Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or the membership of the fuzzy numbers. However, in some cases there may not be enough information for that and grey numbers need to be used. A grey number is an interval number to represent the value of a quantity. Its exact value or the likelihood is not known but the maximum and/or the minimum possible values are. Applications in space exploration, robotics and engineering can be mentioned which involves such a scenario. An optimization problem is called a grey optimization problem if it involves a grey number in the objective function and/or constraint set. Unlike its wide applications, not much research is done in the field. Hence, in this paper, a convex grey optimization problem will be discussed. It will be shown that an optimal solution for a convex grey optimization problem is a grey number where the lower and upper limit are computed by solving the problem in an optimistic and pessimistic way. The optimistic way is when the decision maker counts the grey numbers as decision variables and optimize the objective function for all the decision variables whereas the pessimistic way is solving a minimax or maximin problem over the decision variables and over the grey numbers.
Mots-clés : Grey optimization, interval optimization, convex optimization, uncertainty
@article{RO_2019__53_1_339_0,
author = {Tilahun, Surafel Luleseged},
title = {Convex {Grey} {Optimization}},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {339--349},
publisher = {EDP-Sciences},
volume = {53},
number = {1},
year = {2019},
doi = {10.1051/ro/2018088},
zbl = {1414.90273},
mrnumber = {3912471},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2018088/}
}
TY - JOUR AU - Tilahun, Surafel Luleseged TI - Convex Grey Optimization JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2019 SP - 339 EP - 349 VL - 53 IS - 1 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2018088/ DO - 10.1051/ro/2018088 LA - en ID - RO_2019__53_1_339_0 ER -
Tilahun, Surafel Luleseged. Convex Grey Optimization. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 1, pp. 339-349. doi : 10.1051/ro/2018088. http://archive.numdam.org/articles/10.1051/ro/2018088/
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