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Showing 3 results for Nsga-Ii
Mr. Mahdi Saadat, Dr. Iraj Mahdavi, Dr. Mohammad Mahdi Paydar, Mrs. Sara Firouzian,
Volume 10, Issue 2 (9-2019)
Abstract
Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems
Ladan-Al-Sadat Mousavi, Fariborz Jolai,
Volume 14, Issue 1 (6-2023)
Abstract
This paper considers the optimization problem of scheduling jobs with identical sizes on a single batch processing machine. The jobs are divided into some incompatible families where each family contains the jobs with the same processing times and the jobs from different families could not proceed in the same batch. Our optimization problem has two objectives. The first objective is minimizing the weighted number of tardy jobs regarding the due dates of jobs given by customers. The second one aims to minimize operations costs by finding the schedules with the minimum electrical cost consummation under the Time-of-Use tariff policy. A two-objective mixed integer programming mathematical programming is proposed to find optimal solutions for small-size instances of the problem. To solve the medium and large-scale size of the problem, two meta-heuristic algorithms NSGA-II and MOPSO are proposed. The Computational experiments results show that two solution algorithms are capable to find near-optimal solutions at a reasonable computational time. The MOPSO algorithm generate more diverse solutions in less computational time comparing with NSGA II algorithm. But the quality of the solutions obtained by NSGA II algorithm are superior to the ones obtained by MOPSO.
O. Keramatlou, Dr N. Javadian, H. Didehkhani, M. Amirkhan,
Volume 14, Issue 2 (12-2023)
Abstract
In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1) the type of expected movement and 2) expected coverage. To this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.
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