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Disjunctive programming and a hierarchy of relaxations for discrete optimization problems. (English) Zbl 0592.90070
Summary: We discuss a new conceptual framework for the convexification of discrete optimization problems, and a general technique for obtaining approximations to the convex hull of the feasible set. The concepts come from disjunctive programming and the key tool is a description of the convex hull of a union of polyhedra in terms of a higher dimensional polyhedron. Although this description was known for several years, only recently was it shown by R. G. Jeroslow and J. K. Lowe [Math. Program. Study. 22, 167-184 (1984; Zbl 0554.90081)] to yield improved representations of discrete optimization problems. We express the feasible set of a discrete optimization problem as the intersection (conjunction) of unions of polyhedra, and define an operation that takes one such expression into another, equivalent one, witgh fewer conjuncts. We then introduce a class of relaxations based on replacing each conjunct (union of polyhedra) by its convex hull. The strength of the relaxations increases as the number of conjuncts decreases, and the class of relaxations forms a hierachy that spans the spectrum between the common linear programming relaxation, and the convex hull of the feasible set itself. Instances where this approach has advantages include critical path problems in disjunctive graphs, network synthesis problems, certain fixed charge network flow problems, etc. We illustrate the approach on the first of these problems, which is a model for machine sequencing.

MSC:
90C10 Integer programming
52Bxx Polytopes and polyhedra
90B35 Deterministic scheduling theory in operations research
90B10 Deterministic network models in operations research
90C35 Programming involving graphs or networks
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