Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the public instances available. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

Educational timetabling: Problems, benchmarks, and state-of-the-art results

We propose a survey of the research contributions on the field of Educational Timetabling with a specific focus on “standard” formulations and the corresponding benchmark instances. We identify six of such formulations and we discuss their features, pointing out their relevance and usability. Other available formulations and datasets are also reviewed and briefly discussed. Subsequently, we report the main state-of-the-art results on the selected benchmarks, in terms of solution quality (upper and lower bounds), search techniques, running times, and other side settings

An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data

In this work we analyse basketball play-by-play data in order to evaluate the efficiency of different fiveman lineups employed by teams. Starting from the adjusted plus-minus framework, we present a modelbased strategy for the analysis of the result of partial match outcomes, extending the current literature in two main directions. The first extension replaces the classical response variable (scored points) with a comprehensive score that combines a set of box score statistics. This allows various aspects of the game to be separated. The second extension focuses on entire lineups rather than individual players, using a suitable extended model specification. The model fitting procedure is Bayesian and provides the necessary regularization. An advantage of this approach is the use of posterior distributions to rank players and lineups, providing an effective tool for team managers. For the empirical analysis, we use the 2018/2019 regular season of the Turkish Airlines Euroleague Championship, with play-by-play and box scores for 240 matches, which are made available by the league website. The results of the model fitting can be used for several investigations as, for instance, the comparative analysis of the effects of single players and the estimation of total and synergic effects of lineups monitoring. Moreover, the behaviour of players and lineups during the season, updating the estimation results after each gameday, can represent a rather useful tool

An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data

In this work we analyse basketball play-by-play data in order to evaluate the efficiency of different fiveman lineups employed by teams. Starting from the adjusted plus-minus framework, we present a modelbased strategy for the analysis of the result of partial match outcomes, extending the current literature in two main directions. The first extension replaces the classical response variable (scored points) with a comprehensive score that combines a set of box score statistics. This allows various aspects of the game to be separated. The second extension focuses on entire lineups rather than individual players, using a suitable extended model specification. The model fitting procedure is Bayesian and provides the necessary regularization. An advantage of this approach is the use of posterior distributions to rank players and lineups, providing an effective tool for team managers. For the empirical analysis, we use the 2018/2019 regular season of the Turkish Airlines Euroleague Championship, with play-by-play and box scores for 240 matches, which are made available by the league website. The results of the model fitting can be used for several investigations as, for instance, the comparative analysis of the effects of single players and the estimation of total and synergic effects of lineups monitoring. Moreover, the behaviour of players and lineups during the season, updating the estimation results after each gameday, can represent a rather useful tool

Context Aware Browser

I will present the Context Aware Browser, a novel paradigm for context-aware access to Web contents with mobile devices. The idea is to allow automatic download of Web pages, and even automatic execution of Web applications, on user\u27s own mobile device. The Web resources are not simply pushed on the mobile device; rather, they are selected on the basis of the context the user is in: context data (mainly location, but not only) are used to build a query sent to an external search engine, that selects the most relevant Web content. I will describe the idea, provide some examples, show a video of a recently built prototype, present implementation issues, discuss our specific evaluation methodology and the results, and sketch future work and problems. This is an ongoing project, started about five years ago; it is joint work with the Context Aware and Mobile Systems laboratory (smdc.uniud.it) and the MoBe spinoff (www.mobe.it) at Udine University