TY - JOUR

T1 - Linear solution schemes for Mean-SemiVariance Project portfolio selection problems

T2 - An application in the oil and gas industry

AU - Sefair, Jorge

AU - Méndez, Carlos Y.

AU - Babat, Onur

AU - Medaglia, Andrés L.

AU - Zuluaga, Luis F.

PY - 2014/8/18

Y1 - 2014/8/18

N2 - We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of . non-divisible projects when the risk is measured by the semivariance of the portfolio's Net-Present Value (NPV) and the reward is measured by the portfolio's expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two . linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.

AB - We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of . non-divisible projects when the risk is measured by the semivariance of the portfolio's Net-Present Value (NPV) and the reward is measured by the portfolio's expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two . linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.

KW - Benders decomposition

KW - Mean-SemiVariance

KW - Petroleum industry

KW - Project portfolio optimization

KW - Project selection

KW - Risk

KW - Semivariance

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U2 - 10.1016/j.omega.2016.05.007

DO - 10.1016/j.omega.2016.05.007

M3 - Article

AN - SCOPUS:85002766273

JO - Omega (United States)

JF - Omega (United States)

SN - 0030-2228

ER -