scholarly journals An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

2021 ◽  
Vol 13 (16) ◽  
pp. 9430
Author(s):  
Chen-Yang Cheng ◽  
Kuo-Ching Ying ◽  
Chung-Cheng Lu ◽  
Chumpol Yuangyai ◽  
Wan-Chen Chiang
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Real Life ◽  
Practical Consideration ◽  
Balance Performance ◽  
Routing Problem ◽  
Practical Applications ◽  
Scenario Analyses ◽  
Shortest Route ◽  
Auction Price

In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.

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.

scholarly journals On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 208 ◽  
Author(s):  
Quim Arnau ◽  
Angel Juan ◽  
Isabel Serra
Keyword(s):  
Vehicle Routing ◽  
Optimization Problems ◽  
Time Windows ◽  
Real Life ◽  
Routing Problem ◽  
Routing Optimization ◽  
Statistical Learning Techniques ◽  
Critical Activity ◽  
The Cost ◽  
Vehicle Routing Optimization

Freight transportation is becoming an increasingly critical activity for enterprises in a global world. Moreover, the distribution activities have a non-negligible impact on the environment, as well as on the citizens’ welfare. The classical vehicle routing problem (VRP) aims at designing routes that minimize the cost of serving customers using a given set of capacitated vehicles. Some VRP variants consider traveling times, either in the objective function (e.g., including the goal of minimizing total traveling time or designing balanced routes) or as constraints (e.g., the setting of time windows or a maximum time per route). Typically, the traveling time between two customers or between one customer and the depot is assumed to be both known in advance and static. However, in real life, there are plenty of factors (predictable or not) that may affect these traveling times, e.g., traffic jams, accidents, road works, or even the weather. In this work, we analyze the VRP with dynamic traveling times. Our work assumes not only that these inputs are dynamic in nature, but also that they are a function of the structure of the emerging routing plan. In other words, these traveling times need to be dynamically re-evaluated as the solution is being constructed. In order to solve this dynamic optimization problem, a learnheuristic-based approach is proposed. Our approach integrates statistical learning techniques within a metaheuristic framework. A number of computational experiments are carried out in order to illustrate our approach and discuss its effectiveness.

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.

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.

Variants of VRP to Optimize Logistics Management Problems

2012 ◽  
pp. 207-237 ◽  
Author(s):  
Claudia Gómez Santillán ◽  
Laura Cruz Reyes ◽  
María Lucila Morales Rodríguez ◽  
Juan Javier González Barbosa ◽  
Oscar Castillo López ◽  
...  
Keyword(s):  
Supply Chain ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Supply Chain Coordination ◽  
Real Life ◽  
Routing Problem ◽  
Transportation Management ◽  
Practical Applications ◽  
Operation Costs ◽  
Rich Vrp

The Vehicle Routing Problem (VRP) is a key to the efficient transportation management and supply-chain coordination. VRP research has often been too focused on idealized models with non-realistic assumptions for practical applications. Nowadays the evolution of methodologies allows that the classical problems could be used to solve VRP problems of real life. The evolution of methodologies allows the creation of variants of the VRP which were considered too difficult to handle by their variety of possible restrictions. A VRP problem that includes the addition of restrictions, which represent the variants in the problem, is called Rich VRP. This work presents an algorithm to optimize the transportation management. The authors are including a case of study which solves a real routing problem applied to the distribution of bottled products. The proposed algorithm shows a saving in quantity of vehicles and reduces the operation costs of the company.

Sign in / Sign up

Export Citation Format

Share Document