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.

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.

Research on Multitask Collaborative Scheduling Problem with Heuristic Strategies

2011 ◽  
Vol 66-68 ◽  
pp. 758-763
Author(s):  
Fan Zhang ◽  
Gui Fa Teng ◽  
Jian Bin Ma ◽  
Jie Yao
Keyword(s):  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
Agricultural Machinery ◽  
Start Time ◽  
Farm Machinery ◽  
Scheduling Scheme ◽  
Scheduling Model ◽  
Heuristic Strategies ◽  
Collaborative Scheduling ◽  
Minimal Resource

According to problems existed in the current farm machinery scheduling process, a new farm machinery scheduling scheme is adopted in this dissertation. The collaborative scheduling model of farm machinery is established and multitask collaborative scheduling algorithm is designed through analyzing the differences between Vehicle Scheduling Problem and agricultural machinery scheduling in the dissertation. Earliest Start Time First and minimal resource allocated capacity first strategies are used in the farm machinery scheduling. The algorithm is useful for the case of machinery owner with sufficient farm machinery. The experiment proves that the collaborative scheduling algorithm is more effective than the serial scheduling algorithm.

The Research on Electric Bus Operation Intelligence Scheduling Model and Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 1330-1334
Author(s):  
Song Gao ◽  
Pei Pei Zhang ◽  
De Rong Tan ◽  
Xiao Lin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Discharge Time ◽  
Charging Time ◽  
Scheduling Model ◽  
Bus Scheduling ◽  
Electric Bus ◽  
Driving Range ◽  
Operation Scheduling ◽  
Full Consideration

Electric bus is different from traditional bus. Its operation scheduling is constrained by the charge time, discharge time and driving range. On the base of full consideration of the electric bus driving range and charging time, the electric bus scheduling model is built. Then a genetic algorithm is selected to solve the model. Finally, an electric bus route in Zibo City is taken as an example, to adopt modeling and solving. The results verify the applicability of the model and algorithm.

AN INTEGRATING SCHEDULING MODEL FOR MIXED CROSS-OPERATION IN CONTAINER TERMINALS

Transport ◽  
2012 ◽  
Vol 27 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Chao Chen ◽  
Qingcheng Zeng ◽  
Zhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travel Distance ◽  
Container Terminals ◽  
Numerical Tests ◽  
Scheduling Model ◽  
Scheduling Method ◽  
Equipment Configuration ◽  
Operation Scheduling ◽  
Operation Cost

This paper focuses on the optimization of operation scheduling in container terminals based on mix cross-operation. Mix cross-operation is a scheduling method which allows yard trailers to be shared by different yard cranes in different berths to decrease yard trailers’ travel distance. An integrating scheduling model that optimizes the three key and interrelated issues, namely, berth assignment, equipment configuration and trailer routing are proposed. To solve the model, a bi-level genetic algorithm is designed. Numerical tests show that integrating scheduling method can reduce operation cost of container terminals significantly and mix cross-operation can decrease yard trailers’ empty travel distance to a great extent.

Research of Vehicle Scheduling with Time Window Based on Genetic Algorithm

2013 ◽  
Vol 409-410 ◽  
pp. 1307-1310
Author(s):  
Xiao Rong Zhou ◽  
Meng Tian Song ◽  
Yu Ling Zhang
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Distribution Center ◽  
Optimal Scheduling ◽  
Vehicle Scheduling ◽  
Search Process ◽  
Long Distance ◽  
Soft Time Windows ◽  
Scheduling Model

This paper based on the genetic algorithm,introduced part search process and respectively established the mathematical scheduling model of full loads vehicle optimal scheduling with soft time windows and of non-full loads on the basis of the long distance logistics transportation situation of some companys distribution center in Shenzhen. Then programmed to achieve the scheduling of multi-vehicle touting and selection, and conducted example analysis.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

Parallel Scheduling Algorithms Investigation of Support Strict Resource Reservation from Grid

2014 ◽  
Vol 519-520 ◽  
pp. 108-113 ◽  
Author(s):  
Jun Chen ◽  
Bo Li ◽  
Er Fei Wang
Keyword(s):  
Parallel Computing ◽  
Resource Utilization ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Resource Reservation ◽  
Parallel Scheduling ◽  
Scheduling Model ◽  
Computing Grid ◽  
Simulation Results ◽  
Reservation Request

This paper studies resource reservation mechanisms in the strict parallel computing grid,and proposed to support the parallel strict resource reservation request scheduling model and algorithms, FCFS and EASY backfill analysis of two important parallel scheduling algorithm, given four parallel scheduling algorithms supporting resource reservation. Simulation results of four algorithms of resource utilization, job bounded slowdown factor and the success rate of Advanced Reservation (AR) jobs were studied. The results show that the EASY backfill + firstfit algorithm can ensure QoS of AR jobs while taking into account the performance of good non-AR jobs.

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