Covering arrays can be applied to the testing of software, hardware and advanced materials, and to the effects of hormone interaction on gene expression. In this paper we develop constraint programming models of the problem of finding an optimal covering array. Our models exploit global constraints, multiple viewpoints and symmetry-breaking constraints. We show that compound variables, representing tuples of variables in our original model, allow the constraints of this problem to be represented more easily and hence propagate better. With our best integrated model, we are able to either prove the optimality of existing bounds or find new optimal solutions, for arrays of moderate size. Local search on a SAT-encoding of the model is able to find improved solutions and bounds for larger problems.

No abstract

No abstract

No abstract

Abstract. In manufacturing, different process designs give rise to different schedules and with each an associated cost. In this paper, we report on a real-life example where a manufacturing company wants to evaluate the scheduling implications related to the degree of coupling between their processes of moulding and casting, in terms of the amount of buffer stock held. The results show that the present configuration could be improved as regards the amount of stock, while still meeting the demand levels. We show this as one example of a process design evaluation and propose in this paper an architecture for generic process design for this company, in order to evaluate quickly other scenarios. From this, we will be able to develop an approach of proactively using scheduling information in a systematic way to positively influence design decisions.