Multi-Criteria Decision Making With Machine Learning for Vehicle Routing Problem

2021 ◽  
pp. 232-256
Author(s):  
Fatma Demircan Keskin
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Time Windows ◽  
Real Life ◽  
Support Vector ◽  
Calculation Algorithm ◽  
Analytic Hierarchy ◽  
Routing Problem ◽  
Fcm Clustering

This chapter addresses vehicle routing problem with time windows (VRPTW), one of the most well-known combinatorial optimization problems with many real-world applications in the transportation sector. This chapter proposes a three-stage approach for VRPTW and presents an application of this approach to a real-life problem. The stages of the approach include clustering of customers, determining feasible routes and their criteria values for each cluster, and selecting the best routes for each cluster based on multi-criteria decision analysis. In the first stage of the proposed approach, a fuzzy c-means (FCM) clustering-based assignment algorithm is used. The second stage includes predicting travel times between nodes based on GPS data with support vector regression (SVR) and applying the proposed feasible route determination and criteria value calculation algorithm using these predictions and other inputs. In the last stage, routes are selected with the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) for each cluster.

scholarly journals Solving Multi-Objective Vehicle Routing Problem with Time Windows Using Hybrid Ants Optimization and Tabu Search Based on Performance Metrics

2017 ◽  
Vol 11 (5) ◽  
pp. 52
Author(s):  
Mohammed Hassan Hassan ◽  
Laina Makdyssiian ◽  
Waseem Habib Bilal
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Time Windows ◽  
Real Life ◽  
Routing Problem ◽  
Multi Objective ◽  
Hard Type

In this research, we are studying the possibility of contribution in solving the multi-objective vehicle Routing problem with time windows, that is one of the optimization problems of the NP-hard type, This problem has attracted a lot of attention now because of its real life applications.Moreover, We will also introduce an algorithm called Hybrid Algorithm (HA) which depends on integrates between Multiple Objective Ant Colony Optimisation (MOACO) and Tabu Search (TS) algorithm based on the Pareto optimization, and compare the presented approach is the developer with standard tests to demonstrate the applicability and efficiency.

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.

Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows

2018 ◽  
Vol 9 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lahcene Guezouli ◽  
Mohamed Bensakhria ◽  
Samir Abdelhamid
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Golden Ball

In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.

A Hybrid Meta-Heuristic Algorithm for Vehicle Routing Problem with Time Windows

2015 ◽  
Vol 24 (06) ◽  
pp. 1550021 ◽  
Author(s):  
Esam Taha Yassen ◽  
Masri Ayob ◽  
Mohd Zakree Ahmad Nazri ◽  
Nasser R. Sabar
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Time Windows ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Routing Problem

Harmony search algorithm, which simulates the musical improvisation process in seeking agreeable harmony, is a population based meta-heuristics algorithm for solving optimization problems. Although it has been successfully applied on various optimization problems; it suffers the slow convergence problem, which greatly hinders its applicability for getting good quality solution. Therefore, in this work, we propose a hybrid meta-heuristic algorithm that hybridizes a harmony search with simulated annealing for the purpose of improving the performance of harmony search algorithm. Harmony search algorithm is used to explore the search spaces. Whilst, simulated annealing algorithm is used inside the harmony search algorithm to exploit the search space and further improve the solutions that are generated by harmony search algorithm. The performance of the proposed algorithm is tested using the Solomon's Vehicle Routing Problem with Time Windows (VRPTW) benchmark. Numerical results demonstrate that the hybrid approach is better than the harmony search without simulated annealing and the hybrid also proves itself to be more competent (if not better on some instances) when compared to other approaches in the literature.

scholarly journals Sequential Simulated Annealing for the Vehicle Routing Problem with Time Windows

2009 ◽  
Vol 3 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Marcin Woch ◽  
Piotr Łebkowski
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Time Windows ◽  
High Quality ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Very High

This article presents a new simulated annealing algorithm that provides very high quality solutions to the vehicle routing problem. The aim of described algorithm is to solve the vehicle routing problem with time windows. The tests were carried out with use of some well known instances of the problem defined by M. Solomon. The empirical evidence indicates that simulated annealing can be successfully applied to bi-criterion optimization problems.

An Improved Ant Colony Optimization for VRP with Time Windows

2012 ◽  
Vol 263-266 ◽  
pp. 1609-1613 ◽  
Author(s):  
Su Ping Yu ◽  
Ya Ping Li
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Optimization Problems ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Problem ◽  
Combinatorial Optimization Problems

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The Ant Colony Optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved Ant Colony Optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

scholarly journals Mathematical models for the periodic vehicle routing problem with time windows and time spread constraints

2020 ◽  
Vol 11 (1) ◽  
pp. 10-23
Author(s):  
Hande Öztop ◽  
Damla Kizilay ◽  
Zeynel Abidin Çil
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Valid Inequalities ◽  
Time Windows ◽  
Mixed Integer ◽  
Routing Problem ◽  
Periodic Vehicle Routing Problem ◽  
Time Spread ◽  
Periodic Vehicle Routing

The periodic vehicle routing problem (PVRP) is an extension of the well-known vehicle routing problem. In this paper, the PVRP with time windows and time spread constraints (PVRP-TWTS) is addressed, which arises in the high-value shipment transportation area. In the PVRP-TWTS, period-specific demands of the customers must be delivered by a fleet of heterogeneous capacitated vehicles over the several planning periods. Additionally, the arrival times to a customer should be irregular within its time window over the planning periods, and the waiting time is not allowed for the vehicles due to the security concerns. This study, proposes novel mixed-integer linear programming (MILP) and constraint programming (CP) models for the PVRP-TWTS. Furthermore, we develop several valid inequalities to strengthen the proposed MILP and CP models as well as a lower bound. Even though CP has successful applications for various optimization problems, it is still not as well-known as MILP in the operations research field. This study aims to utilize the effectiveness of CP in solving the PVRP-TWTS. This study presents a CP model for PVRP-TWTS for the first time in the literature to the best of our knowledge. Having a comparison of the CP and MILP models can help in providing a baseline for the problem. We evaluate the performance of the proposed MILP and CP models by modifying the well-known benchmark set from the literature. The extensive computational results show that the CP model performs much better than the MILP model in terms of the solution quality.

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