Multiobjective Optimization Model of Residential Spatial Distribution

The concept of jobs-housing balance has been adopted as an effective way to alleviate the traffic congestion, especially in metropolis. A multiobjective model of residential spatial distribution (MOOMRSD) is developed in this paper to address the problem of how to locate the housing in a reasonable way when the workplace location is given. Three objectives are integrated into the MOOMRSD and they are (1) minimizing the average commute cost from residence to workplace; (2) minimizing the total travel time of citizens; and (3) maximizing the aggregate utility and social benefit. In addition, a multiobjective genetic algorithm (MOGA) is proposed to figure out a satisfactory solution to the MOOMRSD. Finally, Both MOOMRSD and MOGA are applied into two cases.

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A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

Mathematical Problems in Engineering ◽

10.1155/2015/349781 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Qiang Long ◽

Changzhi Wu ◽

Xiangyu Wang ◽

Lin Jiang ◽

Jueyou Li

Keyword(s):

Genetic Algorithm ◽

Multiobjective Optimization ◽

Optimization Problems ◽

Selection Procedure ◽

Population Based ◽

Pareto Solution ◽

Multiobjective Genetic Algorithm ◽

Trade Offs ◽

Multiobjective Optimization Problems ◽

Optimum Order

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

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Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem

Journal of Advanced Transportation ◽

10.1155/2019/3867874 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Wei Zhang ◽

Ruichun He ◽

Yong Chen ◽

Mingxia Gao ◽

Changxi Ma

Keyword(s):

Genetic Algorithm ◽

Multiobjective Optimization ◽

Optimization Model ◽

Travel Distance ◽

Important Influence ◽

Pricing Model ◽

Fair Pricing ◽

Maximum Value ◽

Pricing Scheme

This paper builds a multiobjective optimization model for solving the taxi carpooling with detour problem and designs a genetic algorithm to determine a fair pricing scheme for riders and drivers. The researches show that it is feasible to share a taxi with detour. It is the key to determine appropriate carpooling payment ratio and detour carpooling payment ratio. The ratio of detour distance to travel distance has an important influence on detour carpooling. It should be limited to less than certain values. Payment ratios and the maximum value of the ratio of detour distance to travel distance are determined by the method proposed in this paper. The method can ensure benefits of passengers and drivers, which makes detour carpooling a reality. These conclusions and the method have a certain guiding significance for formulating taxi policy.

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Structural Strength and Reliability Analysis of Important Parts of Marine Diesel Engine Turbocharger

Mathematical Problems in Engineering ◽

10.1155/2021/5547762 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Lin Lei ◽

Ming-ze Ding ◽

Hong-wei Hu ◽

Yun-xiao Gao ◽

Hai-lin Xiong ◽

...

Keyword(s):

Genetic Algorithm ◽

Diesel Engine ◽

Multiobjective Optimization ◽

Turbine Blade ◽

Structural Strength ◽

Turbine Disk ◽

Marine Diesel Engine ◽

Nsga Ii ◽

Multiobjective Genetic Algorithm ◽

Marine Diesel

Supercharging is the main method to improve the output power of marine diesel engines. Nowadays, most marine diesel engines use turbocharging technology, which increases the air pressure and density into the cylinder and the amount of fuel injected correspondingly so as to achieve the purpose of improving the power. In a marine diesel engine, the turbocharger has become an indispensable part. The performance of turbochargers in a harsh working environment of high temperature and high pressure for a long time will directly affect the performance of diesel engine. Based on the market feedback data from manufacturers, the failure modes of compressor impeller, turbine blade, and turbine disk of marine diesel turbocharger are analyzed, and the statistical model of random factors is established. Using DOE design, the structural strength simulation data of 46 compressors and 62 turbines are obtained, and the response surface model is constructed. On this basis, Monte Carlo sampling is carried out to analyze the reliability of the compressor and turbine. The reliability of the compressor is good, while that of the turbine disk is 0.943 and that of the turbine blade is 0.96, which still has the potential of reliability optimization space. Therefore, a multiobjective optimization method based on the NSGA-II genetic algorithm is proposed to obtain the multiobjective optimization scheme data with the reliability and processing cost of turbine disk and blade as the objective function. After optimization, the reliability of turbine disk and blade is 1, the stress value of turbine blade is optimized by 4.7941%, the stress value of turbine disk is optimized by 3.0136%, the machining cost of the turbine blade is reduced by 15.5087%, and the machining cost of turbine disk is reduced by 3.9907%. At the same time, it is verified by simulation, the data based on NSGA-II multiobjective genetic algorithm are more accurate and have practical engineering reference value. The optimized data based on NSGA-II multiobjective genetic algorithm are used to manufacture new turbine samples, and the accelerated test of simulation samples is carried out. The cycle life of the optimized turbine can reach 101,697 cycles and 118,687 cycles, which is 51.75% and 77.11% longer than that of the unoptimized turbine. It can be seen that the optimized turbine can meet the requirements of the reliability index while reducing the manufacturing cost.

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Warehouse Optimization Model Based on Genetic Algorithm

Mathematical Problems in Engineering ◽

10.1155/2013/619029 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Guofeng Qin ◽

Jia Li ◽

Nan Jiang ◽

Qiyan Li ◽

Lisheng Wang

Keyword(s):

Genetic Algorithm ◽

Multiobjective Optimization ◽

Optimization Model ◽

Average Cost ◽

Average Distance ◽

Optimal Solution ◽

Model Based ◽

Local Optimal Solution ◽

The Cost

This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.

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