- http://www.sciencedirect.com/science/journal/15725286
Final published version

Research output: Contribution to journal › Journal article › peer-review

Published

<mark>Journal publication date</mark> | 11/2014 |
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<mark>Journal</mark> | Discrete Optimization |

Volume | 14 |

Number of pages | 15 |

Pages (from-to) | 111-125 |

Publication Status | Published |

<mark>Original language</mark> | English |

We consider a special class of axial multi-dimensional assignment problems called multidimensional 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 sum of the costs of the m-tuples is minimized. The main motivation comes from a yield optimization problem in semiconductor manufacturing. We consider a particular class of polynomial-time heuristics for MVA, namely the sequential heuristics, and we study their approximation ratio. In particular, we show that when the cost function is monotone and subadditive, sequential heuristics have a finite approximation ratio for every fixed m. Moreover, we establish smaller approximation ratios when the cost function is submodular and, for a specific sequential heuristic, when the cost function 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.