JP
J. Pierotti
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2 records found
1
Master thesis
(2020)
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Sébastien Janssens de Bisthoven, Theresia van Essen, Jacopo Pierotti, Karen Aardal, Cornelis Kraaikamp
Automated vehicles have the potential to create a future in which most cars are shared instead of being individually owned and used. The advantages of vehicle sharing are expected to be multiple: reduced traffic, freed-up parking space, safer trips and a lower environmental impact of traveling. The dial-a-ride problem with transfers (DARP-T) consists in finding a set of minimum cost routes that satisfies a set of transportation requests. Requests may share a vehicle and may be transferred from one vehicle to another at any node during their journey. In this thesis, we describe a heuristic solving moderate to large instances of the static heterogeneous DARP-T in which the requests have demanding time constraints. The heuristic builds a solution in a constructive greedy randomized procedure and subsequently improves it with a destroy and repair procedure. The heuristic is tested on instances we randomly generated containing up to 1000 requests traveling between any two of the 100 most populated cities in the Netherlands inside a four-hour timescale. These instances are also solved without transfers to determine the benefits transfers can bring and the user inconvenience they may cause. We also investigate the influence of the demand on the objective function value, look into the influence of the fleet size on the fleet usage, and present some visualizations of the solutions found.
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Automated vehicles have the potential to create a future in which most cars are shared instead of being individually owned and used. The advantages of vehicle sharing are expected to be multiple: reduced traffic, freed-up parking space, safer trips and a lower environmental impact of traveling. The dial-a-ride problem with transfers (DARP-T) consists in finding a set of minimum cost routes that satisfies a set of transportation requests. Requests may share a vehicle and may be transferred from one vehicle to another at any node during their journey. In this thesis, we describe a heuristic solving moderate to large instances of the static heterogeneous DARP-T in which the requests have demanding time constraints. The heuristic builds a solution in a constructive greedy randomized procedure and subsequently improves it with a destroy and repair procedure. The heuristic is tested on instances we randomly generated containing up to 1000 requests traveling between any two of the 100 most populated cities in the Netherlands inside a four-hour timescale. These instances are also solved without transfers to determine the benefits transfers can bring and the user inconvenience they may cause. We also investigate the influence of the demand on the objective function value, look into the influence of the fleet size on the fleet usage, and present some visualizations of the solutions found.
Taxis Dispatch
Minimizing waiting times by maximizing coverage
Bachelor thesis
(2019)
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Maarten van Wijngaarden, Jacopo Pierotti, Theresia van Essen, Jeroen Spandaw, Mark Veraar
In this thesis, we investigate whether maximizing the coverage of taxis can be beneficial when the goal is to minimize the waiting times of the clients. When dispatching taxis, often only current requests are taken into account and not future ones. We examined how beneficial it can be to take coverage into account. For taxi companies it is important to keep their customers satisfied, by serving them as quickly as possible. Often companies assign the taxis to the nearest requests. We investigate whether there are better dispatch methods. We developed three dispatch policies which use ILP models, and discussed what the best option is. The first policy does not consider the coverage and minimizes the waiting times of the clients among the requests that are taking place at that moment. The second one aims to maintain a good coverage and short waiting times when assigning taxis to requests. The third one also aims to maintain a good coverage and short waiting times when assigning taxis to requests and also relocates taxis to gain a better covered area. Those policies can be a contribution for taxi companies because they help to serve clients more quickly. To determine what the best way is of dispatching taxis, we run a simulation on the different policies on a real-data map and compared the results. The literature over taxi dispatch mostly conducts research on a small area like a city or a village. In this thesis, taxis dispatch takes place on a larger area where taxis commute between cities.
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In this thesis, we investigate whether maximizing the coverage of taxis can be beneficial when the goal is to minimize the waiting times of the clients. When dispatching taxis, often only current requests are taken into account and not future ones. We examined how beneficial it can be to take coverage into account. For taxi companies it is important to keep their customers satisfied, by serving them as quickly as possible. Often companies assign the taxis to the nearest requests. We investigate whether there are better dispatch methods. We developed three dispatch policies which use ILP models, and discussed what the best option is. The first policy does not consider the coverage and minimizes the waiting times of the clients among the requests that are taking place at that moment. The second one aims to maintain a good coverage and short waiting times when assigning taxis to requests. The third one also aims to maintain a good coverage and short waiting times when assigning taxis to requests and also relocates taxis to gain a better covered area. Those policies can be a contribution for taxi companies because they help to serve clients more quickly. To determine what the best way is of dispatching taxis, we run a simulation on the different policies on a real-data map and compared the results. The literature over taxi dispatch mostly conducts research on a small area like a city or a village. In this thesis, taxis dispatch takes place on a larger area where taxis commute between cities.