The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

Logistics Distribution Routing Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1740-1743 ◽  
Author(s):  
Zhang Chun Hua ◽  
Hua Xin ◽  
Zhang Wei
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Memory Function ◽  
Memory Device ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic simulated annealing algorithm in solving the logistics distribution routing problem, can not only get a higher qualified solution, but can also significantly reduce the evolutionary generation that algorithm requires, and obtain solution to the problem in less time.

Solving a multi-objective multi-depot stochastic location-routing problem by a hybrid simulated annealing algorithm

2009 ◽  
Vol 223 (8) ◽  
pp. 1045-1054 ◽  
Author(s):  
H A Hassan-Pour ◽  
M Mosadegh-Khah ◽  
R Tavakkoli-Moghaddam
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Genetic Operators ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Multi Objective ◽  
Location Routing ◽  
Proposed Model ◽  
Stochastic Location

This paper presents a novel mathematical model for a stochastic location-routing problem (SLRP) that minimizes the facilities establishing cost and transportation cost, and maximizes the probability of delivery to customers. In this proposed model, new aspects of a location-routing problem (LRP), such as stochastic availability of facilities and routes, are developed that are similar to real-word problems. The proposed model is solved in two stages: (i) solving the facility location problem (FLP) by a mathematical algorithm and (ii) solving the multi-objective multi-depot vehicle routing problem (MO-MDVRP) by a simulated annealing (SA) algorithm hybridized by genetic operators, namely mutation and crossover. The proposed SA can find good solutions in a reasonable time. It solves the proposed model in large-scale problems with acceptable results. Finally, a trade-off curve is used to depict and discuss a large-sized problem. The associated results are compared with the results obtained by the lower bound and Lingo 8.0 software.

Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation

2014 ◽  
Vol 1022 ◽  
pp. 269-272
Author(s):  
Ling Li Zhu ◽  
Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Clustering Analysis ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Initial Condition ◽  
Ct Image ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging, and it can effectively avoid appearing precocity and plunging into local optimum. The example shows that the method is feasible, and better segmentation results have got to satisfy the request for 3D reconstruction, compared with k-means image segmentation and genetic algorithm based image segmentation.

Hybrid Genetic Simulated Annealing Algorithm with its Application in Vehicle Routing Problem with Time Windows

2010 ◽  
Vol 148-149 ◽  
pp. 395-398
Author(s):  
Qiang Zhang ◽  
Qing Guo Lin ◽  
Qin He Zhang ◽  
Ji Chen Fang ◽  
Zhan Gen Wang ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Set Up ◽  
Delayed Time

Under the situations of distribution center and customer demand, a mathematical model of Vehicle Routing Problem with Time Windows(VRPTW) is set up, where the main factors of less total distance of vehicles driving and less delayed time of vehicles are considered. For the "premature" convergence in Genetic Algorithms, Simulated Annealing Algorithm is introduced, and GSA is designed to optimize and analyse the VRPTW examples. It is shown that the performance of GSA is better than Genetic Annealing(GA).

scholarly journals A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yanhui Li ◽  
Hao Guo ◽  
Lin Wang ◽  
Jing Fu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Routing ◽  
Logistics System ◽  
Routing Problem ◽  
Problem Model ◽  
Inventory Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1919-1923
Author(s):  
Zhan Zhong Wang ◽  
Jing Fu ◽  
Lan Fang Liu ◽  
Rui Rui Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Final Optimization ◽  
Packing Optimization

In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.

Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

2011 ◽  
Vol 243-249 ◽  
pp. 1963-1967
Author(s):  
Qing Chen Zhang ◽  
Quan Sheng Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Network Model ◽  
Neural Network Model ◽  
Simulated Annealing Algorithm ◽  
Suspension Bridge ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Bp Neural Network Model

According to the characteristics of self-anchored suspension bridge, a new method to detect damage is introduced in this paper.It works in two stages.First, a BP neural network model is built to predict damaged position. Next, based on the characteristics of genetic algorithm and simulated annealing algorithm, a new approach, genetic-simulated annealing algorithm, is put forward to identify damage extent of detected positions. Compared with the traditional genetic algorithm, the global convergence effect of this algorithm is enhanced by using of the Metropolis acceptance rule of the simulated annealing algorithm in the searching process.

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