Waste collection vehicle routing problem with time windows using multi-objective genetic algorithms

Authors:
Beatrice M. Ombuki-Berman;Andrew Runka;Franklin T. Hanshar
Affiliations:
Brock University, St. Catharines, ON, Canada;Brock University, St. Catharines, ON, Canada;University of Guelph, Guelph, Ontario
Venue:
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Year:
2007

Citing 11
Cited 1

VRP with Time Windows

The vehicle routing problem
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Routing Optimization for Waste Management

Interfaces
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

Evolutionary Computation
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

Applied Intelligence
Waste collection vehicle routing problem with time windows

Computers and Operations Research
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation
Improved route planning and scheduling of waste collection and transport

Expert Systems with Applications: An International Journal

Metaheuristics for the waste collection vehicle routing problem with time windows, driver rest period and multiple disposal facilities

Computers and Operations Research

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Abstract

We study a waste collection vehicle routing problem with time windows (VRPTW) complicated by multiple disposal trips and driver's lunch breaks. Recently Kim et al. [1] introduced and addressed this problem using an extension of the well-known Solomon's insertion approach, and a clustering-based algorithm. We propose and present the results of an initial study of a multi-objective genetic algorithm for the waste collection VRPTW using a set of benchmark data from real-world problems obtained by Kim et al.