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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: NSGA-II,Bi-objective Optimization,Distribution centers,capacitated allocation,non-dominated sorting ant colony optimization
- چکیده:
- چکیده انگلیسی: Distribution centers (DCs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimization tool for solving this problem. The proposed methodology is based on a new variant of ant colony optimization (ACO) specialized in multi-objective optimization problem. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this purpose. For ensuring the robustness of the proposed method and giving a practical sense of this study, the computational results are compared with those obtained by NSGA-II algorithm. Results show that both NSACO and NSGA-II algorithms can yield an acceptable number of non-dominated solutions. In addition, the results show while the distribution of solutions in the trade-off surface of both NSACO and NSGA-II algorithms do not differ significantly, the computational CPU time of NSACO is considerably lower than that of NSGA-II. Moreover, it can be seen that the fast NSACO algorithm is more efficient than NSGA-II in the viewpoint of the optimality and convergence.
- انتشار مقاله: 07-12-1392
- نویسندگان: Jafar Bagherinejad,Mina Dehghani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Project scheduling,Multi- mode activities,Minimum and maximum time lags,Two-stage genetic algorithm
- چکیده:
- چکیده انگلیسی: In this study, we present a MRCPSP/max (Multi-mode Resource-Constrained Project Scheduling Problem with Minimum and Maximum time lags) model with minimization tardiness costs and maximization earliness rewards of activities as objective. The proposed model is nearby to real-world problems and has wide applications in various projects. This problem is not available in the literature exactly and we developed it for the first time. In order to solve this problem, we developed a two-stage genetic algorithm. In the first stage, the main problem is simplified, through applying a genetic algorithm, in which each activity has only one executive mode. In the second phase, with developing another genetic algorithm, the best answer of the problem is achieved. Each phase has its own codification, fitness function, crossover operator and mutation operator. Finally, the computational results obtained from the algorithms of this research, which was written in MATLAB programming language, was compared with the results existing in the project scheduling problems library (PSPLIB). The findings show that, our algorithm improved some of the best solutions, recorded in the PSPLIB.
- انتشار مقاله: 09-02-1391
- نویسندگان: Jafar Bagherinejad,Fariborz Jolai,Zahra Rafiee Majd
- مشاهده