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Öğe An analytics approach to decision alternative prioritization for zero-emission zone logistics(Elsevier Inc., 2022) Deveci, Muhammet; Pamucar, Dragan; Gökaşar, Ilgın; Delen, Dursun; Wu, Qun; Simic, VladimirUrban freight transportation requires wise management considerations since it is one of the most challenging issues cities face to attain sustainability. To help with the challenging decision process, an integrated two-stage decision analysis approach is proposed. In the first stage, the Defining Interrelationships Between Ranked criteria (DIBR) method is used to consolidate the experts’ opinions to compute the weights of the predetermined decision criteria. In the second stage, a novel approach that integrates Combined Compromise Solution (CoCoSo) with the context of type-2 neutrosophic numbers is used to identify the most optimal management decision alternative. A case study is developed to show the viability and practicability of the proposed methodology. The results indicated that “building a logistics center (for fast and cheap delivery)” is the highest-ranked decision alternative, followed by “optimized and integrated operation of urban logistics,” and “zero-emission zone implementation,” respectively. The proposed methodology can be used as a decision analysis framework for urban city authorities while selecting the most optimal policies and related solution alternatives towards achieving and sustaining low-emission urban freight transportation. © 2022 Elsevier Inc.Öğe Appointment scheduling problem under fairness policy in healthcare services: fuzzy ant lion optimizer(Elsevier, 2022) Ala, Ali; Simic, Vladimir; Pamucar, Dragan; Tirkolaee, Erfan BabaeeThis study addresses the application of the Integer Linear Programming technique for the patient Appointment Scheduling Problem (ASP). In this research, we propose a Mixed-Integer Linear Programming (MILP) model to formulate the problem and treat patients admitted to hospitals and stay in a queue based on their general health status (urgent or regular patients). Moreover, the ASP has two main objectives that often provide early patient admissions. The first objective is based on fairness policy as an essential factor in the healthcare service to help minimize patient waiting time. The second one is to maximize the efficiency of healthcare services in line with patients’ satisfaction. Moreover, we have addressed the Fuzzy Ant Lion Optimization (FALO) strategy and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are utilized to compare and solve the resulting multi-objective ASP. As the application of the model, fairness policy is analyzed in scenario 1 using FALO, and in scenario 2, NSGA-II is applied. The performances of the solution algorithms are then tested using datasets of a big regional hospital in Shanghai. The outcomes indicate potential advantages of implementing the presented approach. In particular, the suggested FALO increases the fairness and patients’ satisfaction by more than 80% while reducing the waiting times by 50% within the basic appointment scheduling system. © 2022 Elsevier LtdÖğe Blood supply chain network design with lateral freight: A robust possibilistic optimization model(Pergamon-Elsevier Science Ltd, 2024) Ala, Ali; Simic, Vladimir; Bacanin, Nebojsa; Tirkolaee, Erfan BabaeeThe blood supply chain stands out as a crucial component within a healthcare system, which can significantly improve efficiency and save the health system's costs. This paper presents a multi-objective blood supply chain network design problem that aims to reduce the cost of establishing fixed and temporary facilities, transferring blood products, and the amount of shortage. In order to address the shortfall and boost adaptability, lateral freight across hospitals is suggested due to the uncertainty in supply and demand. A novel robust possibilistic mixed-integer linear programming method is proposed in this work in order to deal with distribution and locational decisions. Two well-known solution approaches of lexicographic and Torabi-Hassini methods are then utilized to treat the multi-objectiveness of the robust possibilistic optimization model. Lateral freight between various blood supply chain demands significantly affects load balancing, declining both delivery time and costs. According to the obtained outcomes, the overall delivery time and total cost decrease by 10% and 15%, respectively. Moreover, it is revealed that the lexicographic approach outperforms the Torabi-Hassini method in this research.Öğe The combined effects of interest and inflation rates on inventory systems: A comparative analysis across countries(Elsevier, 2023) Khakbaz, Amir; Mensi, Walid; Tirkolaee, Erfan Babaee; Hammoudeh, Shawkat; Simic, VladimirInterest and inflation rates are among the most important economic indicators of any country. Inventory management is also known as one of the most critical components of supply chains and logistics systems. This study conducts a comparative study to analyze the combined effects of interest and inflation rates on inventory systems in different countries as part of macroeconomics. To do so, a novel inventory model is developed by accounting for interest rate, inflation, and increasing linear demand over time which affect inventory costs. In terms of the main parameters, the developed model is divided into two groups, where each group is solved separately. The results demonstrate that Venezuela, Sudan, Zimbabwe, Iran, and Liberia are the five countries with the most potential volume of hoarding of goods. These countries should increase their interest rates by at least 118.06%, 22.42%, 7.84%, 10.84%, and 6.94%, respectively, to counter the increasing amount of hoarding. Moreover, the findings reveal that Venezuela, Zimbabwe, Iran, Sudan, and Turkey, have the highest cost of inventory systems.