TY - JOUR
T1 - Solving discrete multi-objective optimization problems using modified augmented weighted Tchebychev scalarizations
AU - Holzmann, Tim
AU - Smith, J. C.
N1 - Funding Information:
The authors are grateful to three anonymous referees for their thorough and insightful comments, which led to an improved exposition of the paper. This work was supported by the Air Force Office of Scientific Research (grant no. FA9550-12-1-0353); and the Office of Naval Research (grant nos. N00014-13-1-0036, N00014-17-1-2421). The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense or the U.S. Government.
Funding Information:
The authors are grateful to three anonymous referees for their thorough and insightful comments, which led to an improved exposition of the paper. This work was supported by the Air Force Office of Scientific Research (grant no. FA9550-12-1-0353 ); and the Office of Naval Research (grant nos. N00014-13-1-0036 , N00014-17-1-2421 ). The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense or the U.S. Government.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - In this paper we present the modified augmented weighted Tchebychev norm, which can be used to generate a complete efficient set of solutions to a discrete multi-objective optimization problem. We contribute a generating algorithm that will, without supervision, generate the entire non-dominated set for any number of objectives. To our knowledge, this is the first generating method for general discrete multi-objective problems that uses a variant of the Tchebychev norm. In a computational study, our algorithm's running times are comparable to previously proposed algorithms.
AB - In this paper we present the modified augmented weighted Tchebychev norm, which can be used to generate a complete efficient set of solutions to a discrete multi-objective optimization problem. We contribute a generating algorithm that will, without supervision, generate the entire non-dominated set for any number of objectives. To our knowledge, this is the first generating method for general discrete multi-objective problems that uses a variant of the Tchebychev norm. In a computational study, our algorithm's running times are comparable to previously proposed algorithms.
KW - Computational optimization
KW - Generating methods
KW - Multiple objective programming
KW - Tchebychev norm
UR - http://www.scopus.com/inward/record.url?scp=85048754440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048754440&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2018.05.036
DO - 10.1016/j.ejor.2018.05.036
M3 - Article
AN - SCOPUS:85048754440
SN - 0377-2217
VL - 271
SP - 436
EP - 449
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -