Stochastic location model and solution method based on synthesizing effect and genetic algorithm

2013 ◽  
Author(s):  
Hui-Zi An ◽  
Cui-Min Wang ◽  
Yi Liu ◽  
Wei Li ◽  
Lei Zhou
Keyword(s):  
Genetic Algorithm ◽  
Solution Method ◽  
Location Model ◽  
Stochastic Location

scholarly journals A Facility Location Model for Air Pollution Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
G. Lancia ◽  
F. Rinaldi ◽  
P. Serafini
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Air Quality ◽  
Mathematical Models ◽  
Facility Location ◽  
Large City ◽  
Location Model ◽  
Practical Algorithms ◽  
Pollution Detection ◽  
The City

We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

Research on Mixed Intelligent Arithmetic of Reuse Reverse Logistics Centers' Location Model

2014 ◽  
Vol 945-949 ◽  
pp. 3246-3251 ◽  
Author(s):  
Lu Feng Dai ◽  
Xi Fu Wang
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Stochastic Simulation ◽  
Reverse Logistics ◽  
Random Variables ◽  
Location Model ◽  
Logistics Center ◽  
Center Location

The paper builds the model of direct reuse reverse logistics center location followed by the uncertainty random variables of the demand of retailers and the recovery of collection points. This model assumes that the enterprises are expanding on the traditional network. For the random variables, they will be solved by using stochastic simulation, genetic algorithm and linear programming, and numericai exampie is presented.

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

The Influence of Estimated Pollution Range on the Groundwater Pollution Source Identification Method Based on the Simple Genetic Algorithm

2014 ◽  
Vol 587-589 ◽  
pp. 836-841 ◽  
Author(s):  
Yu Qiao Long ◽  
Chun Yong Wu ◽  
Jian Ping Wang
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Pollution ◽  
Optimization Model ◽  
Solution Method ◽  
Pollution Source ◽  
Local Optimum ◽  
Optimization Approach ◽  
Optimum Location ◽  
Migration Direction ◽  
Slight Movement

The optimization approach is common approach to solve complex PSI issues. Most researches on the optimization approach focus on the solution method of the optimization model and improving modeling efficiency. In this paper, we give our effort on the influence of estimated pollution range on the groundwater PSI problem and discuss the efficiency and accuracy of 1D and 2D PSI problems. The estimated pollution range of PSI problem could affect how much calculated time would be consumed. The bigger the estimated range is, the more time is consumed. Increasing the dimension of the PSI problem will increase the estimated range greatly, and leads to a great time consuming. A slight movement of the estimated source in the direction perpendicular to the major migrate direction leads to big bias between the calculated source location and the real location. The chance that optimization model falls into the local optimum location is growing in the major migration direction.

Research on Genetic Algorithm-Based Solution Method for Variable Cycle Engine Model

2014 ◽  
Vol 668-669 ◽  
pp. 633-636
Author(s):  
Zheng Jia Wu ◽  
Rong Hua Meng ◽  
Ji Li
Keyword(s):  
Complex System ◽  
Genetic Algorithm ◽  
Solution Method ◽  
Initial Value ◽  
Engine Model ◽  
Computation Efficiency ◽  
Implicit Equations ◽  
Newton Raphson ◽  
The Mathematical Model ◽  
Raphson Method

Variable cycle engine is a complex system, which is usually mathematically modeled as a series of multi-dimensional nonlinear implicit equations. Processes for solution of these equations are often complicated; therefore, a genetic algorithm-based method was presented in this paper for the solution of the mathematical model. The method was also evaluated by such parameters as initial value sensitivity, computation efficiency, convergence and stability; and compared with Newton-Raphson method. It shows that genetic algorithm-based method is less sensitive to initial values, more capable in convergent and computing stability than Newton-Raphson method, however more time consuming.

scholarly journals A Solution Method for Large-Scale Unit Commitment Using Genetic Algorithm

1998 ◽  
Vol 118 (4) ◽  
pp. 413-419
Author(s):  
Takeshi Nagata ◽  
Hanzheng Duo ◽  
Hiroshi Sasaki ◽  
Hideki Fujita
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Solution Method ◽  
Unit Commitment ◽  
Scale Unit

scholarly journals Multiobjective Site Selection Model for Emergency Shelter Facilities in Urban Areas

2021 ◽  
Author(s):  
Kuo-Chi Yen ◽  
Weid Chang ◽  
Wu-Chiao Shih
Keyword(s):  
Genetic Algorithm ◽  
Urban Areas ◽  
Location Model ◽  
Emergency Shelter ◽  
Factors Associated ◽  
Facility Locations ◽  
Random Weights ◽  
The Cost ◽  
Cost Factors ◽  
Selection Of

Abstract Industrial and economic development is primarily applied to densely populated urban areas. If a sudden disaster occurs in such areas, the consequences can be severe. Shelter facility location affects the implementation of postdisaster relief work. This study explored residents’ perceived utility of evacuation time, their risk utility for road blocking, and the cost factors associated with constructing shelter facilities in the context of governance. A location model for emergency shelter facilities in cities was established on the basis of the aforementioned factors. Because the resolution of the random-weighted genetic algorithm (RWGA) is susceptible to influence from random weights, a robustness random-weighted method (RRWM) was developed. The validity and feasibility of the location model were examined through numerical analysis. Finally, the convergence of the RRWM was analyzed and compared with that of the RWGA and a single-objective genetic algorithm. The results revealed that the proposed algorithm exhibited satisfactory performance and can assist in evaluation and decision-making related to the selection of urban shelter facility locations.

The Research on Location of Multi-Level Network Distribution Centers

2011 ◽  
Vol 97-98 ◽  
pp. 653-658
Author(s):  
Ying Wu ◽  
Yi Lu ◽  
Yang Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Strategy ◽  
Service Level ◽  
Distribution Center ◽  
Location Model ◽  
Practical Case ◽  
Distribution Centers ◽  
Advantages And Disadvantages ◽  
Multi Level

This paper studies the time matter in distribution center’s service level, states a model’s preconditions and constructs a location model of multi-level disribution center which concerns the time matter. In solution, it studies the advantages and disadvantages of genetic algorithm, proposes a mixed strategy, designs the HGA program and studies the application of HGA in the location model of distribution center. In the end , it shows the practicality and correctness the proposed methods and models by the application in the practical case.

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