Multiobjective Optimization for Multimode Transportation Problems

We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve...

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Bibliographic Details
Published inAdvances in Operations Research Vol. 2018; no. 2018; pp. 1 - 13
Main Authors Håkansson, Johan, Rebreyend, Pascal, Massé, Damien, Lemarchand, Laurent
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Algorithms
Approximation
Complex Systems – Microdata Analysis
Computer Science
Genetic algorithms
Heuristic
Heuristic methods
Integer programming
Komplexa system - mikrodataanalys
Localization
Mathematical optimization
Multiple objective analysis
Objectives
Operations Research
Optimization
Parallel processing
Public transportation
Service facilities
Software
Test procedures
Transportation
Traveling salesman problem
United States
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Summary:We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve it first exactly using an adapted ϵ-constraint multiobjective method. Because of the size of the instances considered, we have also explored the use of heuristic methods based on evolutionary multiobjective frameworks, namely, NSGA2 and a modified version of PAES. Those methods are mixed with an original local search technique to provide better results. Numerical comparisons of solutions sets quality are made using the hypervolume metric. Based on the results for test-cases that can be solved exactly, efficient implementation for PAES and NSGA2 allows execution times comparison for large instances. Results show good performances for the heuristic approaches as compared to the exact algorithm for small test-cases. Approximate methods present a scalable behavior on largest problem instances. A master/slave parallelization scheme also helps to reduce execution times significantly for the modified PAES approach.
ISSN:1687-9147
1687-9155
1687-9155
DOI:10.1155/2018/8720643