Approximation Algorithms for Multi-Dimensional Vector Assignment Problems

E-print/Working paper (2013)

We consider a special class of axial multi-dimensional assignment problems called multi-dimensional vector assignment (MVA) problems. An instance of the MVA problem is defined by $m$ disjoint sets, each ... [more ▼]

We consider a special class of axial multi-dimensional assignment problems called multi-dimensional vector assignment (MVA) problems. An instance of the MVA problem is defined by $m$ disjoint sets, each of which contains the same number $n$ of $p$-dimensional vectors with nonnegative integral components, and a cost function defined on vectors. The cost of an $m$-tuple of vectors is defined as the cost of their component-wise maximum. The problem is now to partition the $m$ sets of vectors into $n$ $m$-tuples so that no two vectors from the same set are in the same $m$-tuple and so that the total cost of the $m$-tuples is minimized. The main motivation comes from a yield optimization problem in semi-conductor manufacturing. We consider two classes of polynomial-time heuristics for MVA, namely, hub heuristics and sequential heuristics, and we study their approximation ratio. In particular, we show that when the cost function is monotone and subadditive, hub heuristics, as well as sequential heuristics, have finite approximation ratio for every fixed $m$. Moreover, we establish better approximation ratios for certain variants of hub heuristics and sequential heuristics when the cost function is monotone and submodular, or when it is additive. We provide examples to illustrate the tightness of our analysis. Furthermore, we show that the MVA problem is APX-hard even for the case $m=3$ and for binary input vectors. Finally, we show that the problem can be solved in polynomial time in the special case of binary vectors with fixed dimension $p$. [less ▲]

Counting and enumerating aggregate classifiers

in Discrete Applied Mathematics (2008), 156(3), 2459-2468

We propose a generic model for the "weighted voting" aggregation step performed by several methods in supervised classification. Further, we construct an algorithm to count the number of distinct ... [more ▼]

We propose a generic model for the "weighted voting" aggregation step performed by several methods in supervised classification. Further, we construct an algorithm to count the number of distinct aggregate classifiers that arise in this model. When there are only two classes in the classification problem, we show that a class of functions that arises from aggregate classifiers coincides with the class of self-dual positive threshold Boolean functions. [less ▲]

Production planning problems in printed circuit board assembly

in Discrete Applied Mathematics (2002), 123(1-3), 339-361

This survey describes some of the main optimization problems arising in the context of production planning for the assembly of printed circuit boards. The discussion is structured around a hierarchical ... [more ▼]

This survey describes some of the main optimization problems arising in the context of production planning for the assembly of printed circuit boards. The discussion is structured around a hierarchical decomposition of the planning process into distinct optimization subproblems, addressing issues such as the assignment of board types to machine groups, the allocation of component feeders to individual machines, the determination of optimal production sequences, etc, The paper reviews the literature on this topic with an emphasis on the most recent developments, on the fundamental structure of the mathematical models and on the relation between these models and some 'environmental' variables such as the layout of the shop or the product mix. (C) 2002 Elsevier Science B.V, All rights reserved. [less ▲]