The Research on the Optimization of Transportation Routing for Fresh Food by Improved Genetic Algorithm

2012 ◽  
Vol 178-181 ◽  
pp. 1790-1796 ◽  
Author(s):  
Ying Wu ◽  
Zi Bo Meng ◽  
Min Peng
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Customer Satisfaction ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Transport Services ◽  
Fresh Food ◽  
Transportation Routing

In this paper, we research the problem of transportation routing for fresh food. We analyzed the limit of soft and hard time windows in transportation and formed the time window with fuzzy appointment based on customer satisfaction. The optimization of transportation routes mathematical model was structured. The improved genetic algorithm has been applied to matlab progam. This progam has found the optimal solution in the model. We used a case to prove the feasibility of the model and the algorithm. It has twelve customers and one DC need to transport services. The mathematical model is to simulate the transport of fresh food within realistic.The transportation routing is designed to improve customer satisfaction and reduce transportation costs.

scholarly journals MULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM

2018 ◽  
Vol 48 (3) ◽  
pp. 151-156
Author(s):  
S. WU ◽  
C. CHEN
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Population Diversity ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Routing Optimization

In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.

scholarly journals Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huixian Wei ◽  
Jia Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Mobile Computing ◽  
Optimal Solution ◽  
Least Square Method ◽  
Search Space ◽  
Least Square ◽  
Improved Genetic Algorithm ◽  
Operation Rate ◽  
The Individual

In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

scholarly journals An Improved Genetic Algorithm for Vehicle Routing Problem Pick-up and Delivery with Time Windows

2021 ◽  
Vol 22 (1) ◽  
pp. 1-17
Author(s):  
Muhammad Faisal Ibrahim ◽  
M.M Putri ◽  
D Farista ◽  
Dana Marsetiya Utama
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Closing Time ◽  
Routing Problem ◽  
Logistics Company ◽  
Fitness Value

Vehicle Routing Problem (VRP) has many applications in real systems, especially in distribution and transportation. The optimal determination of vehicle routes impacts increasing economic interests. This research aims to find the optimal solution in Vehicle Routing Problem Pick-up and Delivery with Time Windows (VRPPDTW).  Targets of this problem included reducing distance travel and penalties. Three penalties that were considered are a capacity penalty, opening time capacity, and closing time capacity. An improved genetic algorithm was developed and used to determine the vehicle route.  There were one main depot and 42 customers. This research raised the problem of a shipping and logistics company. Analysis of the results showed that the proposed route obtained from improved genetic algorithms (GA) was better than the existing route and previous algorithm. Besides, this research was carried out an analysis on the effect of the number of iterations on distance traveled, the number of penalties, and the fitness value. This algorithm could be applied in VRPPDTW and produces an optimal solution.

Improved Genetic Algorithm of Vehicle Routing Problems with Time Window for Military Logistic Distribution

2011 ◽  
Vol 135-136 ◽  
pp. 585-591
Author(s):  
Zhi Gang Zhang ◽  
Yan Cheng Gong
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Vehicle Scheduling ◽  
Logistic Distribution ◽  
Minimum Length ◽  
Scheduling Problems ◽  
Distribution Centers ◽  
Improved Genetic Algorithm ◽  
Algorithm Efficiency

By changing the constrain conditions of delivery time windows and vehicle capacities to objective function, A vehicle scheduling model was built up based on minimum length of total transportation distance, which included penalty function terms of time window and vehicle capacity constrains, and the model characteristics and application prospects was analyzed. A improved Genetic Algorithm program was put forward to solve the model, in which a chromosome coding suitable to describe delivery routes was designed, a suitable-degree function was proposed, and a reproduction operator, a crossover operator and a mutation operator were constructed. An example was given to demonstrate feasibility of the algorithm. The study indicates that the Algorithm has higher algorithm efficiency and can effectively solve vehicle scheduling problems of military distribution centers.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Routing Problem

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

scholarly journals Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Zheng-kun Zhang ◽  
Chang-feng Zhu ◽  
Qing-rong Wang ◽  
Jia-shan Yuan
Keyword(s):  
Genetic Algorithm ◽  
Prospect Theory ◽  
Decision Maker ◽  
Transportation Network ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Decision Behavior ◽  
Bidirectional Search ◽  
Robustness Optimization

This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.

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