Öğe A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreak(Taylor & Francis Ltd, 2023) Ghasemi, Peiman; Goodarzian, Fariba; Simic, Vladimir; Tirkolaee, Erfan BabaeeThis study develops a novel multi-objective mathematical model for a Plasma Supply Chain Network (PSCN) in order to maximise the coverage of blood donors during periods and minimise the blood transportation costs between different nodes, relocation cost of temporary mobile facilities, inventory holding cost of the blood, and the costs of newly established blood centres. Therefore, the major contribution of this work is the simultaneous consideration of resiliency and efficiency in the proposed PCN during the COVID-19 outbreak. To address the uncertain parameters, Stochastic Chance-Constrained Programming (SCCP) method is applied to the model. Additionally, to solve the PSCN model, the & epsilon;-constraint method is employed for small- and medium-sized problems and then a multi-objective invasive weed optimisation (MOIWO) algorithm is implemented for large-sized problems. To validate the suggested methodology, a variety of problem instances is designed and solved using the solution techniques, considering two assessment metrics of Hyper Volume (HV) and Min Ideal Distance (MID). Moreover, a real case study and sensitivity analyses on significant parameters are conducted to configure the optimal network. Eventually, the obtained results are examined and useful decision aids are suggested.Öğe DeepFND: an ensemble-based deep learning approach for the optimization and improvement of fake news detection in digital platform(Peerj Inc, 2023) Venkatachalam, K.; Al-onazi, Badriyya B.; Simic, Vladimir; Tirkolaee, Erfan Babaee; Jana, ChiranjibeEarly identification of false news is now essential to save lives from the dangers posed by its spread. People keep sharing false information even after it has been debunked. Those responsible for spreading misleading information in the first place should face the consequences, not the victims of their actions. Understanding how misinformation travels and how to stop it is an absolute need for society and government. Consequently, the necessity to identify false news from genuine stories has emerged with the rise of these social media platforms. One of the tough issues of conventional methodologies is identifying false news. In recent years, neural network models' performance has surpassed that of classic machine learning approaches because of their superior feature extraction. This research presents Deep learningbased Fake News Detection (DeepFND). This technique has Visual Geometry Group 19 (VGG-19) and Bidirectional Long Short Term Memory (Bi-LSTM) ensemble models for identifying misinformation spread through social media. This system uses an ensemble deep learning (DL) strategy to extract characteristics from the article's text and photos. The joint feature extractor and the attention modules are used with an ensemble approach, including pre-training and fine-tuning phases. In this article, we utilized a unique customized loss function. In this research, we look at methods for detecting bogus news on the internet without human intervention. We used the Weibo, liar, PHEME, fake and real news, and Buzzfeed datasets to analyze fake and real news. Multiple methods for identifying fake news are compared and contrasted. Precision procedures have been used to calculate the proposed model's output. The model's 99.88% accuracy is better than expected.Öğe Designing a reliable-sustainable supply chain network: adaptive m-objective ?-constraint method(Springer, 2024) Sepehri, Arash; Tirkolaee, Erfan Babaee; Simic, Vladimir; Ali, Sadia SamarIn the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design-a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.Öğe Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model(Elsevier Science Inc, 2023) Jafarzadeh-Ghoushchi, Saeid; Asghari, Mohammad; Mardani, Abbas; Simic, Vladimir; Tirkolaee, Erfan BabaeeEfficient humanitarian supply chain (HSC) management plays an underlying role in saving lives, reducing human torment, and contributing to sustainable development during a disaster. Accordingly, the issue of locating and allocating relief facilities in the first hours after the occurrence of a disaster has a great impact on providing timely service. This study addresses a sustainable location-allocation-inventory problem (LAIP) to design an efficient HSC through concurrently optimizing four objectives of fairness, timeliness, economic productivity, and social justice. To do so, a novel scenario-based multi-objective mixed-integer linear programming (MILP) model is developed to formulate the problem under uncertainty. According to this model, the process of taking care of injured people is carried out in three stages of decision-making. Maximum facilities for sending relief supplies are used to supply