Stochastic multi-objective optimization: a survey on non-scalarizing methods

Currently, stochastic optimization on the one hand and multi-objective optimization on the other hand are rich and well-established special fields of Operations Research. Much less developed, however, is their intersection: the analysis of decision problems involving multiple objectives and stochast...

Full description

Saved in:
Bibliographic Details
Published in:Annals of operations research 2016-01, Vol.236 (2), p.475-499
Main Authors: Gutjahr, Walter J., Pichler, Alois
Format: Article
Language:English
Subjects:
Algorithms
Asset allocation
Business and Management
Combinatorics
Computation
Decision making
Economics
Expected values
Linear programming
Mathematical optimization
Methods
Multiple criteria decision making
Operations research
Operations Research/Decision Theory
Optimization
Pareto optimum
Preserves
Production management
Production planning
Random variables
Stochastic models
Stochastic processes
Stochasticity
Studies
Theory of Computation
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Currently, stochastic optimization on the one hand and multi-objective optimization on the other hand are rich and well-established special fields of Operations Research. Much less developed, however, is their intersection: the analysis of decision problems involving multiple objectives and stochastically represented uncertainty simultaneously. This is amazing, since in economic and managerial applications, the features of multiple decision criteria and uncertainty are very frequently co-occurring. Part of the existing quantitative approaches to deal with problems of this class apply scalarization techniques in order to reduce a given stochastic multi-objective problem to a stochastic single-objective one. The present article gives an overview over a second strand of the recent literature, namely methods that preserve the multi-objective nature of the problem during the computational analysis. We survey publications assuming a risk-neutral decision maker, but also articles addressing the situation where the decision maker is risk-averse. In the second case, modern risk measures play a prominent role, and generalizations of stochastic orders from the univariate to the multivariate case have recently turned out as a promising methodological tool. Modeling questions as well as issues of computational solution are discussed.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-013-1369-5