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HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS
Author(s) -
Glover Fred
Publication year - 1977
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.1540-5915.1977.tb01074.x
Subject(s) - heuristics , mathematical optimization , heuristic , integer programming , class (philosophy) , constraint (computer aided design) , set (abstract data type) , integer (computer science) , computer science , linear programming , nonlinear programming , function (biology) , mathematics , nonlinear system , artificial intelligence , physics , geometry , quantum mechanics , evolutionary biology , biology , programming language
This paper proposes a class of surrogate constraint heuristics for obtaining approximate, near optimal solutions to integer programming problems. These heuristics are based on a simple framework that illuminates the character of several earlier heuristic proposals and provides a variety of new alternatives. The paper also proposes additional heuristics that can be used either to supplement the surrogate constraint procedures or to provide independent solution strategies. Preliminary computational results are reported for applying one of these alternatives to a class of nonlinear generalized set covering problems involving approximately 100 constraints and 300–500 integer variables. The solutions obtained by the tested procedure had objective function values twice as good as values obtained by standard approaches ( e.g. , reducing the best objective function values of other methods from 85 to 40 on the average. Total solution time for the tested procedure ranged from ten to twenty seconds on the CDC 6600.