Fatemeh Ganji; Zahrasadat Zamani
Abstract
Optimization of inventory costs is the most important goal in industries. But in many models, the constraints are considered simple and relaxed. Some actual constraints are to consider the combinatorial production and purchase models in multi-products environment. The purpose of this article is to improve ...
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Optimization of inventory costs is the most important goal in industries. But in many models, the constraints are considered simple and relaxed. Some actual constraints are to consider the combinatorial production and purchase models in multi-products environment. The purpose of this article is to improve the efficiency of inventory management and find the economic order quantity and economic production quantity that can minimize the cost of inventory and customer satisfaction. In this study, the models with these targets in combinatorial production and purchase systems with the assumption the warehouse and budget constraints are proposed. Since a long time for solving the problem with an exact method is required, we develop a genetic algorithm. To evaluate the efficiency of the proposed algorithm, test problems with different sizes of the problem in the range from 1 to 2000 jobs, are generated. The results show that the genetic method is efficient to determine economic order quantity and economic production quantities. The computational results demonstrate that the average error of the solution is 10.93\%.
A. Ghodousian; Abolfazl Javan; Asieh Khoshnood
Abstract
Yager family of t-norms is a parametric family of continuous nilpotent t-norms which is also one of the most frequently applied one. This family of t-norms is strictly increasing in its parameter and covers the whole spectrum of t-norms when the parameter is changed from zero to infinity. In this paper, ...
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Yager family of t-norms is a parametric family of continuous nilpotent t-norms which is also one of the most frequently applied one. This family of t-norms is strictly increasing in its parameter and covers the whole spectrum of t-norms when the parameter is changed from zero to infinity. In this paper, we study a nonlinear optimization problem where the feasible region is formed as a system of fuzzy relational equations (FRE) defined by the Yager t-norm. We firstly investigate the resolution of the feasible region when it is defined with max-Yager composition and present some necessary and sufficient conditions for determining the feasibility and some procedures for simplifying the problem. Since the feasible solutions set of FREs is non-convex and the finding of all minimal solutions is an NP-hard problem, conventional nonlinear programming methods may involve high computation complexity. For these reasons, a method is used, which preserves the feasibility of new generated solutions. The proposed method does not need to initially find the minimal solutions. Also, it does not need to check the feasibility after generating the new solutions. Moreover, we present a technique to generate feasible max-Yager FREs as test problems for evaluating the performance of the current algorithm. The proposed method has been compared with Lu and Fang’s algorithm. The obtained results confirm the high performance of the proposed method in solving such nonlinear problems.