A Bus Driver Scheduling Problem: a new mathematicalmodel and a GRASP approximate solution

Autor: E. Marchitto, Paola Festa, Renato De Leone

Publikováno v: Journal of heuristics 17 (2011): 441–466.
info:cnr-pdr/source/autori:Festa, P.; De Leone, R.; Marchitto, E./titolo:A Bus Driver Scheduling Problem: a new mathematical model and a GRASP approximate solution/doi:/rivista:Journal of heuristics/anno:2011/pagina_da:441/pagina_a:466/intervallo_pagine:441–466/volume:17

This paper addresses the problem of determining the best scheduling for Bus Drivers, a $\mathcal{NP}$ -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 regulati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86d1216cb10418103bf200f97e0a065e
http://hdl.handle.net/11581/202858

A new meta-heuristic for the Bus Driver Scheduling Problem: GRASP combined with Rollout

Autor: Paola Festa, R. De Leone, G. D'Annibale, E. Marchitto

Publikováno v: CISched

The 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 formula

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::024fe85c1fc3086f0ca183ed4c8b37c4
https://doi.org/10.1109/scis.2007.367689

New hybrid heuristics for the Bus Driver Scheduling Problem

Autor: R. DE LEONE, E. MARCHITTO, FESTA, PAOLA

The goal of this paper is to propose and experimentally compare a class of hybrid metaheuristics for the Bus Driver Scheduling Problem. To link our investigation to real world scenarios, we have cooperated with PluService Srl of Senigallia. We briefl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3730::ef33e86784fc370d4b4400bd9ef87a83
http://hdl.handle.net/11588/118626

Integrating support vector machines and neural networks

Autor: Renato De Leone, E. Marchitto, Rosario Capparuccia

Publikováno v: Neural networks : the official journal of the International Neural Network Society. 20(5)

Support 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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b9c3471dcd914135b7f1e49e6e128c3
https://pubmed.ncbi.nlm.nih.gov/17306960

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