scholarly journals Research on Coordination and Optimization of Order Allocation and Delivery Route Planning in Take-Out System

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Guofeng Sun ◽  
Zhiqiang Tian ◽  
Renhua Liu ◽  
Yun Jing ◽  
Yawen Ma
Keyword(s):  
Time Window ◽  
Route Planning ◽  
Route Optimization ◽  
Dynamic Allocation ◽  
Improved Genetic Algorithm ◽  
Time Dimension ◽  
Order Allocation ◽  
Routing Problem ◽  
Delivery Problem ◽  
Delivery Route

This paper studies the take-out route delivery problem (TRDP) with order allocation and unilateral soft time window constraints. The TRDP considers the order allocation and delivery route optimization in the delivery service process. The TRDP is a challenging version of vehicle routing problem. In order to solve this problem, this paper aims to minimize the total cost of delivery, builds an optimization model of this problem by using cumulative time, and adds time dimension in order allocation and path optimization dimensions. It can not only track the real-time location of delivery personnel but also record the delivery personnel to perform a certain task. The main algorithm is the dynamic allocation algorithm designed from the perspective of dispatch efficiency, and the subalgorithm is the improved genetic algorithm. Finally, some experiments are designed to verify the effectiveness of the established model and the designed algorithm, the order allocation and route optimization are calculated with/without the consideration of traffic jam, and the results show that the algorithm can generate better solution in each scene.

Route Optimization of Aquatic Product Transportation Based on an Improved Ant Colony Algorithm

2020 ◽  
Vol 24 (4) ◽  
pp. 488-493
Author(s):  
Chenxiao Yu ◽  
Zuiyi Shen ◽  
Pengfei Li ◽  
◽  
◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Time Window ◽  
Route Planning ◽  
Ant Colony ◽  
Route Optimization ◽  
Routing Problem ◽  
Aquatic Products ◽  
Aquatic Product ◽  
To Receive ◽  
Improved Ant Colony Algorithm

In this paper, the time window in which aquatic products must be delivered and the uncertainty of road conditions that affect the time at which customers are able to receive the goods are added as constraints in the optimization model of the Vehicle Routing Problem. The use of pheromones in the original ant colony algorithm was improved, and the waiting factor was added into the state transition rules to limit the information range. The improved ant colony algorithm was used to simulate the model with the example of aquatic product transportation route planning in Zhoushan city. The results show that this algorithm can optimize the transportation and distribution routes of aquatic products more effectively.

scholarly journals Transportation route optimization based on heuristic algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032016
Author(s):  
Fan Wu ◽  
Yongan Zhu
Keyword(s):  
Heuristic Algorithm ◽  
Time Window ◽  
Rapid Development ◽  
Route Optimization ◽  
Internet Technology ◽  
Mixed Integer ◽  
Routing Problem ◽  
Freight Volume ◽  
Transportation Organization ◽  
Transportation Company

Abstract With the rapid development of Internet technology, many enterprises are committed to finding the best solution in transportation organization and solving the vehicle distribution routing problem. Firstly, this paper introduces the current situation of transportation organization of Sichuan Yida Feiniu Transportation Company, and analyzes the main problems of the company. Secondly, through the prediction of freight volume, prepare the truck vehicle operation plan and optimize the company’s transportation organization and production plan. Finally, the heuristic algorithm is used to establish a mixed integer programming mathematical model to optimize the pooled vehicle distribution path problem and the vehicle distribution path with time window. In terms of centralized vehicle distribution, combined with the actual situation of Sichuan Yida Feiniu Transportation Company, an example is analyzed, the shortest total path is obtained, and the goal of shortest vehicle travel distance is realized. Through the optimization of the company’s transportation organization, this paper is of great significance to improve the company’s transportation organization to a certain extent.

scholarly journals Comparison study of metaheuristics

2017 ◽  
Vol 9 ◽  
pp. 184797901774360 ◽  
Author(s):  
Anna Maria Sri Asih ◽  
Bertha Maya Sopha ◽  
Gilang Kriptaniadewa
Keyword(s):  
Empirical Data ◽  
Lower Number ◽  
Route Optimization ◽  
Comparison Study ◽  
Swarm Optimization ◽  
Delivery Problem ◽  
First Case ◽  
Hypothetical Data ◽  
Delivery Route ◽  
Different Characteristics

Many existing studies have used hypothetical data to evaluate the performance of various metaheuristics in solving delivery route optimization. As empirical data impose characteristics of a particular problem, it is necessary to evaluate whether the problem characteristics may influence to the performance of metaheuristics. This article therefore attempts to compare the performance of metaheuristics, that is, genetic algorithm, ant colony optimization (ACO), particle swarm optimization, and simulated annealing (SA), to solve an empirical delivery problem in Yogyakarta, Indonesia. Two cases are developed to capture different characteristics of empirical data. The first case introduces delivery problem of one logistics operator and 58 retailers; the second case presents collaborative strategy in delivery problem, involving two logistics operators and 142 retailers. Results indicate that ACO and SA perform better with respect to less distance traveled for both cases and higher truck utility and lower number of routes for the second case.

scholarly journals Research on Optimization of Customized Bus Routes Based on Uncertainty Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zi Sang ◽  
Bing Zhang ◽  
Yunqiang Xue ◽  
Hongzhi Guan
Keyword(s):  
Optimization Model ◽  
Uncertainty Theory ◽  
Optimization Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Improved Genetic Algorithm ◽  
Optimization Scheme ◽  
Theoretical Support ◽  
Vehicle Operation

In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes.

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 Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1608
Author(s):  
Mahdi Vahdanjoo ◽  
Kun Zhou ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Route Planning ◽  
Route Optimization ◽  
Specific Load ◽  
Planning Tool ◽  
Routing Problem ◽  
Planning Approach ◽  
Multiple Depots ◽  
General Goal ◽  
Agricultural Machines

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

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