Claude-Guy Quimper

Decomposing global grammar constraints

A wide range of constraints can be specified using automata or formal languages. The GRAMMAR constraint restricts the values taken by a sequence of variables to be a string from a given context-free language. Based on an AND/OR decomposition, we show that this constraint can be converted into clauses in conjunctive normal form without hindering propagation. Using this decomposition, we can propagate the GRAMMAR constraint in O(n3) time. The decomposition also provides an efficient incremental propagator. Down a branch of the search tree of length k, we can enforce GAC k times in the same O(n3) time. On specialized languages, running time can be even better. For example, propagation of the decomposition requires just O(n|δ|) time for regular languages where |δ| is the size of the transition table of the automaton recognizing the regular language. Experiments on a shift scheduling problem with a constraint solver and a state of the art SAT solver show that we can solve problems using this decomposition that defeat existing constraint solvers.

Formal languages for integer programming modeling of shift scheduling problems

This paper approaches the problem of modeling optimization problems containing substructures involving constraints on sequences of decision variables. Such constraints can be very complex to express with Mixed Integer Programming (MIP). We suggest an approach inspired by global constraints used in Constraint Programming (CP) to exploit formal languages for the modeling of such substructures with MIP. More precisely, we first suggest a way to use automata, as the CP regular constraint does, to express allowed patterns for the values taken by the constrained sequence of variables. Secondly, we present how context-free grammars can contribute to formulate constraints on sequences of variables in a MIP model. Experimental results on both approaches show that they facilitate the modeling, but also give models easier to solve by MIP solvers compared to compact assignment MIP formulations.

Encodings of the SEQUENCE constraint

The SEQUENCE constraint is useful in modelling car sequencing, rostering, scheduling and related problems. We introduce half a dozen new encodings of the SEQUENCE constraint, some of which do not hinder propagation. We prove that, down a branch of a search tree, domain consistency can be enforced on the SEQUENCE constraint in just O(n2 log n) time. This improves upon the previous bound of O(n3) for each call down the tree. We also consider a generalization of the SEQUENCE constraint - the Multiple SEQUENCE constraint. Our experiments suggest that, on very large and tight problems, domain consistency algorithms are best. However, on smaller or looser problems, much simpler encodings are better, even though these encodings hinder propagation. When there are multiple SEQUENCE constraints, a more expensive propagator shows promise.

A large neighbourhood search approach to the multi-activity shift scheduling problem

The challenge in shift scheduling lies in the construction of a set of work shifts, which are subject to specific regulations, in order to cover fluctuating staff demands. This problem becomes harder when multi-skill employees can perform many different activities during the same shift. In this paper, we show how formal languages (such as regular and context-free languages) can be enhanced and used to model the complex regulations of the shift construction problem. From these languages we can derive specialized graph structures that can be searched efficiently. The overall shift scheduling problem can then be solved using a Large Neighbourhood Search. These approaches are able to return near optimal solution on traditional single activity problems and they scale well on large instances containing up to 10 activities.

An Efficient Bounds Consistency Algorithm for the Global Cardinality Constraint

Previous studies have demonstrated that designing special purpose constraint propagators can significantly improve the efficiency of a constraint programming approach. In this paper we present an efficient algorithm for bounds consistency propagation of the generalized cardinality constraint (gcc). Using a variety of benchmark and random problems, we show that on some problems our bounds consistency algorithm can dramatically outperform existing state-of-the-art commercial implementations of constraint propagators for the gcc. We also present a new algorithm for domain consistency propagation of the gcc which improves on the worst-case performance of the best previous algorithm for problems that occur often in applications.

Flow-Based Propagators for the SEQUENCE and Related Global Constraints

We propose new filtering algorithms for the Sequence constraint and some extensions of the Sequence constraint based on network flows. We enforce domain consistency on the Sequence constraint in O(n2) time down a branch of the search tree. This improves upon the best existing domain consistency algorithm by a factor of O(logn). The flows used in these algorithms are derived from a linear program. Some of them differ from the flows used to propagate global constraints like Gcc since the domains of the variables are encoded as costs on the edges rather than capacities. Such flows are efficient for maintaining bounds consistency over large domains and may be useful for other global constraints.

Counting-based search: branching heuristics for constraint satisfaction problems

Designing a search heuristic for constraint programming that is reliable across problem domains has been an important research topic in recent years. This paper concentrates on one family of candidates: counting-based search. Such heuristics seek to make branching decisions that preserve most of the solutions by determining what proportion of solutions to each individual constraint agree with that decision. Whereas most generic search heuristics in constraint programming rely on local information at the level of the individual variable, our search heuristics are based on more global information at the constraint level. We design several algorithms that are used to count the number of solutions to specific families of constraints and propose some search heuristics exploiting such information. The experimental part of the paper considers eight problem domains ranging from well-established benchmark puzzles to rostering and sport scheduling. An initial empirical analysis identifies heuristic maxSD as a robust candidate among our proposals. We then evaluate the latter against the state of the art, including the latest generic search heuristics, restarts, and discrepancy-based tree traversals. Experimental results show that counting-based search generally outperforms other generic heuristics.

Parallel discrepancy-based search

Backtracking strategies based on the computation of discrepancies have proved themselves successful at solving large problems. They show really good performance when provided with a high-quality domain-specific branching heuristic variable and value ordering heuristic, which is the case for many industrial problems. We propose a novel approach PDS that allows parallelizing a strategy based on the computation of discrepancies LDS. The pool of processors visits the leaves in exactly the same order as the centralized algorithm would do. The implementation allows for a natural/intrinsic load balancing to occur filtering induced by constraint propagation would affect each processor pretty much in the same way, although there is no communication between processors. These properties make PDS a scalable algorithm that was used on a massively parallel supercomputer with thousands of cores. PDS improved the best known performance on an industrial problem.

Reformulating global constraints: the slide and regular constraints

Global constraints are useful for modelling and reasoning about real-world combinatorial problems. Unfortunately, developing propagation algorithms to reason about global constraints efficiently and effectively is usually a difficult and complex process. In this paper, we show that reformulation may be helpful in building such propagators. We consider both hard and soft forms of two powerful global constraints, Slide and Regular. These global constraints are useful to represent a wide range of problems like rostering and scheduling where we have a sequence of decision variables and some constraint that holds along the sequence. We show that the different forms of Slide and Regular can all be reformulated as each other. We also show that reformulation is an effective method to incorporate such global constraints within an existing constraint toolkit. Finally, this study provides insight into the close relationship between these two important global constraints.

Decomposition of the NVALUE constraint

We study decompositions of the global NVALUE constraint. Our main contribution is theoretical: we show that there are propagators for global constraints like NVALUE which decomposition can simulate with the same time complexity but with a much greater space complexity. This suggests that the benefit of a global propagator may often not be in saving time but in saving space. Our other theoretical contribution is to show for the first time that range consistency can be enforced on NVALUE with the same worst-case time complexity as bound consistency. Finally, the decompositions we study are readily encoded as linear inequalities. We are therefore able to use them in integer linear programs.