Optimization of multimodal transportation problems
Identifieur interne : 000246 ( Hal/Corpus ); précédent : 000245; suivant : 000247Optimization of multimodal transportation problems
Auteurs : Mustapha OudaniSource :
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Abstract
This thesis is a contribution to research on the optimization of multimodal transport problems. The main key concepts of multimodality in the intermodal transportation networks and the state of the art of scientific works in the field are presented. The intermodal terminal location problem is then studied. We propose a genetic algorithm with mixed encoding for solving this problem and we compare our results with those of literature. A set of problems in the framework of our work on the project DCAS (Direct Cargo Axe Seine), carried by the Grand Port Maritime du Havre, are described and modeled by mathematical programming tools. Thus, we studied the problem of the transfer of rail shuttles which is to optimize the transfer of a set of containers between maritime terminals and a multimodal terminal. We then modeled the scheduling problem of freight trains for placement on rail tracks. These problems are solved by using combined optimization simulation approaches. A first application is based on a genetic algorithm coupled with the multi agent’s simulation. A second is to optimize a rail-rail transshipment of containers using an ant colony algorithm embedded in the simulation model and an agent’s collaboration strategy to minimize waiting times and increase cranes productivity.
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The main key concepts of multimodality in the intermodal transportation networks and the state of the art of scientific works in the field are presented. The intermodal terminal location problem is then studied. We propose a genetic algorithm with mixed encoding for solving this problem and we compare our results with those of literature. A set of problems in the framework of our work on the project DCAS (Direct Cargo Axe Seine), carried by the Grand Port Maritime du Havre, are described and modeled by mathematical programming tools. Thus, we studied the problem of the transfer of rail shuttles which is to optimize the transfer of a set of containers between maritime terminals and a multimodal terminal. We then modeled the scheduling problem of freight trains for placement on rail tracks. These problems are solved by using combined optimization simulation approaches. A first application is based on a genetic algorithm coupled with the multi agent’s simulation. A second is to optimize a rail-rail transshipment of containers using an ant colony algorithm embedded in the simulation model and an agent’s collaboration strategy to minimize waiting times and increase cranes productivity.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Optimization of multimodal transportation problems</title><title xml:lang="fr">Optimisation des problèmes de transport multimodal</title><author role="aut"><persName><forename type="first">Mustapha</forename><surname>OUDANI</surname></persName><email>mustapha.oudani@usmba.ac.ma</email><idno type="halauthor">1346879</idno><affiliation ref="#struct-87"></affiliation></author><editor role="depositor"><persName><forename>Mustapha</forename><surname>OUDANI</surname></persName><email>mustapha.oudani@usmba.ac.ma</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-06-07 12:06:01</date><date type="whenModified">2016-06-10 01:09:36</date><date type="whenReleased">2016-06-09 10:44:20</date><date type="whenProduced">2016-05-21</date><date type="whenEndEmbargoed">2016-06-07</date><ref type="file" target="https://tel.archives-ouvertes.fr/tel-01327923/document"><date notBefore="2016-06-07"></date></ref><ref type="file" n="1" target="https://tel.archives-ouvertes.fr/tel-01327923/file/Th%C3%A8seOudani.pdf"><date notBefore="2016-06-07"></date></ref></edition><respStmt><resp>contributor</resp><name key="302582"><persName><forename>Mustapha</forename><surname>OUDANI</surname></persName><email>mustapha.oudani@usmba.ac.ma</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">tel-01327923</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-01327923</idno><idno type="halBibtex">oudani:tel-01327923</idno><idno type="halRefHtml">Informatique [cs]. Normandie université / Université Sidi Mohamed Ben Abdellah, 2016. Français. <NNT : 2016NORMLH05></idno><idno type="halRef">Informatique [cs]. Normandie université / Université Sidi Mohamed Ben Abdellah, 2016. Français. <NNT : 2016NORMLH05></idno></publicationStmt><seriesStmt><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno></seriesStmt><notesStmt></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Optimization