scholarly journals An improved genetic algorithm for low carbon dynamic scheduling in a discrete manufacturing workshop

2021 ◽  
Vol 1820 (1) ◽  
pp. 012111
Author(s):  
Nie Li ◽  
Xiaogang Wang ◽  
Yuewei Bai
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Low Carbon ◽  
Improved Genetic Algorithm ◽  
Discrete Manufacturing

The Genetic Algorithm with Two Heuristic Rules for TSP

2013 ◽  
Vol 694-697 ◽  
pp. 2787-2793
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solutions ◽  
Improved Genetic Algorithm ◽  
Heuristic Rules ◽  
Heuristic Rule ◽  
Hamiltonian Paths ◽  
Sequencing Problem ◽  
Discrete Manufacturing ◽  
Np Complete ◽  
Manufacturing Problems

Many complex discrete manufacturing problems, such as manufacturing sequencing problem or machine scheduling problem etc, can be converted into a general traveling salesman problem (TSP). TSP has been proven to be NP-complete. The genetic algorithm is improved with two heuristic rules for TSP. The first heuristic rule is the four vertices and three lines inequality. It is applied to the local Hamiltonian paths to generate the better solutions. The second heuristic rule is executed to reverse the local Hamiltonian paths, which generates new better solutions. The two heuristic rules coordinate with each other and they are merged into the optimization process of genetic algorithm to improve its performance. The computation results show that the improved genetic algorithm can find the near optimal solutions for most of the TSP instances.

scholarly journals Customer demand-driven low-carbon vehicles combined strategy and route optimisation integrated decision

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanwen Liu ◽  
Xiaobing Liu ◽  
Sardar M. N. Islam ◽  
Xueqiao Yu ◽  
Qiqi Miao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emission ◽  
Operating Cost ◽  
Low Carbon ◽  
Improved Genetic Algorithm ◽  
Customer Demand ◽  
Purchasing Cost ◽  
Combined Strategy ◽  
The Cost ◽  
Energy Type

AbstractWith the optimal operating cost and optimal carbon emission target of the chemical logistics companies, a low-carbon routing optimisation with a multi-energy type vehicle combined problem is proposed by considering the concept of the logistics companies’ low-carbon behaviour. An integrated decision-making of multi-energy type vehicles combined strategy and route optimisation based on customer demand is presented, and an improved genetic algorithm is designed. A case study is then applied based on the data collected from the case research. The effectiveness of the improved genetic algorithm is tested. The two joint objectives of operating cost and carbon emission are examined through the cost analysis of environmental energy vehicles and traditional energy vehicles in different combination scenarios. The case analysis shows that a rational multi-energy type vehicle combination with route optimisation has a significant correlation with the operating cost and carbon emissions, while the environmental vehicle purchasing cost reduction and subsidy policy affect the operating cost.

scholarly journals Dynamic Surgery Scheduling Based on an Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingbing Zhang ◽  
Qiang Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Efficient Solution ◽  
Dynamic Scheduling ◽  
Idle Time ◽  
Scheduling Problem ◽  
Surgery Scheduling ◽  
Programming Formulation ◽  
Improved Genetic Algorithm ◽  
Experienced Nurses

We formulated a new stochastic programming formulation to solve the dynamic scheduling problem in a given set of elective surgeries in the day of operation. The problem is complicated by the fact that the exact surgery durations are not known in advance. Elective surgeries could be performed in parallel in a subset of operating rooms. The appointment times and assignments of surgeries were planned by an experienced nurses in advance. We present a mathematical model to capture the nature of dynamic scheduling problem. We propose an efficient solution based on an improved genetic algorithm (IGA). Our numerical results showed that dynamic scheduling with the IGA improves the resource utilization as measured by surgeon waiting time and operation room idle time.

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.

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