scholarly journals SIMULATED ANNEALING GENETIC ALGORITHM-BASED HARVESTER OPERATION SCHEDULING MODEL

2021 ◽  
pp. 249-260
Author(s):  
Qingkai Zhang ◽  
Guangqiao Cao ◽  
Junjie Zhang ◽  
Yuxiang Huang ◽  
Cong Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Operating Cost ◽  
Optimal Solution ◽  
Agricultural Machinery ◽  
Simulated Annealing Genetic Algorithm ◽  
Scheduling Model ◽  
Operation Scheduling

To address problems involving the poor matching ability of supply and demand information and outdated scheduling methods in agricultural machinery operation service, in this study, we proposed a harvester operation scheduling model and algorithm for an order-oriented multi-machine collaborative operation within a region. First, we analysed the order-oriented multi-machine collaborative operation within the region and the characteristics of agricultural machinery operation scheduling, examined the revenue of a mechanized harvesting operation and the components of each cost, and constructed a harvester operation scheduling model with the operation income as the optimization goal. Second, we proposed a simulated annealing genetic algorithm-based harvester operation scheduling algorithm and analysed the validity and stability of the algorithm through experimental simulations. The results showed that the proposed harvester operation scheduling model effectively integrated the operating cost, transfer cost, waiting time cost, and operation delay cost of the harvester, and the accuracy of the harvester operation scheduling model was improved; the harvester operation scheduling algorithm based on simulated annealing genetic algorithm (SAGA) was able to obtain a global near-optimal solution of high quality and stability with high computational efficiency.

scholarly journals Research on the Scheduling of Tractors in the Major Epidemic to Ensure Spring Ploughing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Cong ◽  
Hu Jianping ◽  
Zhang Qingkai ◽  
Zhang Meng ◽  
Li Yibai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Scheduling Algorithm ◽  
Utilization Efficiency ◽  
Agricultural Machinery ◽  
Scheduling Scheme ◽  
Transfer Distance ◽  
Scheduling Model ◽  
Operation Scheduling

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.

Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Computational Intelligence in Cross Docking

2014 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Bo Xing
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Computational Intelligence ◽  
Simple Concept ◽  
Simulated Annealing Genetic Algorithm ◽  
Cross Docking ◽  
Open Questions ◽  
Key Issues

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

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