the demand at each stage. In addition, the three factors of supply, demand, and communication routes between the centers and the affected areas are defined as fuzzy random parameters. Since the proposed model contains multiple objectives, goal programming (GP) is applied to provide a single-objective model. The validation of the developed methodology is made with the help of an illustrative example in the literature, and the results are obtained and evaluated using sensitivity analysis of the objective functions' weights. As one of the main findings, sending the maximum available supplies in MDCs to the affected areas in three stages using surplus vehicles is the best solution to cover the shortage of products. Finally, it is revealed that the proposed methodology can be utilized by managers to tackle the complexity of the problem during natural disasters.Öğe Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis(Elsevier, 2023) Todorovic, Mihailo; Stanisic, Nemanja; Zivkovic, Miodrag; Bacanin, Nebojsa; Simic, Vladimir; Tirkolaee, Erfan BabaeeThis study aims to create a machine learning model that can predict opinions in external audits and surpass the benchmark set in a prior study from the literature. This tool could reduce audit risk, which is a crucial task in external audits. Previous studies have shown that it is possible to create models that can predict the audit opinion a company will receive. In these studies, authors used statistics and machine learning models, and both non-financial (e.g. audit lag) and financial data (e.g. financial ratios, or absolute value items available from financial statements) to make predictions. In this study, the performance of the XGBoost model optimized by metaheuristics algorithms is examined and evaluated. This study compares the performance of six different metaheuristic algorithms used to tune the XGBoost model in two separate scenarios. The first scenario represents a realistic client portfolio, where a majority of the clients are known, while the second scenario simulates a new clients-only portfolio, a more difficult scenario where prior information such as audit lag is not available. The study uses a dataset of 12,690 observations of Serbian companies and their audit opinions from 2016 to 2019. The findings indicate an improvement over the benchmark due to a more optimized hyperparameter tuning process and the use of the iterative sine-cosine algorithm for the XGBoost model.Öğe An integrated decision support framework for resilient vaccine supply chain network design(Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Torkayesh, Ali Ebadi; Tavana, Madjid; Goli, Alireza; Simic, Vladimir; Ding, WeipingDesigning resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Nondominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the & epsilon;-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.Öğe Intuitionistic fuzzy power aczel-alsina model for prioritization of sustainable transportation sharing practices(Elsevier, 2022) Senapati, Tapan; Simic, Vladimir; Saha, Abhijit; Dobrodolac, Momcilo; Rong, Yuan; Tirkolaee, Erfan BabaeeTraffic congestion and environmental pollution generated by transportation activities significantly endanger the sustainable development of cities. This study presents a new strategy for implementing the concept of shared mobility, where postal operators use their widespread networks of units and serve as service providers. To enhance its real-world implementation, four sustainable transportation sharing practices are elaborated. The key question is how to identify the most sustainable alternative that should be offered by service providers. To resolve this challenge, this paper develops an advanced decision support model based on Aczel-Alsina aggregation operators and power operators within the intuitionistic fuzzy (IF) environment. The criteria weights are determined through the Shannon entropy-based power weighted method. Aczel-Alsina operations for IF numbers are proposed to aggregate the decision information. In light of these operational laws, two IF Aczel-Alsina aggregation operators and their enviable characteristics are provided. These advanced aggregation operators are used to formulate the IF power Aczel-Alsina model. The case study of the city of Novi Sad illustrates its applicability. According to the research findings, it is recommended that the public postal operator invests in a sharing e-bicycle fleet. The comparative investigation demonstrates the superiority of the developed decision support model. Its major strengths are simple calculation and fast information processing. © 2022 Elsevier LtdÖğe Neutrosophic CEBOM-MACONT model for sustainable management of end-of-life tires(Elsevier, 2023) Simic, Vladimir; Dabic-Miletic, Svetlana; Tirkolaee, Erfan Babaee; Stevic, Zeljko; Deveci, Muhammet; Senapati, TapanManagement of end-of-life tires (ELTs) has evolved into an important sustainability requirement that should follow circular economy principles. It is increasingly important to find environmentally -friendly and cost-effective solutions for ELT management, particularly in the context of large freight transportation companies. Selecting the most sustainable solution from the set of available ELT management strategies, such as retreading, recycling, energy recovery, and landfilling, presents a decision-making challenge for authorities. This