scholarly journals Reverse Logistics Vehicle Routing Optimization Problem Based on Multivehicle Recycling

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhaohui Gao ◽  
Caiyun Ye
Keyword(s):  
Genetic Algorithm ◽  
Reverse Logistics ◽  
Time Windows ◽  
Initial Population ◽  
Delivery Mode ◽  
Fixed Costs ◽  
Improved Genetic Algorithm ◽  
Routing Optimization ◽  
Penalty Costs ◽  
Logistics Process

In the reverse logistics recycling process, considering the diversity of recycling types, a variety of vehicle models are used to meet the reverse logistics recycling requirements. Therefore, this paper considers the fixed costs, transportation costs, carbon emission costs during driving, and time penalty costs in the reverse logistics process under multiple constraints such as multiple vehicle types and time windows in the recycling center. The recovery point is the recovery model, and an improved genetic algorithm is used to solve it. Drawing lessons from the idea of greed, the superiority of the initial population is improved; the entry matrix and the exit matrix of the iterative population are constructed, and the crossover operator is improved based on this, and the forward insertion method is introduced to design the hybrid crossover operation to speed up the population. At the same time, the improvement of the mutation operator is proposed to increase the diversity of the population. The experimental results show that the multivehicle mixed delivery mode can reduce costs more effectively than single-vehicle models, and the improved genetic algorithm has better convergence and stability.

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.

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.

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 Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Research on Vehicle Routing Problem Models and Algorithms of Batteries Logistics Distribution for Electric Vehicle

2013 ◽  
Vol 791-793 ◽  
pp. 1409-1414 ◽  
Author(s):  
Meng Wang ◽  
Kai Liu ◽  
Zhu Long Jiang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Distribution Model ◽  
Initial Population ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Short Period

The battery quick exchange mode is an effective solution to resolve the battery charging problem of electric vehicle. For the electric vehicle battery distribution network with the battery quick exchange mode, the distribution model and algorithm are researched; the general mathematical model to take delivery of the vehicle routing problem with time window (VRP-SDPTW) is established. By analyzing the relationship between the main variables, structure priority function of the initial population, a new front crossover operator, swap mutation operator and reverse mutation operator are designed, and an improved genetic algorithm solving VRP-SDPTW is constructed. The algorithm could overcome the traditional genetic algorithm premature convergence defects. The example shows that the improved genetic algorithm can be effective in the short period of time to obtain the satisfactory solution of the VRP-SDPTW.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

scholarly journals An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Deng ◽  
Yang Liu ◽  
Deyun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Average Error ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Population Strategy ◽  
Series Of Experiments

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

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