A Mathematical Model of the Vehicles Routing Problem of Perishable Materials Using Genetic Algorithm

2021 ◽  
Vol 20 (2) ◽  
pp. 315-321
Author(s):  
Anastasia A. Kurilova
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Routing Problem

scholarly journals The Mathematical Model and an Genetic Algorithm for the Two-Echelon Electric Vehicle Routing Problem

2021 ◽  
Vol 1813 (1) ◽  
pp. 012006
Author(s):  
Yue Zhang ◽  
Shenghan Zhou ◽  
XinPeng Ji ◽  
Bang Chen ◽  
HouXiang Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
The Mathematical Model

Multi-Depot Vehicle Routing Problem with Hybrid Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 555-560 ◽  
Author(s):  
Li Wei Dang ◽  
Xiao Ming Sun
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Transport Distance ◽  
Delivery Costs ◽  
Two Factors

About the multi-depot vehicle routing problem, considering the transport distance and the number of dispatching vehicles together can effectively reduce the total delivery costs. Firstly establish the corresponding mathematical model by taking the two factors into account. Secondly solve the model by using hybrid genetic algorithms. Thirdly demonstrate the effectiveness of the model and algorithm by an example

scholarly journals Optimization of a Two-Echelon Location Lot-Sizing Routing Problem with Deterministic Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Laila Kechmane ◽  
Benayad Nsiri ◽  
Azeddine Baalal
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Inventory Management ◽  
Lot Sizing ◽  
Search Procedure ◽  
Strategic Decisions ◽  
Routing Problem ◽  
Deterministic Demand ◽  
Local Search Procedure ◽  
Tactical Decisions

This paper aims to solve a multiperiod location lot-sizing routing problem with deterministic demand in a two-echelon network composed of a single factory, a set of potential depots, and a set of customers. Solving this problem involves making strategic decisions such as location of depots as well as operational and tactical decisions which include customers’ assignment to the open depots, vehicle routing organization, and inventory management. A mathematical model is presented to describe the problem and a genetic algorithm combined with a local search procedure is proposed to solve it and is tested over three sets of instances.

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.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

scholarly journals Stochastic mathematical model for vehicle routing problem in collecting perishable products

DYNA ◽  
2015 ◽  
Vol 82 (189) ◽  
pp. 199-206 ◽  
Author(s):  
Elsa Cristina Gonzalez-L. ◽  
Wilson Adarme-Jaimes ◽  
Javier Arturo Orjuela-Castro
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Perishable Products ◽  
Routing Problem
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