Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 264, Issue 3, Pages 1005-1019Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2017.01.016
Keywords
Evolutionary computations; Biorefinery; Evolutionary strategy; Geographic Information System; Location planning; NLP
Funding
- German Research Foundation (RTG) [GRK 1703/1]
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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. (C) 2017 Elsevier B.V. All rights reserved.
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