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.

Sign in / Sign up

Export Citation Format

Share Document