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This paper presents a branch-and-bound algorithm for solving fixed charge transportation problems where not all cells exist. The algorithm exploits the absence of full problem density in several ways, thus yielding a procedure which is especially applicable to solving real world problems which are normally quite sparse. Additionally, streamlined new procedures for pruning the decision tree and calculating penalties are presented. We present computational experience with both a set of large test problems and a set of dense test problems from the literature. Comparisons with other codes are uniformly favorable to the new method, which runs more than twice as fast as the best alternative. [ABSTRACT FROM AUTHOR]

This paper presents two versions of a method for solving large linear programs through decomposition. In both versions the problem is decomposed into subprograms as in the DANTZIG-WOLFE model, but each version uses a different type of linking program. The subprograms are first solved, and then the infeasibility of the resulting solution for the linking program is used to generate a price-adjustment process (much like a market mechanism), which gradually corrects the objective functions of the subprograms, until either some optimal solutions to the latter yield a feasible solution to the linking program (then the problem is solved), or evidence is obtained that the problem has no feasible solution. [ABSTRACT FROM AUTHOR]