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A NOTE ON DECISION‐MAKING CRITERIA FOR ALGORITHM SELECTION: REDUCING GOAL PROGRAMMING COMPUTATIONAL EFFORT
Author(s) -
Schniederjans Marc J.
Publication year - 1985
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.1540-5915.1985.tb01478.x
Subject(s) - simplex algorithm , goal programming , linear programming , computer science , variable (mathematics) , set (abstract data type) , sample (material) , selection (genetic algorithm) , simplex , variables , mathematical optimization , algorithm , machine learning , mathematics , mathematical analysis , chemistry , geometry , chromatography , programming language
Most linear goal programming (LPG) algorithms are based on a simplex‐type solution method that begins with an initial simplex tableau with solution‐set variables (basic variables) consisting of all‐negative deviational variables or all‐positive deviational variables. This note (1) demonstrates how computational solution effort can be reduced by selecting the appropriate all‐negative or all‐positive deviational variable algorithm and (2) describes a procedure that can be used to aid decision makers in selecting the appropriate algorithm for all types of applied goal programming (GP) models. Results of this study reveal the procedure as accurate and providing computational time savings when applied to a large sample of GP problems.