1,720,967 research outputs found
A Bus Driver Scheduling Problem: a new mathematicalmodel and a GRASP approximate solution
No full textThis paper addresses the problem of determining the best scheduling for
Bus Drivers, a N P-hard problem consisting of finding the minimum number of
drivers to cover a set of Pieces-Of-Work (POWs) subject to a variety of rules and
regulations that must be enforced such as spreadover and working time. This problem
is known in literature as Crew Scheduling Problem and, in particular in public
transportation, it is designated as Bus Driver Scheduling Problem. We propose a new
mathematical formulation of a Bus Driver Scheduling Problem under special constraints
imposed by Italian transportation rules. Unfortunately, this model can only
be usefully applied to small or medium size problem instances. For large instances,
a Greedy Randomized Adaptive Search Procedure (GRASP) is proposed. Results
are reported for a set of real-word problems and comparison is made with an exact
method. Moreover, we report a comparison of the computational results obtained
with our GRASP procedure with the results obtained by Huisman et al. (Transp. Sci.
39(4):491–502, 2005)
A GRASP for the bus driver scheduling problem
No full textThis paper addresses the problem of determining the best
scheduling for Bus Drivers, i.e. the problem of finding the
minimum number of drivers required to cover a set of
Piece-Of-Works (POWs) subject to a variety of rules and
regulations that must be enforced such as the overspread and the
working time. This problem is known in literature as Crew
Scheduling Problem and in particular in public transportation it
is designated as Bus Driver Scheduling Problem. The Bus Driver
Scheduling Problem is an extremely complex part of the
Transportation Planning System. Its
combinatorial nature and the large dimension of real-world
problems has led to the development of several heuristics. Wren
and Rousseau~\cite{Wren-conference} give an outline of the Bus
Driver Scheduling Problem (BDSP) and propose various approaches for solving it
Integrating support vector machines and neural networks
No full textSupport vector machines (SVMs) are a powerful technique developed in the last decade to effectively tackle classification and regression problems. In this paper we describe how support vector machines and artificial neural networks can be integrated in order to classify objects correctly. This technique has been successfully applied to the problem of determining the quality of tiles. Using an optical reader system, some features are automatically extracted, then a subset of the features is determined and the tiles are classified based on this subset
Ottimizzazione dei turni dei veicoli e del personale viaggiante nel settore del Trasporto Pubblico Locale
No full textAutoregression and artificial neural networks for financial market forecast
No full textIn recent years the interest of the investors in e±cient methods for the forecasting price trend of a share in financial markets has grown steadily. The aim is to accurately forecast the future behavior of the market in order to identificate the so-called "correct timing".
In this paper we analyze three di®erent approaches for forecasting financial data: Autoregression, artificial neural networks and support vector machines and
we will determine potentials and limits of these methods. Application to the Italian financial market is also presented
Autoregressione e reti neurali artificiali per la previsione finanziaria
No full textIn questi ultimi anni è cresciuto enormente da parte degli investitori sia neofiti che istituzionali nei confronti di tecnologie e metodi volti ad una efficiente previsione dell'andamento dei prezzi di un titol
A new meta-heuristic for the Bus Driver Scheduling Problem: GRASP combined with Rollout
No full textThe bus driver scheduling problem (BDSP) is one of the most important planning decision problems that public transportation companies must solve and that appear as an extremely complex part of the general transportation planning system. It is formulated as a minimization problem whose objective is to determine the minimum number of driver shifts, subject to a variety of rules and regulations that must be enforced, such as overspread and working time. In this article, a greedy randomized adaptive search procedure (GRASP) and a rollout heuristic for BDSP are proposed and tested. A new hybrid heuristic that combines GRASP and rollout is also proposed and tested. Computational results indicate that these randomized heuristics find near-optimal solution
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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