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COST‐VOLUME‐PROFIT ANALYSIS IN STOCHASTIC PROGRAMMING MODELS
Author(s) -
Chen Joyce T.
Publication year - 1980
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.1540-5915.1980.tb01166.x
Subject(s) - probabilistic logic , mathematical optimization , computer science , flexibility (engineering) , stochastic programming , quadratic programming , linear programming , parametric statistics , parametric programming , efficient frontier , operations research , mathematics , economics , statistics , portfolio , artificial intelligence , financial economics
This paper applies mathematical programming to cost‐volume‐profit (CVP) analysis under contribution margin uncertainty. Three CVP probabilistic chance‐constraint models based on various safety‐first criteria for decisions under uncertainty are presented and compared. It is shown that a break‐even segment of the mean‐standard deviation frontier is a set of optimal solutions for the proposed models. An operational parametric quadratic programming (QP) model is constructed, and the efficiency frontier is generated. The procedures for locating an optimal solution on the efficiency frontier are then presented. The recommended QP procedure offers both technical relief from the computational difficulties posed by the probabilistic constraints and a desired flexibility in generating and presenting the relevant information for decisions under uncertainty.