scholarly journals Arc Routing with Time-Dependent Travel Times and Paths

2021 ◽  
Author(s):  
Thibaut Vidal ◽  
Rafael Martinelli ◽  
Tuan Anh Pham ◽  
Minh Hoàng Hà
Keyword(s):  
Complete Graph ◽  
Road Network ◽  
Travel Speed ◽  
Complex Problem ◽  
Time Dependent ◽  
Arc Routing ◽  
Routing Problem ◽  
Genetic Search ◽  
Quickest Path ◽  
Routing Optimization

Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for preprocessing quickest paths in a closed form, efficient data structures for travel time queries during routing optimization, and heuristic and exact solution approaches for the TDCARP. Our heuristic uses the hybrid genetic search principle with tailored solution-decoding algorithms and lower bounds for filtering moves. Our branch-and-price algorithm exploits dedicated pricing routines, heuristic dominance rules, and completion bounds to find optimal solutions for problems counting up to 75 services. From these algorithms, we measure the benefits of time-dependent routing optimization for different levels of travel-speed data accuracy.

Vehicle Routing Planning in Dynamic Transportation Network Based on Floating Car Data

2012 ◽  
Vol 209-211 ◽  
pp. 707-716
Author(s):  
Zheng Yu Duan ◽  
Dong Yuan Yang
Keyword(s):  
Vehicle Routing ◽  
Road Network ◽  
Transportation Network ◽  
Dynamic Network ◽  
Travel Speed ◽  
Time Dependent ◽  
Routing Problem ◽  
Traffic Conditions ◽  
Floating Car Data ◽  
Departure Time

The traditional research on vehicle routing planning was mostly on the assumption that link travel time is constant, but the traffic conditions in real road network are often fluctuant. In order to meet the requirements of fast and efficient delivery, it is necessary to study vehicle routing planning in dynamic transportation network. In recent years, time dependent vehicle routing problem (TDVRP) which considers traffic conditions attracted more and more scholar's attention. However, most studies on TDVRP are based on simple test network, and assumed all vehicles depart from the depot at a fix time. In this paper, we studied TDVRP based on floating car data. We gave a mathematical model for TDVRP, and represented the dynamic network as a first in first out (FIFO) network by time dependent function of travel speed. Then, we designed a routing construction algorithm named DTO-NNC algorithm for TDVRP. Moreover, we constructed a test instance of 100 customers based on floating car data in the road network of Shanghai, and solved it in the case of fixed departure time and variable departure time. Through the instance, DTO-NNC algorithm has been proven efficient in real road network.

scholarly journals Multi-Depot Joint Distribution Vehicle Routing Problem Considering Energy Consumption with Time-Dependent Networks

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2082
Author(s):  
Dengkai Hou ◽  
Houming Fan ◽  
Xiaoxue Ren
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Joint Distribution ◽  
Road Network ◽  
Time Dependent ◽  
Neighborhood Search ◽  
Routing Problem ◽  
The Road

This paper studies the multi-depot joint distribution vehicle routing problem considering energy consumption with time-dependent networks (MDJDVRP-TDN). Aiming at the multi-depot joint distribution vehicle routing problem where the vehicle travel time depends on the variation characteristics of the road network speed in the distribution area, considering the influence of the road network on the vehicle speed and the relationship between vehicle load and fuel consumption, a multi-depot joint distribution vehicle routing optimization model is established to minimize the sum of vehicle fixed cost, fuel consumption cost and time window penalty cost. Traditional vehicle routing problems are modeled based on symmetric graphs. In this paper, considering the influence of time-dependent networks on routes optimization, modeling is based on asymmetric graphs, which increases the complexity of the problem. A hybrid genetic algorithm with variable neighborhood search (HGAVNS) is designed to solve the model, in which the nearest neighbor insertion method and Logistic mapping equation are used to generate the initial solution firstly, and then five neighborhood structures are designed to improve the algorithm. An adaptive neighborhood search times strategy is used to balance the diversification and depth search of the population. The effectiveness of the designed algorithm is verified through several groups of numerical instances with different scales. The research can enrich the relevant theoretical research of multi-depot vehicle routing problems and provide the theoretical basis for transportation enterprises to formulate reasonable distribution schemes.

scholarly journals Memetic algorithm with route decomposing for periodic capacitated arc routing problem

2021 ◽  
Author(s):  
Yuzhou Zhang ◽  
Yi Mei ◽  
Ke Tang ◽  
Keqin Jiang
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Memetic Algorithm ◽  
State Of The Art ◽  
Arc Routing ◽  
Data Set ◽  
Routing Problem ◽  
Capacitated Arc Routing Problem ◽  
Single Period ◽  
Capacitated Arc Routing

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles (nv), and the other is the total cost (tc). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified. Graphical abstract https://ars.els-cdn.com/content/image/1-s2.0-S1568494616304768-fx1_lrg.jpg © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

scholarly journals Time-Dependent Electric Vehicle Routing Problem with Time Windows and Path Flexibility

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Li Wang ◽  
Shuai Gao ◽  
Kai Wang ◽  
Tong Li ◽  
Lin Li ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Path ◽  
Travel Speed ◽  
Path Selection ◽  
Time Dependent ◽  
Mixed Integer ◽  
Routing Problem

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises.

A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem

2021 ◽  
pp. 403-425
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Central Point ◽  
Routing Problem ◽  
Genetic Search ◽  
Routing Optimization ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.

scholarly journals Memetic algorithm with route decomposing for periodic capacitated arc routing problem

2021 ◽  
Author(s):  
Yuzhou Zhang ◽  
Yi Mei ◽  
Ke Tang ◽  
Keqin Jiang
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Memetic Algorithm ◽  
State Of The Art ◽  
Arc Routing ◽  
Data Set ◽  
Routing Problem ◽  
Capacitated Arc Routing Problem ◽  
Single Period ◽  
Capacitated Arc Routing

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles (nv), and the other is the total cost (tc). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified. Graphical abstract https://ars.els-cdn.com/content/image/1-s2.0-S1568494616304768-fx1_lrg.jpg © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

scholarly journals The Time-Dependent Vehicle Routing Problem with Time Windows and Road-Network Information

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Hamza Ben Ticha ◽  
Nabil Absi ◽  
Dominique Feillet ◽  
Alain Quilliot ◽  
Tom Van Woensel
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Road Network ◽  
Time Windows ◽  
Time Dependent ◽  
Routing Problem ◽  
Network Information
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