A Modified Genetic Algorithm for Agricultural By-products Logistics Delivery Route Planning Problem

2016 ◽  
pp. 193-205 ◽  
Author(s):  
Guofu Luo ◽  
Dayuan Wu ◽  
Jun Ma ◽  
Xiaoyu Wen
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Planning Problem ◽  
Modified Genetic Algorithm ◽  
Delivery Route ◽  
Logistics Delivery ◽  
By Products

An XGBoost-enhanced fast constructive algorithm for food delivery route planning problem

2021 ◽  
Vol 152 ◽  
pp. 107029
Author(s):  
Xing Wang ◽  
Ling Wang ◽  
Shengyao Wang ◽  
Jing-fang Chen ◽  
Chuge Wu
Keyword(s):  
Route Planning ◽  
Food Delivery ◽  
Planning Problem ◽  
Constructive Algorithm ◽  
Delivery Route

Route Planning Modeling Study Based on Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1443-1447
Author(s):  
Shou Fu Sun ◽  
Jun Huang ◽  
Wan Feng Ji ◽  
Yun Lin ◽  
Qian Yu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Probability Function ◽  
Model Problem ◽  
Route Planning ◽  
Planning Problem ◽  
Function Model ◽  
Simulation Calculation ◽  
Flight Route ◽  
Planning Objective

Route planning model problem is a key point in flight route planning problem research. Whether objective function model design is reasonable or not has very important influence on the efficiency and accuracy of route planning. Continuous threat probability function model is established, route planning objective function model is constructed, and genetic algorithm is applied to route planning, and finally the effectiveness of the model is verified by simulation calculation.

scholarly journals The Multiagent Planning Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Giovanni Giardini ◽  
Bendegúz Dezső Bak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Problem Solver ◽  
Planning Problem ◽  
Genetic Operators ◽  
Total Distance ◽  
Modified Genetic Algorithm ◽  
Multiagent Planning ◽  
Simple Paths ◽  
Better Than

The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

A Solution to UAV Route Planning Problem Based on Chaotic Genetic Algorithm

2021 ◽  
pp. 305-312
Author(s):  
Trong-The Nguyen ◽  
Jun-Feng Guo ◽  
Jin-Yang Lin ◽  
Tien-Wen Sung ◽  
Truong-Giang Ngo
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Planning Problem

scholarly journals Urban Logistics Delivery Route Planning Based on A Single Metro Line

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Changjiang Zheng ◽  
Yuhang Gu ◽  
Jinxing Shen ◽  
Muqing Du
Keyword(s):  
Route Planning ◽  
Delivery Route ◽  
Logistics Delivery

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

Sign in / Sign up

Export Citation Format

Share Document