Hybrid meta-heuristics for combinatorial optimization

Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling. Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services. The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.openDottorato di ricerca in Ingegneria industriale e dell'informazioneopenUrli, Tommas

An artificial bee colony algorithm for public bike repositioning problem

Paper PresentationConference Theme: Informing transport’s future through practical researchPublic bike repositioning is crucial in public bike sharing systems due to the imbalanced distribution of public bikes. This paper models the public bike repositioning problem (PBRP) involving two non-linear objectives, which are to minimize total service duration and the duration of the longest vehicle route. It includes practical constraints such as the tolerance of demand dissatisfaction and the limitation of duration on the longest route. These objective functions and constraints make the PBRP become NP-hard, so here introduces an artificial bee colony (ABC) algorithm to solve this PBRP. Three neighbourhood operators are introduced to improve the solution search. A modified ABC is proposed to further improve the solution quality. The performance of the modified heuristic was evaluated with the network of Vélib', and compared with the original heuristic and the Genetic Algorithm. These results may therefore prove that the modified heuristic can be an alternative to solve the PBRP. The numerical studies demonstrated that the two objective functions performed differently in which the increase in fleet size may not improve the objective value. This paper will therefore discuss on the practical implications of the trade-offs and provide suggestions about similar repositioning operations.postprin

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.postprin

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station must be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances

Diseño de estrategias para el reposicionamiento de unidades en Sistemas Públicos de Bicicletas

RESUMEN: Los Sistemas Públicos de Bicicletas (SPB) son un medio de transporte económico y amigable con el medio ambiente para recorrer distancias medianas y cortas al interior de las ciudades. Estos poseen una demanda asimétrica que los caracteriza generando así abundancia o escasez de bicicletas y puntos de anclajes en las estaciones a lo largo de la operación. Se propone un modelo matemático de optimización de programación binaria que tiene como objetivo agrupar las estaciones de un SPB en zonas de reposicionamiento, teniendo impacto en un horizonte de planeación táctico. El modelo considera aspectos adicionales a los geográficos. Se realizan pruebas con instancias reales de un SPB con 452 estaciones

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Bicycle sharing systems can significantly reduce traffic, pollution, and the need for parking spaces in city centers. One of the keys to success for a bicycle sharing system is the efficiency of rebalancing operations, where the number of bicycles in each station has to be restored to its target value by a truck through pickup and delivery operations. The Static Bicycle Rebalancing Problem aims to determine a minimum cost sequence of stations to be visited by a single vehicle as well as the amount of bicycles to be collected or delivered at each station. Multiple visits to a station are allowed, as well as using stations as temporary storage. This paper presents an exact algorithm for the problem and results of computational tests on benchmark instances from the literature. The computational experiments show that instances with up to 60 stations can be solved to optimality within 2 hours of computing time

A decomposition approach for the tactical and operational management of bike sharing systems

Die Mobilitätsbedürfnisse der Menschen in Großstädten steigen und mit ihnen auch Straßenauslastung und Umweltverschmutzung. Bike-Sharing-Systeme bieten eine nachhaltige Alternative für den öffentlichen Personennahverkehr, die einige der Verkehrsprobleme entschärfen könnte. Dafür bedarf es großer Nutzerzahlen, die durch entsprechende Servicequalität erreicht werden können. Dabei besteht die Herausforderung darin, dass dem im Betrieb entstehenden Ungleichgewicht in der räumlichen Verteilung der Fahrräder im System entgegengewirkt wird. Damit den Kunden zu jeder Zeit Fahrräder und Fahrradstellplätze zur Verfügung stehen, müssen Maßnahmen zur Repositionierung der Fahrräder getroffen werden. Diese Maßnahmen bestehen aus dem Transport der Räder von Stationen mit Stellplatzbedarf zu Stationen mit Fahrradbedarf. Die vorliegende Arbeit untersucht das Problem der Repositionierung und stellt ein Konzept eines dekomponierten Optimierungsverfahrens vor, welches mittels antizipierender stochastischer und dynamischer Optimierung die Menge der für die Repositionierung relevanten Stationen ermittelt. Auf Basis dieses Ergebnisses schließt sich die heuristische Tourenplanung an.Mobility requirements of people in large cities are increasing. Some consequences are congested roads and environmental pollution. Bike sharing systems provide a sustainable alternative for public transportation which could help reducing traffic problems. A good service level is needed to achieve a high number of users. The challenge is to counter the spatial imbalance of the bike distribution across the system. To ensure the availability of bikes and bike racks, bikes must be repositioned. Repositioning actions consist of bike transports from stations with a surplus to stations with a lack of bikes. This dissertation considers the repositioning problem and introduces a decomposed optimization approach which uses anticipatory stochastic optimization to determine the relevant stations for the repositioning actions. Based on these results, the subsequent routing problem is solved heuristically

Robust Repositioning to Counter Unpredictable Demand in Bike Sharing Systems

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