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.

A Hybrid Algorithm for the Multi-Objective Time Dependent Vehicle Routing Problem

2014 ◽  
Vol 505-506 ◽  
pp. 1071-1075
Author(s):  
Yi Sun ◽  
Yue Chen ◽  
Chang Chun Pan ◽  
Gen Ke Yang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Time Dependent ◽  
Ant Colony System ◽  
Optimal Solutions ◽  
Test Results ◽  
Routing Problem ◽  
Traffic Conditions

This paper presents a real road network case based on the time dependent vehicle routing problem with time windows (TDVRPTW), which involves optimally routing a fleet of vehicles with fixed capacity when traffic conditions are time dependent and services at customers are only available in their own time tables. A hybrid algorithm based on the Genetic Algorithm (GA) and the Multi Ant Colony System (MACS) is introduced in order to find optimal solutions that minimize two hierarchical objectives: the number of tours and the total travel cost. The test results show that the integrated algorithm outperforms both of its traditional ones in terms of the convergence speed towards optimal solutions.

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 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.

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.

scholarly journals A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

2021 ◽  
Author(s):  
Saeed Khanchehzarrin ◽  
Maral Shahmizad ◽  
Iraj Mahdavi ◽  
Nezam Mahdavi-Amiri ◽  
Peiman Ghasemi
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Time Windows ◽  
Time Dependent ◽  
Mixed Integer ◽  
Travel Times ◽  
Routing Problem ◽  
Traffic Conditions

A new mixed-integer nonlinear programming model is presented for the time-dependent vehicle routing problem with time windows and intelligent travel times. The aim is to minimize fixed and variable costs, with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time. Depending on working hours, the route between any two nodes has a unique traffic parameter. We consider each working day to be divided into several equal and large intervals, termed as a scenario. Here, allowing for long distances between some of the nodes, travel time may take more than one scenario, resulting in resetting the scenario at the start of each large interval. This repetition of scenarios has been used in modeling and calculating travel time. A tabu search optimization algorithm is devised for solving large problems. Also, after linearization, a number of random instances are generated and solved by the CPLEX solver of GAMS to assess the effectiveness of our proposed algorithm. Results indicate that the initial travel time is estimated appropriately and updated properly in accordance with to the repeating traffic conditions.

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

scholarly journals Vehicle Routing Problems with Fuel Consumption and Stochastic Travel Speeds

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Yanling Feng ◽  
Ren-Qian Zhang ◽  
Guozhu Jia
Keyword(s):  
Vehicle Routing ◽  
Fuel Consumption ◽  
Mathematic Model ◽  
Travel Speed ◽  
Time Dependent ◽  
Computational Results ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Conventional Vehicle ◽  
Traffic Conditions

Conventional vehicle routing problems (VRP) always assume that the vehicle travel speed is fixed or time-dependent on arcs. However, due to the uncertainty of weather, traffic conditions, and other random factors, it is not appropriate to set travel speeds to fixed constants in advance. Consequently, we propose a mathematic model for calculating expected fuel consumption and fixed vehicle cost where average speed is assumed to obey normal distribution on each arc which is more realistic than the existing model. For small-scaled problems, we make a linear transformation and solve them by existing solver CPLEX, while, for large-scaled problems, an improved simulated annealing (ISA) algorithm is constructed. Finally, instances from real road networks of England are performed with the ISA algorithm. Computational results show that our ISA algorithm performs well in a reasonable amount of time. We also find that when taking stochastic speeds into consideration, the fuel consumption is always larger than that with fixed speed model.

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