Strategic Planing
Sajad Moradi
Abstract
Purpose: This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon.Methodology: In this study, a new formulation is presented as a mixed integer ...
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Purpose: This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon.Methodology: In this study, a new formulation is presented as a mixed integer linear programming model that could find the optimum solution quickly. In the new proposed formulation, some intermediate stages of the breeding chain that do not affect decisions are ignored, and therefore, the size and complexity of the proposed model reduce without compromising the optimality of the answers.Findings: After implementing the proposed model, using different data samples, it can be seen that this model achieves the optimal solution in a short time, including volume and time of spawning in each breeding chain and different periods, harvesting time, and accepting or rejecting the main demands.Originality/Value: In this paper, the issue of scheduling of fish farming chains and sales management, which there are a few studies in this field, has been studied and a new mixed integer linear programming model is presented. Compared to the previous model, this model has more realistic assumptions and less complexity and execution time.
Scheduling Modeling
Mohsen Bagheri; Neda Babaei Meybodi; Amir Hossein Enzebati
Abstract
Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider ...
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Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider energy consumption, completion time and tardiness in the presented Multi-Objective Mixed Integer Programming flow shop scheduling model. After validating the model by solving small-scale numerical examples with Weighted Sum and Epsilon-constraint method in GAMS, the large and medium-scale examples are solved via NSGA-II and SPEA-II metaheuristic-algorithms. The results prove the efficiency of the proposed algorithms.