Abstract
This paper presents the computational results of a new zero-one goal programming algorithm. These results are compared, based upon established criteria, to those obtained using the Lee and Morris algorithm. The applications found in the literature in which sufficient data existed were used as test data. The results of this study suggest that this new approach generates more solution alternatives, identifies better solutions, and takes less than 10% of the CPU time required by the Lee and Morris algorithm. Improvements in the algorithm are presented, and the use of this approach to solve zero-one linear multiple objective programming and zero-one linear programming problems is also discussed.
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