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PORTFOLIO MODELING IN MULTIPLE‐CRITERIA SITUATIONS UNDER UNCERTAINTY
Author(s) -
Muhlemann Alan Paul,
Lockett Alan Geoffrey,
Gear Anthony Edward
Publication year - 1978
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1978.tb00749.x
Subject(s) - portfolio , mathematical optimization , computer science , maximization , variety (cybernetics) , selection (genetic algorithm) , stochastic programming , portfolio optimization , project portfolio management , tree (set theory) , operations research , integer programming , mathematics , project management , machine learning , artificial intelligence , economics , mathematical analysis , management , financial economics
There have been many models for portfolio selection, but most do not explicitly include uncertainty and multiple objectives. This paper presents an approach that includes these aspects using a form of stochastic integer programming with recourse. The method involves the use of a time‐based decision tree structure called a “project tree.” Using this basic format, an illustrative six‐project example is presented and analyzed. Various forms of objectives are discussed, ranging from the maximization of expected portfolio value to the maximization of the minimum weighted portfolio deviation from two goals. In each case, formulated numerical problems are given, and the solutions derived are presented. The approach is shown to be very flexible and capable of handling a variety of situations and objectives.