Please use this identifier to cite or link to this item: https://doi.org/10.1016/B978-0-444-53711-9.50114-0
Title: Integrating Graph-based Representation and Genetic Algorithm for Large-Scale Optimization: Refinery Crude Oil Scheduling
Authors: Ramteke, M.
Srinivasan, R. 
Keywords: Genetic algorithm
Graphs
Network
Scheduling
Issue Date: 2011
Citation: Ramteke, M., Srinivasan, R. (2011). Integrating Graph-based Representation and Genetic Algorithm for Large-Scale Optimization: Refinery Crude Oil Scheduling. Computer Aided Chemical Engineering 29 : 568-571. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-444-53711-9.50114-0
Abstract: Scheduling optimization problems are often associated with large number of variables and combinatorial constraints. These problems can be represented graphically through a network structure. This graphical representation can provide important insights to handle the combinatorial constraints. In this study, the graphical representation is incorporated in the framework of genetic algorithm to solve large-scale refinery crude oil scheduling problems. Our results show that use of such graphical representation offers significant advantages while solving multi-objective, multi-solution and nonlinear formulations in reasonable computational time. © 2011 Elsevier B.V.
Source Title: Computer Aided Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/117418
ISSN: 15707946
DOI: 10.1016/B978-0-444-53711-9.50114-0
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