We present methods that are useful in solving some large scale hierarchical planning models involving 0-1 variables. These 0-1 programming problems initially could not be solved with any standard techniques. We employed several approaches to take advantage of the hierarchical structure of variables (ordered by importance) and other structures present in the models. Critical, but not sufficient for success, was a strong linear programming formulation. We describe methods for strengthening the linear programs, as well as other techniques necessary for a commercial branch-and-bound code to be successful in solving these problems.

This paper discusses the computation of LU factorizations for large sparse matrices with emphasis on large-scale linear programming bases. We present new implementation techniques which reduce the computation times significantly. Numerical experiments with large-scale real life test problems were conducted. The software is compared with the basis factorization of MPSX/370, IBM's commercial LP system. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

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The dual simplex algorithm has become a strong contender in solving large scale LP problems. One key problem of any dual simplex algorithm is to obtain a dual feasible basis as a starting point. We give an overview of methods which have been proposed in the literature and present new stable and efficient ways to combine them within a state-of-the-art optimization system for solving real world linear and mixed integer programs. Furthermore, we address implementation aspects and the connection between dual feasibility and LP-preprocessing. Computational results are given for a large set of large scale LP problems, which show our dual simplex implementation to be superior to the best existing research and open-source codes and competitive to the leading commercial code on many of our most difficult problem instances.