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Öğe Big data-driven cognitive computing system for optimization of social media analytics(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Sangaiah, Arun Kumar; Goli, Alireza; Tirkolaee, Erfan Babaee; Ranjbar-Bourani, Mehdi; Pandey, Hari Mohan; Zhang, WeizheThe integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.Öğe Circular economy application in designing sustainable medical waste management systems(Springer, 2022) Tirkolaee, Erfan Babaee; Goli, Alireza; Mirjalili, SeyedaliNo Abstract AvailableÖğe Circular Economy Practices in the Context of Emerging Economies(Mdpi, 2024) Ali, Sadia Samar; Weber, Gerhard-Wilhelm; Tirkolaee, Erfan Babaee; Goli, Alireza[Abstract Not Available]Öğe Designing a portfolio-based closed-loop supply chain network for dairy products with a financial approach: Accelerated Benders decomposition algorithm(Pergamon-Elsevier Science Ltd, 2023) Goli, Alireza; Tirkolaee, Erfan BabaeeProduct portfolio design is one of the important and effective factors in the financial and physical flows of various supply chains, especially dairy products. Accordingly, the financial and physical flows of the portfolio should be taken into consideration during the supply chain design. In this study, a closed-loop supply chain (CLSC) network is designed for dairy products aiming at maximizing the net cash flow from assets and maximizing the amounts paid to shareholders simultaneously. To find the optimal policy, an accelerated Benders decomposition (ABD) algorithm is implemented to tackle the complexity of the model. Moreover, three multi-objective optimization solution approaches of the weighted sum method (WSM), augmented epsilon-constraint (AEC), and fuzzy multiobjective programming (FMOP) are implemented to tackle the bi-objectiveness of the model. Next, a real case study in Iran is investigated to reveal the applicability of the developed methodology. Numerical results reveal that the ABD method reduces CPU time by about 10.8%. Moreover, the results of the case study demonstrate that by integrating financial and physical flows, an improvement of 4.8478% in the net cash flow from assets and a 2.3% improvement in the amounts paid to shareholders compared to the current situation.Öğe Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors(Institute of Electrical and Electronics Engineers Inc., 2021) Goli, Alireza; Tirkolaee, Erfan Babaee; Aydın, Nadi SerhanIn today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this study, a fuzzy Mixed Integer Linear Programming (MILP) model is designed for Cell Formation Problem (CFP) including the scheduling of parts within cells in a Cellular Manufacturing System (CMS) where several Automated Guided Vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This paper tries to investigate the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and inter-cellular movements of parts. Due to the NP-hardness of the problem, a hybrid Genetic Algorithm (GA/heuristic) and a Whale Optimization Algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and other two well-known algorithms, i.e., Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem. IEEEÖğe Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm forjJust-in-time energy-aware flow shop scheduling problem with outsourcing option(Institute of Electrical and Electronics Engineers Inc., 2020) Tirkolaee, Erfan Babaee; Goli, Alireza; Weber, Gerhard WilhelmFlow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposed model is treated as a single objective MILP using a multiobjective fuzzy mathematical programming technique based on the ?-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions.Öğe Human paradigm and reliability for aggregate production planning under uncertainty(Elsevier, 2022) Gütmen, Selma; Weber, Gerhard Wilhelm; Goli, Alireza; Tirkolaee, Erfan BabaeeAggregate production planning (APP) within a supply chain is known as one of the main activities in general planning of large and leading companies throughout the world. In the present study, a survey is conducted on the importance of three main factors: (1) human paradigm, (2) reliability, and (3) uncertainty in the APP. To do so, these three factors are investigated and reviewed and the most significant challenges are discussed accordingly. Moreover, the most relevant studies performed recently are reviewed to find the major gaps in the literature. Finally, the survey is concluded through discussing the main challenges, limitations, and recommendations for future research.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 An integration of neural network and shuffled frog-leaping algorithm for CNC machining monitoring(Sciendo, 2021) Goli, Alireza; Tirkolaee, Erfan Babaee; Weber, Gerhard WilhelmThis paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).Öğe Logistics and Operations Modelling and Optimization for Sustainable Supply Chain(Mdpi, 2023) Weber, Gerhard-Wilhelm; Goli, Alireza; Tirkolaee, Erfan Babaee[Abstract Not Available]Öğe A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms(SPRINGER, 2022) Tirkolaee, Erfan Babaee; Goli, Alireza; Gutmen, Selma; Weber, Gerhard-Wilhelm; Szwedzka, KatarzynaMunicipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.Öğe A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems(Springer, 2021) Babaee Tirkolaee, Erfan; Goli, Alireza; Mardani, AbbasThe present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids.