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

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.

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An Improved Genetic Algorithm of Vehicle Scheduling Problems for Military Logistic Distribution

2013 Third International Conference on Intelligent System Design and Engineering Applications ◽

10.1109/isdea.2012.71 ◽

2013 ◽

Cited By ~ 1

Author(s):

Gong Yancheng

Keyword(s):

Genetic Algorithm ◽

Vehicle Scheduling ◽

Logistic Distribution ◽

Scheduling Problems ◽

Improved Genetic Algorithm

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.1790 ◽

2012 ◽

Vol 178-181 ◽

pp. 1790-1796 ◽

Cited By ~ 2

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.

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Vehicle scheduling problems of military logistics distribution based on improved Genetic Algorithm

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5623185 ◽

2010 ◽

Cited By ~ 1

Author(s):

Gong Yancheng

Keyword(s):

Genetic Algorithm ◽

Vehicle Scheduling ◽

Scheduling Problems ◽

Improved Genetic Algorithm ◽

Military Logistics ◽

Logistics Distribution

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

Latin American Applied Research - An international journal ◽

10.52292/j.laar.2018.218 ◽

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.

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Research of Vehicle Scheduling with Time Window Based on Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.409-410.1307 ◽

2013 ◽

Vol 409-410 ◽

pp. 1307-1310

Author(s):

Xiao Rong Zhou ◽

Meng Tian Song ◽

Yu Ling Zhang

Keyword(s):

Genetic Algorithm ◽

Time Window ◽

Time Windows ◽

Distribution Center ◽

Optimal Scheduling ◽

Vehicle Scheduling ◽

Search Process ◽

Long Distance ◽

Soft Time Windows ◽

Scheduling Model

This paper based on the genetic algorithm,introduced part search process and respectively established the mathematical scheduling model of full loads vehicle optimal scheduling with soft time windows and of non-full loads on the basis of the long distance logistics transportation situation of some companys distribution center in Shenzhen. Then programmed to achieve the scheduling of multi-vehicle touting and selection, and conducted example analysis.

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A new improved genetic algorithm approach and a competitive heuristic method for large-scale multiple resource-constrained project-scheduling problems

International Journal of Industrial Engineering Computations ◽

10.5267/j.ijiec.2011.06.009 ◽

2011 ◽

Vol 2 (4) ◽

pp. 737-748 ◽

Cited By ~ 5

Author(s):

Mostafa Khanzadi ◽

Rambod Soufipour ◽

Mohammad Rostami

Keyword(s):

Genetic Algorithm ◽

Project Scheduling ◽

Large Scale ◽

Heuristic Method ◽

Scheduling Problems ◽

Improved Genetic Algorithm ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Genetic Algorithm Approach ◽

Multiple Resource

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Integrated Tomato Picking and Distribution Scheduling Based on Maturity

Sustainability ◽

10.3390/su12197934 ◽

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.

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