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A Decision Support System for Sustainable Waste Collection
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and automation engineering)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and automation engineering)ORCID-id: 0000-0003-3973-3394
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and automation engineering)
2017 (engelsk)Inngår i: International Journal of Decision Support System Technology, ISSN 1941-6296, E-ISSN 1941-630X, Vol. 9, nr 4, s. 49-65, artikkel-id 4Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents a decision support system (DSS) for making the waste collection process more sustainable. Currently, waste collection schedules and routes are created manually in most waste management organizations. Thisis both very time consuming and likely to result in poorsolutions, as the task is extremely difficult due to the large number of bins combined with the many parametersto be considered simultaneously. With a sophisticated DSS, it becomes possible to addressthe complexities of optimal waste collection and improve sustainability—not least from the environmental perspective. The DSS proposed here is designed to be used on the operational level in the waste management organization and supports daily operations and activities. System evaluation indicatesthat it can reduce truck operating time by approximately 25%, corresponding to a saving of approximately 21,300 kg of carbon dioxide and 187 kg of nitrogen oxides per year and truck.

sted, utgiver, år, opplag, sider
I G I Global , 2017. Vol. 9, nr 4, s. 49-65, artikkel-id 4
Emneord [en]
Decision Support System, Simulation-Based Optimization, Sustainability, Waste Collection
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; INF201 Virtual Production Development
Identifikatorer
URN: urn:nbn:se:his:diva-13924DOI: 10.4018/IJDSST.2017100104ISI: 000418547100005Scopus ID: 2-s2.0-85028708937OAI: oai:DiVA.org:his-13924DiVA, id: diva2:1127487
Tilgjengelig fra: 2017-07-15 Laget: 2017-07-15 Sist oppdatert: 2019-11-21bibliografisk kontrollert

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