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\%.
Fatemeh Ganji; Amir Jamali
Abstract
In this study, single machine scheduling with flexible maintenance is investigated with non-resumable jobs by minimizing the weighted number of tardy jobs. It is assumed that the machine stops for a constant interval time during the scheduling period to perform maintenance. In other words, the starting ...
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In this study, single machine scheduling with flexible maintenance is investigated with non-resumable jobs by minimizing the weighted number of tardy jobs. It is assumed that the machine stops for a constant interval time during the scheduling period to perform maintenance. In other words, the starting time of maintenance is the decision variable. By reviewing the literature, we noticed that this problem has not been studied yet. Initially, it is proved that the problem is NP-hard. Then, a mathematical model is proposed and solved by the GAMS software. Because of the long time for solving the problem with an exact method, we develop a heuristic algorithm. To evaluate the efficiency of the proposed algorithm, 696 test problems with different sizes of the problem in the range from 1 to 2000 jobs, are generated. The computational results demonstrate that the average error of solution is 10.93\%.