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			<TitleText>Thèses de l'Université catholique de Louvain (UCL)</TitleText>
			
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		<TitleText textcase="01">Advanced Modelling and Search Techniques for Routing and Scheduling Problems</TitleText>
		
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		<PersonName>Charles Thomas</PersonName> 
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		<BiographicalNote language="eng" textformat="02">&lt;p&gt;Charles Thomas graduated from UCLouvain in 2016 with a Master degree in Computer Science with a major in Artificial Intelligence. His studies introduced him to the fields of Combinatorial Optimisation and Constraint Programming. Following his Master, he started a Ph.D. in Applied Sciences under the supervision of Prof. Pierre Schaus. During his Ph.D. Charles was involved computer science courses as teaching assistant. He was also collaborator for the PRESupply project, a research project financed by the Walloon Region which aimed at designing generic and affordable optimisation solutions in the domain of supply chain and operations for small and medium businesses.&lt;/p&gt;</BiographicalNote>
		
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		<Text language="eng" textformat="02">&lt;p&gt;This thesis presents the application of several constraint programming techniques to combinatorial problems. In particular, hybrid scheduling and routing problems such as Dial-A-Ride Problems (DARP) are explored. A variant of this problem, the Patient Transportation Problem (PTP) is formalized and resolved. Various approaches to model the PTP and DARP are studied, including a scheduling model and a classical successor model. The usage of sequence variables to model the routes of vehicles is investigated. Two different implementations of a sequence variable are presented as well as several global constraints used in conjunction with these variables to provide efficient propagation algorithms.&lt;br /&gt;
Additionally, the use of an adaptive variant of the Large Neighborhood Search (LNS) is considered in a black-box context, without prior knowledge about the problem being solved. The approach studied uses a portfolio of different heuristics combined with a selection mechanism to adapt the heuristics used to the current problem during the search. Experimental results show the efficiency of the techniques proposed and hint at promising research directions in the domain of PTP-like problems, sequence variables and adaptive LNS.&lt;/p&gt;</Text>
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		<Text language="eng" textformat="02">&lt;p&gt;This thesis presents the application of several constraint programming techniques to combinatorial problems. In particular, hybrid scheduling and routing problems such as Dial-A-Ride Problems (DARP) are explored. A variant of this problem, the Patient Transportation Problem (PTP) is formalized and resolved. Various approaches to model the PTP and DARP are studied, including a scheduling model and a classical successor model. The usage of sequence variables to model the routes of vehicles is investigated. Two different implementations of a sequence variable are presented as well as several global constraints used in conjunction with these variables to provide efficient propagation algorithms.&lt;br /&gt;
Additionally, the use of an adaptive variant of the Large Neighborhood Search (LNS) is considered in a black-box context, without prior knowledge about the problem being solved. The approach studied uses a portfolio of different heuristics combined with a selection mechanism to adapt the heuristics used to the current problem during the search. Experimental results show the efficiency of the techniques proposed and hint at promising research directions in the domain of PTP-like problems, sequence variables and adaptive LNS.&lt;/p&gt;</Text>
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		<Text language="eng">This thesis presents the application of several constraint programming techniques to combinatorial problems. In particular, hybrid scheduling and routing problems such as Dial-A-Ride Problems (DARP) are explored. A variant of this problem, the Patient Transportation Problem (PTP) is formalized and resolved.</Text>
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		<Text textformat="02">&lt;p&gt;1 Introduction 1&lt;br /&gt;
2 Background 5&lt;br /&gt;
3 Adaptive Large Neighborhood Search 33&lt;br /&gt;
4 Patient Transportation Problem 47&lt;br /&gt;
5 Sequence Variables 73&lt;br /&gt;
6 Conclusion 117&lt;br /&gt;
Bibliography 121&lt;/p&gt;

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