Abstract

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.

Highlights

  • The Vehicle Routing Problem with Time Windows (VRPTW) is an extension of the VRP and was First introduced in 1959 (Dantzig et al, 1959).In the VRPTW, vehicles are required to serve customers within a limited time period

  • After the researchers studied the different algorithms separately, they researchers realized that it is not enough, and the researchers believe that the hybridization of this Heuristic algorithms and metaheuristic can lead to better results through the integration of these algorithms and the combination of their different function (Angus & Woodward, 2009; Tan, Chew & Lee, 2006; Bouhafs, Hajjam & Koukam, 2010; Baran & Schaerer, (2003) And we will propose in this paper a hybrid algorithm developed from Heuristic algorithms and metaheuristic to resolve the Multi-Objective Vehicle Routing Problem With Time windows, The proposed algorithm will form a different framework of processing

  • Most of the applications of real-life have problems with multiple conflicting objectives, and will be considering the vehicle Routing problem with time windows along with a multi-objective stretch .This work draws inspiration from the approach proposed by Baran and Schaerer in 2003 in order to solve the biobjective TSP (Pinto & Barán, 2005; Maniezzo, Gambardella & De Luigi, 2004; Iredi, Merkle & Middendorf, 2001; Baran & Schaerer, (2003), which consists in a Multiple Objective Ant Colony Optimisation (MOACO) algorithm with a single ant colony, a single pheromone structure τ and different adaptive visibility for each objective function

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Summary

Introduction

The Vehicle Routing Problem with Time Windows (VRPTW) is an extension of the VRP and was First introduced in 1959 (Dantzig et al, 1959). In the VRPTW, vehicles are required to serve customers within a limited time period. It has been included in the category of NP-Hard problems. Fleet managers want to know the trade-off between these two objectives before determining the best routing plan. To accomplish this goal, another stream of research was started by searching for the set of Pareto optimal solutions rather than a single optimal solution. By looking into the trade-off between these solutions, managers can get more information and make a better decision. (Moccia, Cordeau & Laporte, 2012; Archetti & Speranza, 2014; Dantzig & Ramser, 1959)

The Assumptions
Multi-Objective Optimization and Pareto-Optimal Solutions
Objective Weighting
Min–Max formulation
Lexicographic Approach
Drawbacks Classic Methods
Performance Metrics
M1 Metric
M2 Metric
C Metric
Processing the Problem
Tabu Search TS
Memory medium-term structure
10. Moaco Generalization
11. Construct a Solution
11.1 Pheromone Updating
12. The General Procedure for Finding No-Dominated Solutions
13. Proposed Algorithm
14. Experimentation and Results
15. Conclusion

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