Iterated local search using an add and delete hyper- heuristic for university course timetabling

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach

A New Boolean Encoding for MAPF and its Performance with ASP and MaxSAT Solvers

Multi-agent pathfinding (MAPF) is an NP-hard problem. As such, dense maps may be very hard to solve optimally. In such scenarios, compilation-based approaches, via Boolean satisfiability (SAT) and answer set programming (ASP), have proven to be most effective. In this paper, we propose a new encoding for MAPF, which we implement and solve using both ASP and MaxSAT solvers. Our encoding builds on a recent ASP encoding for MAPF but changes the way agent moves are encoded. This allows to represent swap and follow conflicts with binary clauses, which are known to work well along with conflict-based clause learning. For MaxSAT, we study different ways in which we may combine the MSU3 and LSU algorithms for maximum performance. Our results, over grid and warehouse maps, show that the ASP solver scales better when the number of agents is increased on grids with few obstacles, while the MaxSAT solver performs better in scenarios with more obstacles and fewer agents

Personalized course schedule planning using answer set programming

Course scheduling or timetabling is a well-known problem that is generally studied from the perspective of schools; the goal is to schedule the courses, considering, e.g., the expected number of students, the sizes of the available classrooms, time conflicts between courses of the same category. We study a complementary problem to help the students during the course registration periods; the goal is to plan personalized course schedules for students, considering, e.g., their preferences over sections, instructors, distribution of the courses. We present a declarative method to compute personalized course schedules, and an application of this method using answer set programming, and discuss promising results of some preliminary user evaluations via surveys

Core-Boosted Linear Search for Incomplete MaxSAT

Maximum Satisfiability (MaxSAT), the optimisation extension of the well-known Boolean Satisfiability (SAT) problem, is a competitive approach for solving NP-hard problems encountered in various artificial intelligence and industrial domains. Due to its computational complexity, there is an inherent tradeoff between scalability and guarantee on solution quality in MaxSAT solving. Limitations on available computational resources in many practical applications motivate the development of complete any-time MaxSAT solvers, i.e. algorithms that compute optimal solutions while providing intermediate results. In this work, we propose core-boosted linear search, a generic search-strategy that combines two central approaches in modern MaxSAT solving, namely linear and core-guided algorithms. Our experimental evaluation on a prototype combining reimplementations of two state-of-the-art MaxSAT solvers, PMRES as the core-guided approach and LinSBPS as the linear algorithm, demonstrates that our core-boosted linear algorithm often outperforms its individual components and shows competitive and, on many domains, superior results when compared to other state-of-the-art solvers for incomplete MaxSAT solving.Peer reviewe