This volume examines two important classes that are characteristic of combinatorial optimization problems: sequencing and scheduling (in which a set of objects has to be ordered subject to a number of conditions), and packing and partitioning (in which a set of objects has to be split into subsets in order to meet a certain objective). These classes of problems encompass a wide range of practical applications, from production planning and flexible manufacturing to computer scheduling and
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This is a theoretical analysis of a probabilistic approach to solving packing or partitioning algorithms. These generally require the partitioning of a set of nonnegative numbers so that the sums of the elements in the blocks of the partition satisfy some given property. Departs from previous research on these types of algorithms in that it takes a probabilistic rather than a heuristic approach to solving them.
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