Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

doi:10.1016/j.asoc.2010.04.001

Return

Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

2010-09-01

207

PRE

Seyed Farid Ghannadpour

DOI：10.1016/j.asoc.2010.04.001

Original

Original request for help

Save

Abstract

En 中文

This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and lambda-interchange mechanism. The algorithm is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature. (C) 2010 Elsevier B. V. All rights reserved.

Keywords:

Vehicle routing problem with time windows (VRPTW)

Goal programming (GP)

Genetic algorithm

Multiple objective optimization

Pareto ranking

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.