Abstract
In many applications the number of decision variables is large. The feasible decision set is defined by many constraints in a high dimensional space, and therefore it has a lot of vertices and faces. Its descriptive analysis becomes costly and time consuming. On the other hand, the number of objective functions is limited, frequently does not exceed four or five. This leads to the outcome or value set having far fewer vertices and faces and much simpler structure. Because of this advantage outcome space methods are aimed at developing algorithms to compute efficient vertices and efficient faces of the value set in the outcome space.
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© 2016 Springer International Publishing Switzerland
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Luc, D.T. (2016). Outcome Space Method. In: Multiobjective Linear Programming. Springer, Cham. https://doi.org/10.1007/978-3-319-21091-9_9
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DOI: https://doi.org/10.1007/978-3-319-21091-9_9
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21090-2
Online ISBN: 978-3-319-21091-9
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