Dynamic scheduling using real-time social data

The goal of this thesis is to develop an Android application that allows dynamic scheduling of a number of activities, based on changing information that is received from a group of contacts. The idea is that multiple users can input a number of (possibly group) activities that need to be scheduled, together with a list of constraints. The application then creates an individual schedule for each user, which can be updated if new data about the activities becomes available (e.g. delays).

Consider the following motivating scenario: A large group of friends visits an amusement park. Each person in the group has a set of rides he wants to go on. To start the schedule, each person enters the rides he wants to visit, possibly together with a number of constraints (wants to be together with certain persons, prefer some rides over others, minimize walking distance). Based on this data the application creates an initial schedule, listing for each person the order in which to visit the rides. The friends can then split up and follow their initial schedules. During the execution of the schedules, however, each friend gathers new data about the time it takes to go on the ride (which depends on the current queues at the ride). This data can be shared with friends on other rides through the application. Based on this newer information, the application can then update their schedules. For example: rides which currently have no lines can be moved forward in the schedule, rides with extremely long lines can be postponed (or possible even dropped completely). Of course during this rescheduling, constraints between persons should still be taken into account.

The thesis can be divided into 2 main requirements

• development of the Android application
• Investigation of algorithms to perform the scheduling and possibly perform data prediction

An interest in machine learning and distributed computing are a must.

some related works:

• Towards a Hardware Implementation of Reinforcement Learning for Call Admission Control in Networks for Integrated Services
• Item-based Collaborative Filtering Recommendation Algorithms
• Scheduling: Scheduling Theory, Algorithms, and Systems by M. Pinedo (2008)

• Ann Nowe (promotor): ann.nowe@vub.ac.be
• Peter Vrancx: pvrancx@vub.ac.be