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 Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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 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.

scholarly journals Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 77
Author(s):  
Afra A. Alabbadi ◽  
Maysoon F. Abulkhair
Keyword(s):  
Task Scheduling ◽  
Fitness Function ◽  
Optimal Solution ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Spatial Crowdsourcing ◽  
Travel Costs ◽  
Multi Objective ◽  
Ranking Strategy

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.

scholarly journals Contention-Aware Mapping and Scheduling Optimization for NoC-Based MPSoCs (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13995-13996
Author(s):  
Yupeng Zhou ◽  
Rongjie Yan ◽  
Anyu Cai ◽  
Yige Yan ◽  
Minghao Yin
Keyword(s):  
Evolutionary Algorithm ◽  
Network On Chip ◽  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Temporal Aspects ◽  
Network Contention ◽  
On Chip ◽  
Constraint Model

We consider spacial and temporal aspects of communication to avoid contention in Network-on-Chip (NoC) architectures. A constraint model is constructed such that the design concerns can be evaluated, and an efficient evolutionary algorithm with various heuristics is proposed to search for better solutions. Experimentations from random benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention.

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.

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