Innovative Applications of Constraint Programming

Constraint programming (CP) is a declarative paradigm that enables us to model a problem in the form of constraints to be satisfied. It offers powerful constraint solvers which, by implementing general-purpose search techniques, are fast and robust to address complex constraint models automatically. Constraint programming has attracted the attention of people from various domains. By separating the definition of a problem from its solution, it is more natural for people to implement the program directly from the problem specification, reducing the cost of development and future maintenance significantly. Furthermore, CP provides the flexibility of choosing a suitable solver for a problem of a given nature, which overcomes the limitations of a unique solver. Thanks to this, CP has allowed many non-domain experts to solve emerging problems efficiently. This thesis studies the innovative applications of CP by examining two topics: constraint modeling for several novel problems, and automatic solver selection. For the modeling, we explored two case studies, namely the (sub)group activity optimization problem, and the service function chaining deployment problem that comes from the Software Defined Network (SDN) domain. Concerning the solver selection, we improved an algorithm selection technique called “SUNNY”, which generates a schedule of solvers for a given problem instance. In this work, we demonstrate with empirical experiments that the procedure we have designed to configure SUNNY parameters is effective, and it makes SUNNY scalable to an even broader range of algorithm selection problems not restricted to CP

Networked by design: can policy constraints support the development of capabilities for collaborative innovation?

While there has been some recent interest in the behavioural effects of policies in support of innovation networks, this research field is still relatively new. In particular, an important but under-researched question for policy design is “what kind of networks” should be supported, if the objective of the policy is not just to fund successful innovation projects, but also to stimulate behavioural changes in the participants, such as increasing their ability to engage in collaborative innovation. By studying the case of the innovation policy programmes implemented by the regional government of Tuscany, in Italy, between 2002 and 2008, we assess whether the imposition of constraints on the design of innovation networks has enhanced the participants’ collaborative innovation capabilities, and we draw some general implications for policy

Software tools for stochastic programming: A Stochastic Programming Integrated Environment (SPInE)

SP models combine the paradigm of dynamic linear programming with modelling of random parameters, providing optimal decisions which hedge against future uncertainties. Advances in hardware as well as software techniques and solution methods have made SP a viable optimisation tool. We identify a growing need for modelling systems which support the creation and investigation of SP problems. Our SPInE system integrates a number of components which include a flexible modelling tool (based on stochastic extensions of the algebraic modelling languages AMPL and MPL), stochastic solvers, as well as special purpose scenario generators and database tools. We introduce an asset/liability management model and illustrate how SPInE can be used to create and process this model as a multistage SP application

Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms

In this paper, we present two alternative approaches to defining answer sets for logic programs with arbitrary types of abstract constraint atoms (c-atoms). These approaches generalize the fixpoint-based and the level mapping based answer set semantics of normal logic programs to the case of logic programs with arbitrary types of c-atoms. The results are four different answer set definitions which are equivalent when applied to normal logic programs. The standard fixpoint-based semantics of logic programs is generalized in two directions, called answer set by reduct and answer set by complement. These definitions, which differ from each other in the treatment of negation-as-failure (naf) atoms, make use of an immediate consequence operator to perform answer set checking, whose definition relies on the notion of conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other two definitions, called strongly and weakly well-supported models, are generalizations of the notion of well-supported models of normal logic programs to the case of programs with c-atoms. As for the case of fixpoint-based semantics, the difference between these two definitions is rooted in the treatment of naf atoms. We prove that answer sets by reduct (resp. by complement) are equivalent to weakly (resp. strongly) well-supported models of a program, thus generalizing the theorem on the correspondence between stable models and well-supported models of a normal logic program to the class of programs with c-atoms. We show that the newly defined semantics coincide with previously introduced semantics for logic programs with monotone c-atoms, and they extend the original answer set semantics of normal logic programs. We also study some properties of answer sets of programs with c-atoms, and relate our definitions to several semantics for logic programs with aggregates presented in the literature

Innovative systems for the transportation disadvantaged: towards more efficient and operationally usable planning tools

When considering innovative forms of public transport for specific groups, such as demand responsive services, the challenge is to find a good balance between operational efficiency and 'user friendliness' of the scheduling algorithm even when specialized skills are not available. Regret insertion-based processes have shown their effectiveness in addressing this specific concern. We introduce a new class of hybrid regret measures to understand better why the behaviour of this kind of heuristic is superior to that of other insertion rules. Our analyses show the importance of keeping a good balance between short- and long-term strategies during the solution process. We also use this methodology to investigate the relationship between the number of vehicles needed and total distance covered - the key point of any cost analysis striving for greater efficiency. Against expectations, in most cases decreasing fleet size leads to savings in vehicle mileage, since the heuristic solution is still far from optimality

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding 'optimal' and 'feasible' operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually 'non-Gaussian' distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power