This paper addresses the weighted vertex coloring problem (WVCP) which is an NP-hard variant of the graph coloring problem with various applications. Given a vertex-weighted graph, the problem consists of partitioning vertices in independent sets (colors) so as to minimize the sum of the maximum weights of the colors. We first present an iterative procedure to reduce the size of WVCP instances and prove new upper bounds on the objective value and the number of colors. Alternative constraint programming models are then introduced which rely on primal and dual encodings of the problem and use symmetry breaking constraints. A large number of experiments are conducted on benchmark instances. We analyze the impact of using specific bounds to reduce the search space and speed up the exact resolution of instances. New optimality proofs are reported for some benchmark instances.

Distribution planning in closed-loop supply chains is concerned with determining transfer and repair operations based on demand forecasts and subject to backordering, inventory, transfer and repair constraints. We present a mixed-integer programming model and a dedicated metaheuristics for this problem and show it is is NP-hard. The model is applicable to a wide range of closed-loop supply chains with different network topologies and site functions and it can also support different planning strategies by means of a weighted objective function. Comparative experiments on pseudo-random instances built on a case study in telecommunication service operations demonstrate the effectiveness and scalability of the metaheuristics. Lastly, we discuss possible extensions to address common supply chain requirements, including the ability to produce robust plans in uncertain environments.

This paper addresses the discovery of discriminative nary motifs in databases of labeled sequences. We consider databases made up of positive and negative sequences and define a motif as a set of patterns embedded in all positive sequences and subject to alignment constraints. We formulate constraints to eliminate redundant motifs and present a general constraint optimization framework to compute motifs that are exclusive to the positive sequences. We cast the discovery of closed and replication-free motifs in this framework and propose a two-stage approach whose last stage reduces to a minimum set covering problem. Experiments on protein sequence datasets demonstrate its efficiency.

Considerable effort has been invested over the years in ad-hoc algorithms for item set and pattern mining. Constraint programming has recently been proposed as a means to tackle item set mining tasks within a general modelling framework. We follow this approach to address the discovery of discriminative n-ary motifs in databases of labeled sequences. We define a n-ary motif as a mapping of n patterns to n class-wide embeddings and we restrict the interpretation of constraints on a motif to the sequences embedding all patterns. We formulate core constraints that minimize redundancy between motifs and introduce a general constraint optimization framework to compute common and exclusive motifs. We cast the discovery of closed and replication-free motifs in this framework for which we propose a two-stage approach based on constraint programming. Experimental results on datasets of protein sequences demonstrate the efficiency of the approach.

A soft global constraint SOFTPREC has been proposed recently for solving optimisation problems involving precedence relations. In this paper we present new pruning rules for this global constraint. We introduce a pruning rule that improves propagation from the objective variable to the decision variables, which is believed to be harder to achieve. We further introduce a pruning rule based on linear programming, and thereby make SOFTPREC a hybrid of constraint programming and linear programming. We present results demonstrating the efficiency of the pruning rules.

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation, which is a generalisation of the feedback vertex set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial weighted maximum Boolean satisfiability and mixed integer linear programming. We study all these formulations by experimentally comparing them on a variety of randomly generated instances of the feature subscription problem.

Modern feature-rich telecommunications services offer significant opportunities to human users. To make these services more usable, facilitating personalisation is very important since it enhances the users' experience considerably. However, regardless how service providers organise their catalogues of features, they cannot achieve complete configurability due to the existence of feature interactions. Distributed Feature Composition (DFC) provides a comprehensive methodology, underpinned by a formal architecture model to address this issue. In this paper we present an approach based on using Binary Decision Diagrams (BDD) to find optimal reconfigurations of features when a user's preferences violate the technical constraints defined by a set of DFC rules. In particular, we propose hybridizing constraint programming and standard BDD compilation techniques in order to scale the construction of a BDD for larger size catalogues. Our approach outperforms the standard BDD techniques by reducing the memory requirements by as much as five orders-of-magnitude and compiles the catalogues for which the standard techniques ran out of memory.

Telecommunication services are playing an increasing and potentially disruptive role in our lives. As a result, service providers seek to develop personalisation solutions that put customers in charge of controlling and enriching their services. In this context, the personalisation approach consists of exposing a catalogue of call control features (e.g., call-divert, voice-mail) to end-users and letting them subscribe to a subset of features subject to a set of precedence and exclusion constraints. When a subscription is inconsistent, the problem is to find an optimal relaxation. We present a constraint programming formulation to find an optimal reconfiguration of features. We investigate the performance of maintaining arc consistency within branch and bound search. We also study the impact of maintaining mixed consistency, that is maintaining different levels of consistency on different sets of variables. We further present a global constraint and a set of filtering rules that exploit the structure of our problem. We theoretically and experimentally compare all approaches. Our results demonstrate that the filtering rules of the global constraint outperform all other approaches when a catalogue is dense, and mixed consistency pays off when a catalogue is sparse.

Component-based product-line architectures (PLAs) must support two operations: application configuration - the construction of valid application specifications - and application generation - the compilation of specifications into executable applications. Whereas configuration is a combinatorial task involving advanced knowledge-based reasoning, generation is a deterministic compilation process. This suggests an application synthesis model where configuration and generation are carried out separately by interoperable tools. To this end, we introduce a PLA development toolkit which includes a constraint-based configuration language and an aspect-based generation language supporting the same architecture model. The toolkit imposes dual PLA implementations consisting of a configuration program and a generation program. The compilation of the configuration program yields an interactive configurator used to produce valid configurations at run-time. Valid configurations are then compiled by the generator with the generation program to produce Java applications. Overall, this model allows the use of powerful configuration and generation technologies - namely, constraint programming and aspect-oriented programming - while enforcing view consistency and tool interoperability.