A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. © 2014 Springer Science+Business Media New York

A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Personalized learning is emerging in schools as an alternative to one-size-fits-all education. This study introduces and explores a weekly demand-driven flexible learning activity planning problem of own-pace own-method personalized learning. The introduced problem is a computationally intractable optimization problem involving many decision dimensions and also many soft constraints. We propose batch and decomposition methods to generate good-quality initial solutions and a dynamic Thompson sampling based hyper-heuristic framework, as a local search mechanism, which explores the large solution space of this problem in an integrative way. The characteristics of our test instances comply with average secondary schools in the Netherlands and are based on expert opinions and surveys. The experiments, which benchmark the proposed heuristics against Gurobi MIP solver on small instances, illustrate the computational challenge of this problem numerically. According to our experiments, the batch method seems quicker and also can provide better quality solutions for the instances in which resource levels are not scarce, while the decomposition method seems more suitable in resource scarcity situations. The dynamic Thompson sampling based online learning heuristic selection mechanism is shown to provide significant value to the performance of our hyper-heuristic local search. We also provide some practical insights; our experiments numerically demonstrate the alleviating effects of large school sizes on the challenge of satisfying high-spread learning demands

Applying human-like intelligence to future generation network to improve communication efficiency

Includes abstract.Includes bibliographical references (leaves 251-257).In recent decades, communications network has evolved at drastic speed to provide advanced and intelligent services. This strengthening service provision owes to the successful establishment of various intelligent networks and the use of artificial intelligence, pervasive computing, and social networking in communications. It has consequently endowed network users with abundant choices of communication services. While these communications services are bringing convenience to human lives, people in turn are performing more tasks. The current network with its large number of available communications services is then often burdening network users with the complexity and inflexibility in using these services. In particular, the network lacks the initiative and the ability to investigate a user’s most recent communication needs and subsequently adjust the manner of service provision according to these needs and user connecting possibilities. The network needs to be more intelligent to handle these problems. We therefore propose importing human-like intelligence into the network to facilitate communication-session processing according to user needs

Enterprise Social Networks: The Case of CERN

Social networks are commonly seen as a global trend that allows users to search and contact others with similar interests, write a post, reply, like or share content, create groups and organize events. This said, there is much more that can be done to exploit the full potential of social media. In order to improve the business, providing employees, customers and partners the best tools to cooperate and gain value from the whole community, many organizations are taking the matter in their own hands, using Enterprise Social Networks. Close analysis of case studies and comprehensive statistics shows why it is important to pursue this path. At CERN, the European Organization for Nuclear Research, where the number of employees, students and volunteers that everyday work in partnership both on site and through the network reaches the thousands, a new kind of platform has been deployed, able to exploit the social knowledge of the personnel. The thesis will describe the case study of CERN to understand not only why it is essential to become a social organization but also how a social environment can be developed. The last chapters will focus on examining my work on the platform, considering a mobile responsive design, realized to make the environment adapt to any screen size, an integrated resource planning tool, which gives the scientists the mean to easily manage the everyday work on the particle accelerators, and the platform’s Application Programming Interface, which allows anyone with the right credentials to include content from the enterprise social network into a personal or departmental webpage, giving everyone an even easier way to participate