A Self-adaptive Greedy Scheduling Scheme for a Multi-Objective Optimization on Identical Parallel Machines

2009 ◽  
pp. 43-55
Author(s):  
Liya Fan ◽  
Fa Zhang ◽  
Gongming Wang ◽  
Bo Yuan ◽  
Zhiyong Liu
Keyword(s):  
Parallel Machines ◽  
Identical Parallel Machines ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Scheduling Scheme ◽  
Self Adaptive ◽  
Greedy Scheduling

scholarly journals Research on urban public bicycle dispatching optimization method

2019 ◽  
Author(s):  
Lin Fei ◽  
Yang Yang ◽  
Wang Shihua ◽  
Xu Yudi ◽  
Ma Hong
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Community Discovery ◽  
The Public ◽  
Multi Objective ◽  
Scheduling Scheme ◽  
Theoretical Support ◽  
The Matrix

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

scholarly journals Multi-Objective Optimization of Workshop Scheduling with Multiprocess Route Considering Logistics Intensity

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 838
Author(s):  
Yu Sun ◽  
Qingshan Gong ◽  
Mingmao Hu ◽  
Ning Yang
Keyword(s):  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Carbon Efficiency ◽  
Scheduling Scheme ◽  
Process Route ◽  
Single Target ◽  
Scheduling Plan ◽  
Route Scheduling ◽  
Mode A

In order to solve the problems of flexible process route and workshop scheduling scheme changes frequently in the multi-variety small batch production mode, a multiprocess route scheduling optimization model with carbon emissions and cost as the multi-objective was established. At the same time, it is considered to optimize under the existing machine tool conditions in the workshop, then the theory of logistics intensity between equipment is introduced into the model. By designing efficient constraints to ensure reasonable processing logic, and then applying multilayer coding genetic algorithm to solve the case. The optimization results under single-target and multi-target conditions are contrasted and analyzed, so as to guide enterprises to choose a reasonable scheduling plan, improve the carbon efficiency of the production line, and save costs.

scholarly journals Urban public bicycle dispatching optimization method

2019 ◽  
Vol 5 ◽  
pp. e224
Author(s):  
Fei Lin ◽  
Yang Yang ◽  
Shihua Wang ◽  
Yudi Xu ◽  
Hong Ma ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Community Discovery ◽  
The Public ◽  
Multi Objective ◽  
Scheduling Scheme ◽  
Theoretical Support ◽  
The Matrix

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%–50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

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