study aims to introduce a practical evaluation frame-work comprised of strategy alternatives and key decision-making criteria to support transportation companies in managing ELT flows. Also, the research introduces an advanced two-stage neutrosophic decision support model to solve the addressed problem and reveal the most sustainable strategy. The model is based on the integration of the cross-entropy-based optimization model (CEBOM) method and mixed aggregation by comprehensive normalization technique (MACONT) under the type -2 neutrosophic number (T2NN) environment. Prominent features of T2NN-CEBOM are hybrid weighting sub-framework and processing controllability. Distinguished characteristics of T2NN-MACONT are av-erage referencing, triple-normalization support, modeling of risk attitude behavior, adjustable ordering schemes, and an advanced scoring system. The real-life study of one of the largest German freight transportation companies that operates along an important European transit route offers practical insights for decision-makers when evaluating ELT management strategies. The research findings show that retreading is the most sustainable solution. The eight sensitivity analyses confirm the high robustness of the introduced decision-support model. The comparative analysis reveals the superiority of T2NN-CEBOM-MACONT for employment in practical settings.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems(Elsevier, 2023) Simic, Vladimir; Dabic-Miletic, Svetlana; Tirkolaee, Erfan Babaee; Stevic, Zeljko; Ala, Ali; Amirteimoori, ArashIndustry 4.0 technologies embedded in the warehouse management system (WMS) are needed to improve the automation of material handling activities such as receiving, storing, picking, sorting, packaging, and delivering. This research aims to introduce a neutrosophic multi-criteria group decision -making tool that is intelligible in supporting the transition and upgrading of WMS with Industry 4.0-based solutions. This advanced two-stage model is based on the integration of the logarithmic percentage change-driven objective weighting (LOPCOW) method and the additive ratio assessment (ARAS) method under the type-2 neutrosophic number (T2NN) environment. In the first stage, T2NN-LOPCOW generates an objective importance vector of decision-making criteria. In the second stage, T2NN-ARAS based on the generalized weighted Heronian mean operator provides an advantageous order of Industry 4.0-based material handling technologies. T2NN-LOPCOW-ARAS brings the following novelties: ((i) to straightforwardly represent and explore interconnection levels between weights of criteria, ((ii) to provide wide-scoping insight into the stability of initial priority order, as well as a broad spectrum of flexible solutions, ((iii) to control the normalization procedure and minimize distortions due to the double-normalization backbone. The real-life case study of a logistics company from the Serbian grocery retail sector illustrates the practical applicability of T2NN-LOPCOW-ARAS. A practical evaluation framework is defined to comprehensively assess automated guided vehicles (AGVs), collaborative robotics, and drones. The sensitivity analyses show the high robustness of the proposed framework. The comparative investigation shows that T2NN-LOPCOW-ARAS is superior to the extant methods. The research findings show that AGVs are the most favorable Industry 4.0-based material handling solution.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe A parallel heuristic for hybrid job shop scheduling problem considering conflict-free AGV routing(Elsevier, 2023) Amirteimoori, Arash; Tirkolaee, Erfan Babaee; Simic, Vladimir; Weber, Gerhard-WilhelmIn this study, a novel and computationally efficacious Parallel Two-Step Decomposition-Based Heuristic (PTSDBH) and a Mixed Integer Linear Programming (MILP) are developed to tackle the concurrent scheduling of jobs and Automated Guided Vehicles (AGVs) or transporters in a hybrid job shop system. Finite multiple AGVs, AGV eligibility, job's alternative process routes, job re-entry, and conflict-free AGV routing are considered. As far as the authors know, the importance of conflict-free routing for AGVs has not been featured in any of the past studies. Conflict-free AGV routing is an indispensable technicality, specifically where AGVs are the main mean of transportation as AGVs may collide on routes and the whole system ends up in breakdown. To avoid this issue, a conflict-free routing strategy is considered. Utilizing the parallel computing approach, PTSDBH is capable of tackling large-sized problems in remarkably shorter runtimes. To support this, PTSDBH is compared against three literarily well-known metaheuristics; i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) along with TSDBH (i.e., the single-core variant of PTSDBH) on three different-sized sets of benchmark instances. The results reveal that PTSDBH and TSDBH produce the same objective values and outperform the metaheuristics in terms of the quality of objective value. However, the runtimes of TSDBH are considerably higher than those of PTSDBH as it only uses one core to process. Finally, employing Nemenyi's post-hoc procedure for Friedman's test and the convergence plot, it is supported that the objective values generated by PTSDBH and TSDBH are significantly more desirable than those generated by the metaheuristics.
