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Titlebook: Methods and Models in Mathematical Programming; S. A. MirHassani,F. Hooshmand Book 2019 Springer Nature Switzerland AG 2019 Mathematical m

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21#
發(fā)表于 2025-3-25 06:09:59 | 只看該作者
Qualitative Comparison of Models,l show a formulation of a specific problem. For example, in ?ncan et al. (2009), more than 10 different formulations are presented for the traveling salesman problem (TSP). Different models of a given problem are expected to be different in the formulation, but they agree with the optimal solution.
22#
發(fā)表于 2025-3-25 10:02:00 | 只看該作者
Applications of Mathematical Modeling,chapters are addressed. For each application, the problem is defined, the model’s components are introduced and then, the model is presented in the general form. Afterward, it is solved on a given set of data with the aid of computer, and the results are analyzed.
23#
發(fā)表于 2025-3-25 13:13:58 | 只看該作者
set of employees from a set of available workers and to assign this staff to the jobs to be performed. A workforce planning problem is very complex and requires special algorithms to be solved. The complexity of this problem does not allow the application of exact methods for instances of realistic
24#
發(fā)表于 2025-3-25 17:58:53 | 只看該作者
25#
發(fā)表于 2025-3-25 22:09:23 | 只看該作者
S. A. MirHassani,F. Hooshmandcontributions from the 15th International Workshop on Comput.The book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2022. Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profi
26#
發(fā)表于 2025-3-26 00:56:23 | 只看該作者
27#
發(fā)表于 2025-3-26 04:41:25 | 只看該作者
28#
發(fā)表于 2025-3-26 10:45:31 | 只看該作者
29#
發(fā)表于 2025-3-26 14:46:50 | 只看該作者
S. A. MirHassani,F. Hooshmandign, simulation, and implementation more and more challenging. Memory management is among the main challenge that electronic designers have to face. In fact, it impacts heavily the main cost metrics, including area, performance (real-time aspect) and energy consumption, of modern-day electronic devi
30#
發(fā)表于 2025-3-26 18:17:15 | 只看該作者
inks) between them. In this paper the Structure Optimization Genetic Algorithm (SOGA) for FCMs learning is presented for prediction of indoor temperature. The proposed approach allows to automatically construct and optimize the FCM model on the basis of historical multivariate time series. The SOGA
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