scholarly journals OPTIMIZATION MODEL OF SELECTIVE CUTTING FOR TIMBER HARVEST PLANNING BASED ON A SIMULATED ANNEALING APPROACH

2014 ◽  
Vol 10 (1) ◽  
pp. 54-65 ◽  
Author(s):  
Munaisyah Abdullah ◽  
Salwani Abdullah ◽  
Abdul Razak Hamdan ◽  
Roslan Ismail
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Timber Harvest ◽  
Selective Cutting

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

scholarly journals Research on Flexible Equipment Maintenance Process Scheduling Problems Based on HNSGSA Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Weixing Song ◽  
Jianshe Kang ◽  
Jingjing Wu ◽  
Hui Jia ◽  
Huiqiang Chang
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Simulated Annealing Algorithm ◽  
Equipment Maintenance ◽  
Scheduling Problems ◽  
Process Scheduling ◽  
Nsga Ii ◽  
Maintenance Work ◽  
Annealing Algorithm ◽  
Maintenance Process

The characteristics of military equipment maintenance work are analyzed. According to the actual needs of the army, the optimization objective is designed, and a multiobjective flexible maintenance process optimization model is built based on the maintenance business organization process. Combining the advantages of NSGA-II algorithm and the simulated annealing algorithm, this paper proposes a novel improved HNSGSA algorithm, of which algorithm flow is detailed. In accordance with the requirements of the optimization model, this paper also specifically designs the coding methods of the process sequence, the equipment selection and the process scheduling, and the corresponding cross mutation method. The feasibility of the built model is verified by the actual data of maintenance business. And, the superiority, accuracy, and effectiveness of the proposed algorithm are further validated by the comparison with the NSGA-II algorithm and the simulated annealing algorithm, providing a scientific reference for the army to carry out equipment maintenance.

scholarly journals Metaheuristic with Cooperative Processes for the University Course Timetabling Problem

2022 ◽  
Vol 12 (2) ◽  
pp. 542
Author(s):  
Martín H. Cruz-Rosales ◽  
Marco Antonio Cruz-Chávez ◽  
Federico Alonso-Pecina ◽  
Jesus del C. Peralta-Abarca ◽  
Erika Yesenia Ávila-Melgar ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
The Other ◽  
Course Timetabling ◽  
University Course Timetabling ◽  
Point To Point ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University ◽  
Cooperative Processes

This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared.

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