scholarly journals Deep Learning to Handle Congestion in Vehicle Routing Problem: A Review

2021 ◽  
Vol 2129 (1) ◽  
pp. 012023
Author(s):  
D Satyananda ◽  
A Abdullah
Keyword(s):  
Deep Learning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Traffic Data ◽  
Real Situation ◽  
Traffic Condition ◽  
Routing Problem ◽  
The Real ◽  
Benchmark Datasets

Abstract This paper reviews the implementation design of Deep Learning in Vehicle Routing Problem. Congestion and traffic condition are usually avoided in Vehicle Routing Problem due to its modeling complexity, and even the benchmark datasets only cover essential conditions. In the real situation, the traffic condition is varied, and congestion is the worst part. To model the real life, the delivery route must consider these situations. The vehicle needs information on traffic prediction in future time to avoid congestion. The prediction needs historical traffic data, which is very large. Deep Learning can handle the enormous size and extract data features to infer the prediction.

scholarly journals The Robust Vehicle Routing Problem with Time Windows: Compact Formulation and Branch-Price-and-Cut Method

2019 ◽  
Vol 53 (4) ◽  
pp. 1043-1066 ◽  
Author(s):  
Pedro Munari ◽  
Alfredo Moreno ◽  
Jonathan De La Vega ◽  
Douglas Alem ◽  
Jacek Gondzio ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Real Life ◽  
Set Partitioning ◽  
Branch And Cut ◽  
Small Scale ◽  
Routing Problem ◽  
Compact Formulation ◽  
Cut Method

We address the robust vehicle routing problem with time windows (RVRPTW) under customer demand and travel time uncertainties. As presented thus far in the literature, robust counterparts of standard formulations have challenged general-purpose optimization solvers and specialized branch-and-cut methods. Hence, optimal solutions have been reported for small-scale instances only. Additionally, although the most successful methods for solving many variants of vehicle routing problems are based on the column generation technique, the RVRPTW has never been addressed by this type of method. In this paper, we introduce a novel robust counterpart model based on the well-known budgeted uncertainty set, which has advantageous features in comparison with other formulations and presents better overall performance when solved by commercial solvers. This model results from incorporating dynamic programming recursive equations into a standard deterministic formulation and does not require the classical dualization scheme typically used in robust optimization. In addition, we propose a branch-price-and-cut method based on a set partitioning formulation of the problem, which relies on a robust resource-constrained elementary shortest path problem to generate routes that are robust regarding both vehicle capacity and customer time windows. Computational experiments using Solomon’s instances show that the proposed approach is effective and able to obtain robust solutions within a reasonable running time. The results of an extensive Monte Carlo simulation indicate the relevance of obtaining robust routes for a more reliable decision-making process in real-life settings.

APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

2003 ◽  
Vol 03 (01) ◽  
pp. 1-21 ◽  
Author(s):  
GEORGE MOURKOUSIS ◽  
MATHEW PROTONOTARIOS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Performance ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Search Space ◽  
Genetic Operators ◽  
Actual Problem ◽  
Routing Problem

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

scholarly journals Proposed Modified Clarke-Wright Saving Algorithm for Capacitated Vehicle Routing Problem

2019 ◽  
Vol 1 (1) ◽  
pp. 9 ◽  
Author(s):  
A.K. Pamosoaji ◽  
P.K. Dewa ◽  
J.V. Krisnanta
Keyword(s):  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Algorithm ◽  
Delivery Time ◽  
Traffic Condition ◽  
Routing Problem ◽  
Multi Objective ◽  
Saving Algorithm ◽  
Capacitated Vehicle

A multi-objective distribution routing algorithm by using modified Clarke and Wright Saving algorithm is presented. The problem to solve is to deliver loads to a number of outlets based load requirement. The objective function to minimize is the distance saving and traveling time of the resulted route started from depot to the outlets and return to the original depot. Problem to solve is generating a distribution route in a week considering traffic condition for each day. The original Clarke and Wright saving algorithm is modified such that the resulted routes (from a depot to some outlets) accommodates some constraints such as the maximum allowable traveling time, maximum number of delivery shifts, and maximum number of vehicles. The algorithm is applied to a distributor company with nine outlets, two vehicles, and two delivery shifts. In addition, the traffic condition on the outlet-to-outlet and the depot-to-outlet routes is considered. The simulation of the proposed algorithm shows that the algorithm can generate routes that comply with shift’s maximum delivery time and the vehicles’ capacities. 

Sign in / Sign up

Export Citation Format

Share Document