caa a22 4500 606233083 CHVBK 20210128101230.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10287-015-0238-x doi (NATIONALLICENCE)springer-10.1007/s10287-015-0238-x Detection of local tourism systems by threshold accepting [Elektronische Daten] [Joseph Andria, Giacomo di Tollo, Raffaele Pesenti] Despite the importance of tourism as a leading industry in the development of a country's economy, there is a lack of criteria and methodologies for the detection, promotion, and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems the size of which is optimal with respect to geographical, economic, and demographical criteria: we formulate the problem as an optimisation problem and we solve it by a metaheuristic approach; then we compare the obtained results with standard clustering approaches and with an exact optimisation solver. Results show that our approach requires low computational times to provide results that are better than other clustering techniques and than the current approach used by local authorities. Springer-Verlag Berlin Heidelberg, 2015 Threshold accepting nationallicence Clusters nationallicence Optimisation nationallicence Local tourism systems nationallicence Andria Joseph Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università di Palermo, Palermo, Italy aut di Tollo Giacomo Dipartimento di Economia, Universitá Ca' Foscari, Venice, Italy aut Pesenti Raffaele Dipartimento di Management, Universitá Ca' Foscari, Venice, Italy aut Computational Management Science Springer Berlin Heidelberg 12/4(2015-10-01), 559-575 1619-697X 12:4<559 2015 12 10287 https://doi.org/10.1007/s10287-015-0238-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10287-015-0238-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Andria Joseph Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università di Palermo, Palermo, Italy aut NATIONALLICENCE 700 1- di Tollo Giacomo Dipartimento di Economia, Universitá Ca' Foscari, Venice, Italy aut NATIONALLICENCE 700 1- Pesenti Raffaele Dipartimento di Management, Universitá Ca' Foscari, Venice, Italy aut NATIONALLICENCE 773 0- Computational Management Science Springer Berlin Heidelberg 12/4(2015-10-01), 559-575 1619-697X 12:4<559 2015 12 10287