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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Luc, D.T. (2016). Outcome Space Method. In: Multiobjective Linear Programming. Springer, Cham. https://doi.org/10.1007/978-3-319-21091-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-21091-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21090-2
Online ISBN: 978-3-319-21091-9
eBook Packages: Business and ManagementBusiness and Management (R0)