Barry O’Sullivan

Local search and constraint programming for the post enrolment-based course timetabling problem

We present a variety of approaches for solving the post enrolment-based course timetabling problem, which was proposed as Track 2 of the 2007 International Timetabling Competition. We approach the problem using local search and constraint programming techniques. We show how to take advantage of a list-colouring relaxation of the problem. Our local search approach won Track 2 of the 2007 competition. Our best constraint programming approach uses an original problem decomposition. Incorporating this into a large neighbourhood search scheme seems promising, and provides motivation for studying complete approaches in further detail.

Proteus: A Hierarchical Portfolio of Solvers and Transformations

In recent years, portfolio approaches to solving SAT problems and CSPs have become increasingly common. There are also a number of different encodings for representing CSPs as SAT instances. In this paper, we leverage advances in both SAT and CSP solving to present a novel hierarchical portfolio-based approach to CSP solving, which we call Proteus, that does not rely purely on CSP solvers. Instead, it may decide that it is best to encode a CSP problem instance into SAT, selecting an appropriate encoding and a corresponding SAT solver. Our experimental evaluation used an instance of Proteus that involved four CSP solvers, three SAT encodings, and six SAT solvers, evaluated on the most challenging problem instances from the CSP solver competitions, involving global and intensional constraints. We show that significant performance improvements can be achieved by Proteus obtained by exploiting alternative view-points and solvers for combinatorial problem-solving.

Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem

Data centers are a critical and ubiquitous resource for providing infrastructure for banking, Internet and electronic commerce. One way of managing data centers efficiently is to minimize a cost function that takes into account the load of the machines, the balance among a set of available resources of the machines, and the costs of moving processes while respecting a set of constraints. This problem is called the machine reassignment problem. An instance of this online problem can have several tens of thousands of processes. Therefore, the challenge is to solve a very large sized instance in a very limited time. In this paper, we describe a constraint programming-based Large Neighborhood Search (LNS) approach for solving this problem. The values of the parameters of the LNS can have a significant impact on the performance of LNS when solving an instance. We, therefore, employ the Instance Specific Algorithm Configuration (ISAC) methodology, where a clustering of the instances is maintained in an offline phase and the parameters of the LNS are automatically tuned for each cluster. When a new instance arrives, the values of the parameters of the closest cluster are used for solving the instance in the online phase. Results confirm that our CP-based LNS approach, with high quality parameter settings, finds good quality solutions for very large sized instances in very limited time. Our results also significantly outperform the hand-tuned settings of the parameters selected by a human expert which were used in the runner-up entry in the 2012 EURO/ROADEF Challenge.

Multilevel Security and Quality of Protection

Constraining how information may flow within a system is at the heart of many protection mechanisms and many security policies have direct interpretations in terms of information flow and multilevel security style controls. However, while conceptually simple, multilevel security controls have been difficult to achieve in practice. In this paper we explore how the traditional assurance measures that are used in the network multilevel security model can be re-interpreted and generalised to provide the basis of a framework for reasoning about the quality of protection provided by a secure system configuration.

Constraint Acquisition as Semi-Automatic Modeling

Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable solutions or non-desirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem. This paper provides a theoretical framework for a research agenda in the area of interactive constraint acquisition, automated modelling and automated constraint programming.

A Constraint-Based Local Search for Edge Disjoint Rooted Distance-Constrained Minimum Spanning Tree Problem

Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be modelled as Rooted Distance-Constrained Minimum Spanning-Tree Problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths. We call this problem the Edge-disjoint RDCMST (ERDCMST). Previous works on RDCMST have focused on dedicated algorithms which are hard to extend with side constraints, and therefore these algorithms cannot be extended for solving ERDCMST. We present a constraint-based local search algorithm for which we present two efficient local move operators and an incremental way of maintaining objective function. Our local search algorithm can easily be extended and it is able to solve both problems. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, the UK, and Italy. We compare our approach with existing exact and heuristic approaches. Results show that our approach is superior to both of the latter in terms of scalability and its anytime behaviour.

Grand challenges for constraint programming

Every field should have its Grand Challenges. After discussing some general “why and how” issues, with brief reference to some sample challenges, we devote attention to the challenges raised by the new world of “BigData” and to some new ways of approaching the classic Grand Challenge of the Holy Grail (where one merely states the problem and the computer solves it). There can, of course, never be a definitive catalogue of Grand Challenges. The ultimate Grand Challenge is for everyone working on Constraint Programming to look up on occasion from their everyday pursuits to consider how they might contribute to a Grand Challenge, and even to try their hand at formulating their own Grand Challenges.

Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem

The satisfiability problem was amongst the very first problems proven to be NP-Complete. It arises in many real world domains such as hardware verification, planning, scheduling, configuration and telecommunications. Recently, there has been growing interest in using portfolios of solvers for this problem. In this paper we present a case-based reasoning approach to SAT solving. A key challenge is the adaptation phase, which we focus on in some depth. We present a variety of adaptation approaches, some heuristic, and one that computes an optimal Kemeny ranking over solvers in our portfolio. Our evaluation over three large case bases of problem instances from artificial, hand-crafted and industrial domains, shows the power of a CBR approach, and the importance of the adaptation schemes used.

A Study of Query Generation Strategies for Interactive Constraint Acquisition

In many practical applications users find it difficult to articulate their constraints. While users can recognize examples of where a constraint should be satisfied or violated, they cannot articulate the constraint itself. For example, a customer may be trying to specify a constraint to an engineer, without being able to use the correct engineering terms for the relevant concepts.

A Theoretical Framework for Tradeoff Generation using Soft Constraints

Tradeoffs have been proposed in the literature as an approach to resolving over-constrainedness in interactive constraint-based tools, such as product configurators, that reason about user preferences . It has been reported how tradeoffs can be modeled as additional constraints. This paper presents a formal framework for tradeoff generation based on the semiring approach to soft constraints. In particular, user preferences and tradeoffs are represented as soft constraints and as an entailment operator, respectively. The entailment operator is used to interactively generate new constraints representing tradeoffs. We also introduce a novel definit ion of substitutability for soft constraints upon which we present a relaxed definition of tradeoffs.