of multimodal transportation problems</title><title xml:lang="fr">Optimisation des problèmes de transport multimodal</title><author role="aut"><persName><forename type="first">Mustapha</forename><surname>OUDANI</surname></persName><email>mustapha.oudani@usmba.ac.ma</email><idno type="halAuthorId">1346879</idno><affiliation ref="#struct-87"></affiliation></author></analytic><monogr><idno type="nnt">2016NORMLH05</idno><imprint><date type="dateDefended">2016-05-21</date></imprint><authority type="institution">Normandie université / Université Sidi Mohamed Ben Abdellah</authority><authority type="school">Sciences Physiques, Mathématiques et de l’Information pour l’Ingénieur</authority><authority type="supervisor">Jaouad Boukachour / Ahmed El Hilali Alaoui</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Combinatorial optimization</term><term xml:lang="en"> multimodal transportation</term><term xml:lang="en"> metaheuristics</term><term xml:lang="en"> operations research</term><term xml:lang="en"> port logistics</term><term xml:lang="fr">Optimisation Combinatoire</term><term xml:lang="fr"> Transport multimodal</term><term xml:lang="fr"> conteneurisation</term><term xml:lang="fr"> métaheuristiques</term><term xml:lang="fr"> recherche opérationnelle</term><term xml:lang="fr"> logistique portuaire </term></keywords><classCode scheme="halDomain" n="info">Computer Science [cs]</classCode><classCode scheme="halTypology" n="THESE">Theses</classCode></textClass><abstract xml:lang="en">This thesis is a contribution to research on the optimization of multimodal transport problems. The main key concepts of multimodality in the intermodal transportation networks and the state of the art of scientific works in the field are presented. The intermodal terminal location problem is then studied. We propose a genetic algorithm with mixed encoding for solving this problem and we compare our results with those of literature. A set of problems in the framework of our work on the project DCAS (Direct Cargo Axe Seine), carried by the Grand Port Maritime du Havre, are described and modeled by mathematical programming tools. Thus, we studied the problem of the transfer of rail shuttles which is to optimize the transfer of a set of containers between maritime terminals and a multimodal terminal. We then modeled the scheduling problem of freight trains for placement on rail tracks. These problems are solved by using combined optimization simulation approaches. A first application is based on a genetic algorithm coupled with the multi agent’s simulation. A second is to optimize a rail-rail transshipment of containers using an ant colony algorithm embedded in the simulation model and an agent’s collaboration strategy to minimize waiting times and increase cranes productivity.</abstract><abstract xml:lang="fr">Cette thèse est une contribution aux travaux de recherche sur l’optimisation des problèmes du transport multimodal. Les principaux concepts clé de la multimodalité dans les réseaux du transport intermodal et l’état de l’art des travaux scientifique du domaine y sont présentés. Le problème de la localisation des terminaux du transport combiné est ensuite étudié. Nous proposons un algorithme génétique à codage mixte pour la résolution de ce problème et nous comparons nos résultats avec ceux de la littérature. Un ensemble de problèmes posés dans le cadre de notre travail sur le projet DCAS (Direct Cargo Axe Seine), porté par le Grand Port Maritime du Havre, y est décrit et modélisé par des outils de programmation mathématique. Ainsi, nous avons étudié le problème du transfert de navettes ferroviaires qui consiste à optimiser le transfert d’un ensemble de conteneurs entre des terminaux maritimes et un terminal multimodal. Ensuite, nous avons modélisé le problème d’ordonnancement des trains de grandes de lignes pour le placement sur les voies de la cour ferroviaire du terminal multimodal du Havre. Ces problèmes sont résolus en utilisant une approche combinée optimisation-simulation. Une première application est basée sur un algorithme génétique couplé avec la simulation multi agents pour l’affectation des voies aux trains. Une deuxième, consiste à optimiser la manutention des conteneurs lors d’un transbordement rail-rail en utilisant un algorithme de colonie de fourmis intégré dans le modèle de simulation et une stratégie de collaboration agents pour minimiser les temps d’attente des portiques et ainsi augmenter leurs productivités</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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