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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.
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An analysis of the operation factors of three PSO-GA-ED meta-heuristic search methods for solving a single-objective optimization problem
(Hindawi Limited, 2022-01-01)
In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in optimization of mathematical equations. A comparison is then made between the results and the efficiency, which is determined by the number of iterations, the observed accuracy, and the overall run time. Additionally, the optimization employs 12 functions from Easom, Holder table, Michalewicz, Ackley, Rastrigin, Rosen, Rosen Brock, Shubert, Sphere, Schaffer, Himmelblau's, and Spring Force Vanderplaats. Furthermore, the crossover rate, mutation rate, and scaling factor are evaluated to determine the effectiveness of the following algorithms. According to the results of the comparison of optimization algorithms, the DE algorithm has the lowest time complexity of the others. Furthermore, GA demonstrated the greatest degree of temporal complexity. As a result, using the PSO method produces different results when repeating the same algorithm with low reliability in terms of locating the optimal location.
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A fuzzy project buffer management algorithm: A case study in the construction of a renewable project
(Taylor & Francis, 2022-03-09)
One of the major problems with projects is that they are not completed according to schedule. Uncertainty always exists at the heart of real-world project scheduling problems. This paper introduces a fuzzy project buffer management (FPBM) algorithm which is a combination of the adaptive procedure with resource tightness (APRT) and fuzzy failure mode and effects analysis (FFMEA) methods. This paper aims to present an efficient model for project buffer sizing by taking FFMEA into account to reach a more realistic schedule. In this research, for increasing the efficiency of the APRT method, the FFMEA technique is simultaneously applied with them. This research was carried out as a case study in a renewable energy (RE) project. The methodology of this research consists of two phases. The first phase is the implementation of the APRT buffer sizing method. In the second phase of the research methodology, the fuzzy FMEA method is implemented. To validate the proposed model, the results are compared to several buffer management models proposed recently. Also, the results were compared with the results of similar projects. The findings show that considering the fuzzy FMEA technique in the APRT method, a more realistic schedule was obtained in this project.
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A Novel Bi-Objective Integrated Model for Routing and Scheduling Problem Considering Traceability
(RELX Group (Netherlands), 2025-01-01)
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