A NOTE ON DECISION‐MAKING CRITERIA FOR ALGORITHM SELECTION: REDUCING GOAL PROGRAMMING COMPUTATIONAL EFFORT
展示了如何通过选择适当的负偏差变量或正偏差变量算法来减少线性目标规划的计算工作量，并描述了一个帮助决策者为各类目标规划模型选择合适算法的程序。
ABSTRACT 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.