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Item type:Publication, A hub location model in the sustainable supply chain considering customer segmentation(Emerald, 2020-12-30)Purpose This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type. Design/methodology/approach In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem. Findings As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms. Practical implications The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability. Originality/value The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of three metaheuristic algorithms to time-cost-quality trade-off project scheduling problem for construction projects considering time value of money(MDPI, 2021-12-01)Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The sustainable two-echelon location-routing problem considering combined open and closed routes under uncertainty(Research Square, 2022-03-14)Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing which leads to a specific type of transportation problems called open routing. On the other hand, the growing concerns of environmental impacts have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes are simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing is considered in each echelon. A small-sized problem instance is solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) are exploited to solve the medium and large size problems. The performance of the algorithms is compared in terms of time, MID, diversity, spacing, SNS, and RAS indexes. The results show that the MOSFS algorithm outperforms the NSGA-II based on several indexes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-objective sustainable closed-loop supply chain network design considering multiple products with different quality levels(MDPI, 2022-08-01)International laws and increasing consumer awareness have led to drastic changes in traditional supply chain network designs. Moreover, because of environmental and social require-ments, traditional supply chain networks have changed to sustainable supply chain networks. On the other hand, reverse logistics can be effective in terms of environmental and economic aspects, so the design of the supply chain network as a closed loop is necessary. In addition, customers have a demand for different products with different quality levels. Considering different types of customers with a variety of consumption trends can be a challenging issue, and is addressed in this study. The main contributions of this research are considering different quality levels for products as well as different tendencies of customers towards environmental issues. In this study, a sustainable closed-loop supply chain model is designed that seeks to balance economic, environmental, and social responsibilities. In this paper, costs and customer demands for different types of products at different quality levels are considered under uncertain conditions using a robust possibilistic programming method. The proposed multi-objective model is solved using the Augmented Epsilon Constraint (AEC) method that provides an efficient set of solutions for all decision-making levels. The results show that the robust possibilistic programming method is more effective in dealing with uncertainties than the possibilistic programming method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Green two-echelon closed and open location-routing problem: Application of NSGA-II and MOGWO metaheuristic approaches(Springer Science+Business Media, 2022-06-02)Nowadays organizations outsource transportation of goods or services to reduce cost which leads to a particular type of problem called open location-routing. Also, each logistic organization possesses a limited number of specific vehicles that may not be enough in certain circumstances. This issue indicates the importance of simultaneously considering both open and closed routs. On the other hand, the growing concerns about the detrimental environmental impacts of human activities reveal the necessity of paying attention to environmental issues in logistics. In this study, a bi-objective mathematical programming model is proposed for two-echelon close and open location-routing problem (2E-COLRP) including two echelons of factories, depots and customers to minimize costs and CO2 emissions. The proposed model finds the optimal routs, optimal number of vehicles and facilities as well as the locations of facilities. The augmented epsilon constraint method is used as an exact method to solve the small-sized problems. Due to complexity of model, two metaheuristic algorithms named MOGWO and NSGA-II are utilized to tackle the problems. The efficiency of two aforementioned algorithms is evaluated in terms of several indices considering 22 problem instances with various sizes. The results show that MOGWO performs better than NSGA-II. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient intelligent intrusion detection system using fuzzy logic based on the particle swarm optimization algorithm: A case study(IOS Press, 2024-10-25)This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 seconds and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A multi-objective model for cooperative delivery of customer orders using multiple trucks and UAVs considering weather conditions(Elsevier BV, 2024-12-16)The increasing demand for fast, eco-friendly delivery services driven by e-commerce growth has led to innovative logistics solutions. Hybrid delivery systems combining trucks and Unmanned Aerial Vehicles (UAVs) are emerging as innovative approaches to meet these demands. This study develops a comprehensive mathematical model to optimize such systems, addressing key challenges such as UAV limitations (short range, cargo weight, and energy constraints) and the influence of weather conditions (wind speed, wind direction , and temperature). A significant contribution of this work is the simultaneous consideration of weather factors on both truck energy consumption and UAV flight performance , enabling realistic and adaptive logistics planning. In the proposed system, UAVs operate alongside trucks, returning for recharging after completing their assigned deliveries, which enhances operational feasibility. The model is designed to minimize two objectives: delivery time and cost. Small problem instances are solved using CPLEX solver for validation, while larger instances are tackled using NSGA-II and MOPSO meta-heuristic algorithms. Sensitivity analyses further explore the impact of weather parameters on system performance , offering valuable insights into its adaptability under uncertain conditions. Results demonstrate the model's effectiveness and the computational efficiency of the algorithms in handling complex, real-world scenarios, contributing to sustainable and intelligent logistics solutions.8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A green project scheduling with material ordering problem considering cost constraints and quality of materials(Emerald Publishing Limited, 2024-06-17)Purpose Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors. Design/methodology/approach In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation. Findings Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule. Originality/value The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A fuzzy bi-objective mathematical model for perishable medical goods supply chain network considering crisis situations: An empirical study(SAGE Publications Ltd, 2024-01-01)In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment. In this paper, a three-level supply chain including suppliers, intermediate warehouses, and final customers is developed for perishable medical items. The uncertainty of customer demand for service and the spent time in the intermediate warehouses are considered using the exponential distribution functions. Also, it is assumed that the life-cycle of perishable medical goods follow the Weibull distribution function. The model attempts to minimize the total costs of the supply chain and total presence time of perishable items in the whole chain. The LP-Metric method is employed for solving small-sized problems. Due to the NP-Hardness of the problem, the modified Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are utilized as 2 well-known and efficient meta-heuristic algorithms for solving large-sized problems. The findings indicate that the meta-heuristic algorithms are efficient in achieving close to the optimal solution for large-size problems in a reasonable time. Also, the results demonstrate that NSGA-II outperforms MOPSO in terms of the high quality solution. Finally, the applicability of the model to real-world problems is demonstrated using a real case study. This paper can assist the planners and decision-makers of perishable drugs supply chain networks in crisis conditions with on-time supplying and distributing the required emergency items. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A sustainable hub location-allocation model considering the inspection of defective wagons in the rail freight network(Inderscience Publishers, 2024-06-15)The increasing demand for rail transport necessitates an effective transportation network design for the shipment of goods with minimum cost and time. Since the breakdown of wagons is a main delay factor in rail transportation, the maintenance and repairs of defective wagons becomes prominent. In this study, main stations are considered as hubs, and hubs are places where defective wagons are collected. For this purpose, a robust multi-objective mathematical model is proposed to minimise transportation costs considering customer demand. Also, the model seeks to minimise the total transportation time and emissions. The AEC method is exploited to solve and validate the proposed model. Moreover, the sensitivity analysis is performed to demonstrate the effect of changing the main parameters on the outcomes. The results show that repairs and maintenance can affect the capacity. Also, the findings demonstrate the applicability and validity of the proposed model in the railway sector. [Submitted: 29 May 2023; Accepted: 16 January 2024]