A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems

Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.

SAT-Based Approaches for the General High School Timetabling Problem

High School Timetabling (HSTT) is a well known and widespread problem. It consists of coordinating resources (e.g. teachers, rooms), times, and events (e.g. lectures) with respect to various constraints. In this paper, I summarize the work I have done towards exploring the relationship between propositional logic and HSTT. This includes various modeling techniques in the form of maxSAT and bitvectors, data structures for local search algorithms, and the combination of maxSAT and metaheuristic algorithms. In addition, I discuss possible directions for future work as a part of a long-term research goal to combine complete and metaheuristic algorithms.