Solving the Resource Constrained Project Scheduling Problem with Generalized Precedences by Lazy Clause Generation

The technical report presents a generic exact solution approach for minimizing the project duration of the resource-constrained project scheduling problem with generalized precedences (Rcpsp/max). The approach uses lazy clause generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, in order to apply nogood learning and conflict-driven search on the solution generation. Our experiments show the benefit of lazy clause generation for finding an optimal solutions and proving its optimality in comparison to other state-of-the-art exact and non-exact methods. The method is highly robust: it matched or bettered the best known results on all of the 2340 instances we examined except 3, according to the currently available data on the PSPLib. Of the 631 open instances in this set it closed 573 and improved the bounds of 51 of the remaining 58 instances.Comment: 37 pages, 3 figures, 16 table

Global Difference Constraint Propagation for Finite Domain Solvers

Difference constraints of the form x − y ≤ d are well studied, with efficient algorithms for satisfaction and implication, because of their connection to shortest paths. Finite domain propagation algorithms however do not make use of these algorithms, and typically treat each difference constraint as a separate propagator. Propagation does guarantee completeness of solving but can be needlessly slow. In this paper we describe how to build a (bounds consistent) global propagator for difference constraints that treats them all simultaneously. SAT modulo theory solvers have included theory solvers for difference constraints for some time. While a theory solver for difference constraints gives the basis of a global difference constraint propagator, we show how the requirements on the propagator are quite different. We give experiments showing that treating difference constraints globally can substantially improve on the standard propagation approach

Mixed conflict model for Air Traffic Control

ce congestion is today the most critical issue European Air Traffic Management (ATM) has to face. Current real-time Air Traffic Control (ATC) is achieved by human controllers. One of their main tasks is to keep separation between aircraft, asking to the pilots to do basic avoidance manoeuvres. We propose here two mixed CSP models of this separation issue, combining discrete and continuous variables. An implementation of these models allows to produce optimal solutions for problems where numerous aircraft are conflicting