Schröder, Tim

[["dc.bibliographiccitation.firstpage","1005"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","European Journal of Operational Research"],["dc.bibliographiccitation.lastpage","1019"],["dc.bibliographiccitation.volume","264"],["dc.contributor.author","Schröder, Tim"],["dc.contributor.author","Lauven, Lars-Peter"],["dc.contributor.author","Geldermann, Jutta"],["dc.date.accessioned","2020-12-10T14:23:40Z"],["dc.date.available","2020-12-10T14:23:40Z"],["dc.date.issued","2018"],["dc.description.abstract","Biorefineries can provide a product portfolio from renewable biomass similar to that of crude oil refineries. To operate biorefineries of any kind, however, the availability of biomass inputs is crucial and must be considered during planning. Here, we develop a planning approach that uses Geographic Information Systems (GIS) to account for spatially scattered biomass when optimizing a biorefinery’s location, capacity, and configuration. To deal with the challenges of a non-smooth objective function arising from the geographic data, higher dimensionality, and strict constraints, the planning problem is repeatedly decomposed by nesting an exact nonlinear program (NLP) inside an evolutionary strategy (ES) heuristic, which handles the spatial data from the GIS. We demonstrate the functionality of the algorithm and show how including spatial data improves the planning process by optimizing a synthesis gas biorefinery using this new planning approach."],["dc.identifier.doi","10.1016/j.ejor.2017.01.016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72009"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.orgunit","Professur für Produktion und Logistik"],["dc.rights","CC BY-NC-ND 4.0"],["dc.title","Improving biorefinery planning: Integration of spatial data using exact optimization nested in an evolutionary strategy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","submitted_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","287"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Central European Journal of Operations Research"],["dc.bibliographiccitation.lastpage","309"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Schröder, Tim"],["dc.contributor.author","Lauven, Lars-Peter"],["dc.contributor.author","Beyer, Beatriz"],["dc.contributor.author","Lerche, Nils"],["dc.contributor.author","Geldermann, Jutta"],["dc.date.accessioned","2020-12-10T14:11:10Z"],["dc.date.available","2020-12-10T14:11:10Z"],["dc.date.issued","2019"],["dc.description.abstract","Investment and policy decisions in the context of sustainable development are classic application areas for multi-criteria decision analysis. Ranking various pathways, i.e. conversion routes, for biomass use in the energy sector is particularly challenging. Depending on how ecological, economic, and social criteria are weighed, a multi-criteria decision analysis can lead to significantly contrasting recommendations. In this paper, we present a decision support for eleven energy pathways using decision criteria drawn from all three sustainability dimensions—ecological, economic, and social. For the graphical presentation of the relatively large number of pathways and criteria weightings, we introduce a novel visualization approach that combines the results of both PROMETHEE I and II. This visualization approach permits stakeholders to quickly and intuitively gather insights about the result structure and the consequences of different input parameters, for instance different criteria weightings."],["dc.identifier.doi","10.1007/s10100-018-0590-3"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/70989"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.orgunit","Professur für Produktion und Logistik"],["dc.rights","CC BY 4.0"],["dc.title","Using PROMETHEE to assess bioenergy pathways"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","submitted_version"],["dspace.entity.type","Publication"]]