous studies. The fact that the proposed approach receives raw EEG signals as input without any need for feature extraction (end-to-end), shows high accuracy when using a small number of EEG channels and yields high accuracy against artificial EEG signals, making it reliable and easy to deploy in real-life applications.
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.
Stepping into Industry 4.0-based optimization model: a hybrid of the NSGA-III and MOAOA
(Emerald group publishing LTD, 2024) Sadati-Keneti, Yaser; Sebt, Mohammad Vahid; Tavakkoli-Moghaddam, Reza; Baboli, Armand; Rahbari, Misagh
PurposeAlthough the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including preventive self-maintenance, self-scheduling and real-time decision-making.Design/methodology/approachA new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.FindingsThe results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.Originality/valueIn this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

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