Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

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Modeling and Optimization of a Multi-Objective Ridesharing Problem in the Case of Medical Waste

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1198.0882s819 ◽

2019 ◽

Vol 8 (2S8) ◽

pp. 1911-1918 ◽

Cited By ~ 3

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Medical Waste ◽

Evolution Strategy ◽

World Community ◽

Multi Objective ◽

Exact Approach ◽

Serious Infections ◽

The World ◽

Medical Wastes

Medical wastes is now a major concern of the world community and more particularly that of Moroccans. Indeed, these wastes, classified as hazardous products, are the source of serious infections, contamination of groundwater and air pollution. Through this paper, we encouraged the use of ridesharing to cope with the risks and costs arising from the logistics of these medical wastes. Thus, we have proposed a mathematical model that governs the multi-objective nature of this logistics and the various constraints associated with it. Since the exact approach had difficulties in large instances, we proposed the Genetic Algorithm and Evolution Strategy as metaheuristic to solve the model. The Evolution Strategy showed its efficiency and stability and therefore we have demonstrated through this metaheuristic the possibility of a compromise between the main objectives of our model.

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Research on Multi-Objective Transmission Planning in Electricity Markets

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.516-517.1429 ◽

2012 ◽

Vol 516-517 ◽

pp. 1429-1432

Author(s):

Yang Liu ◽

Xu Liu ◽

Feng Xian Cui ◽

Liang Gao

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Genetic Algorithms ◽

Electricity Markets ◽

Optimization Problem ◽

Nsga Ii ◽

Transmission Planning ◽

Multi Objective ◽

Complex Optimization ◽

The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

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Multi-objective Pick-up Point Location Optimization Based on a Modified Genetic Algorithm

Communications in Computer and Information Science - Bio-inspired Computing: Theories and Applications ◽

10.1007/978-981-15-3425-6_60 ◽

2020 ◽

pp. 751-760

Author(s):

Shuai Chen ◽

Bin Cao ◽

Ruichang Li

Keyword(s):

Genetic Algorithm ◽

Point Location ◽

Location Optimization ◽

Multi Objective ◽

Modified Genetic Algorithm

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Genetic Algorithm for the Multi-Objective Optimization of Product Gene

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.116 ◽

2012 ◽

Vol 6-7 ◽

pp. 116-121

Author(s):

Qing Song Ai ◽

Zhou Liu ◽

Yan Wang

Keyword(s):

Genetic Algorithm ◽

Genetic Engineering ◽

Manufacturing Industry ◽

Optimization Problems ◽

Rapid Development ◽

Optimal Solution ◽

Adaptive Mutation ◽

Multi Objective Optimization ◽

Multi Objective ◽

Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

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Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm

Concurrent Engineering ◽

10.1177/1063293x18796365 ◽

2018 ◽

Vol 26 (4) ◽

pp. 367-377 ◽

Cited By ~ 3

Author(s):

Yu-ling Jiao ◽

Xiao-cui Xing ◽

Peng Zhang ◽

Liang-cheng Xu ◽

Xin-Ran Liu

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Dynamic Simulation ◽

Population Genetic ◽

Physical Parameters ◽

Location Allocation ◽

Multi Objective ◽

Storage Location ◽

Population Genetic Algorithm ◽

The Mathematical Model

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound–inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound–inbound dynamic simulation.

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