Öğe Preface to the special issue on computational performance analysis based on novel Intelligent methods: exploration and future directions in production and logistics(SCIENDO, 2022) Goli, Alireza; Tirkolaee, Erfan Babaee; Weber, Gerhard-WilhelmThis special issue of the Foundations of Computing and Decision Sciences, titled "Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics", is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field.Öğe Recent Advances of Solutions Algorithms for Logistics Routing Problems(Mdpi, 2023) Tirkolaee, Erfan Babaee; Goli, Alireza; Malmir, Behnam[Abstract Not Available]Öğe A robust optimization model to design an IoT-based sustainable supply chain network with flexibility(Springer, 2023) Goli, Alireza; Tirkolaee, Erfan Babaee; Golmohammadi, Amir-Mohammad; Atan, Zumbul; Weber, Gerhard-Wilhelm; Ali, Sadia SamarSupply chain network design is one of the most important issues in today's competitive environment. Moreover, the ratio of transportation costs to the income of manufacturing companies has increased significantly. In this regard, strategic decisions, as well as tactical decisions making, are of concern for supply chain network design. In this research, a flexible, sustainable, multi-product, multi-period, and Internet-of-Things (IoT)-based supply chain network with an integrated forward/reverse logistics system is configured where the actors are suppliers, producers, distribution centers, first- and second-stage customers, repair/disassembly centers, recycling centers, and disposal centers. In order to create flexibility in this supply chain, it is possible to dispatch directly to customers from distribution centers or manufacturing plants. For direct shipping, the application IoT system is taken into account in the transportation system to make them able to manage direct and indirect delivery at the same time. The options and considerations are then incorporated into a Multi-Objective Mixed-Integer Linear Programming model to formulate the problem which is then converted into a single-objective model using Goal Programming (GP) method. Moreover, in order to deal with uncertainty in the demand parameter, robust optimization approach is applied. The obtained results from a numerical example reveal that the proposed model is able to optimally design the supply chain network whose robustness is highly dependent on the budgets of uncertainty whereas up to 213.528% increase in the GP objective function is observed.Öğe A robust two-echelon periodic multi-commodity RFID-based location routing problem to design petroleum logistics networks: a case study(Springer Science and Business Media Deutschland GmbH, 2021) Babaee Tirkolaee, Erfan; Goli, Alireza; Weber, Gerhard WilhemThis study proposes a robust two-echelon periodic multi-commodity Location Routing Problem (LRP) by the use of RFID which is one of the most useful utilities in the field of Internet of Things (IoT). Moreover, uncertain demands are considered as the main part to design multi-level petroleum logistics networks. The different levels of this chain contain plants, warehouse facilities, and customers, respectively. The locational and routing decisions are made on two echelons. To do so, a novel mixed-integer linear programming (MILP) model is presented to determine the best locations for the plants and warehouses and also to find the optimal routes between plant level and warehouse facilities level, for the vehicles and between warehouse facilities level and customers’ level in order to satisfy all the uncertain demands. To validate the proposed model, the CPLEX solver/GAMS software is employed to solve several problem instances. These problems are analyzed with different uncertain conditions based on the applied robust optimization technique. Finally, a case study is evaluated in Farasakou Assaluyeh Company to demonstrate the applicability of our methodology and find the optimal policy.Öğe The stochastic location-routing-inventory problem of perishable products with reneging and balking(Springer Science and Business Media Deutschland GmbH, 2021) Aghighi, Azam; Goli, Alireza; Malmir, Behnam; Tirkolaee, Erfan BabaeeThe transport of perishable products is in need of specific control and safety operations, either due to their short shelf life or their particular storage circumstances. This study investigates an extended Location-routing-inventory problem (LRIP) for perishable products, in which a two-phase hybrid mathematical model is developed. In the first phase, the location-routing problem (LRP) is formulated with stochastic demands and travel time, and then in the second phase, a queue system is employed to model the inventory control problem based on the established locations and routes. Moreover, the effects of reneging and balking behaviors are studied in the second phase, and hereby, holding, shortage, product expiration, customer waiting times, and customer loss costs are calculated. To tackle the complexity of the problem, an improved genetic algorithm (IGA) is designed and is compared with the classic genetic algorithm (GA) and GAMS software. Finally, two small and large-sized illustrative examples and then different problem instances are taken into account to test the applicability of the suggested methodology. The obtained results demonstrate that the developed methodology of the research has an appropriate performance to deal with the high complexity of the problem.Öğe Sustainable Global Supply Chain Management from an International Perspective(Mdpi, 2023) Tirkolaee, Erfan Babaee; Goli, Alireza; Golpira, Heris; Gonzalez, Ernesto D. R. Santibanez[Abstract Not Available]
