Wang S.,Singapore Management University |
Bussieck M.,GAMS Development Corporation |
Guignard M.,University of Pennsylvania |
Meeraus A.,GAMS Development Corporation
Journal of Scheduling | Year: 2010
Scheduling term-end exams (TEE) at the United States Military Academy in West Point is unlike any other exam timetabling problem we know of. Exam timetabling normally produces a conflict-free timetable covering a reasonably long exam period, where every exam is scheduled exactly once for all the students enrolled in the corresponding class. The situation is quite different at West Point. There are hundreds of exams to schedule over such a short time period that there is simply no feasible solution. The challenge is then to allow something that is not even considered elsewhere, that is, creating multiple sessions of some exams, scheduled at different times within the exam period, to allow each student to take all exams he/she must take. The overall objective is to find a feasible exam schedule with a minimum number of such duplicate exams. The paper describes a system that has been developed at GAMS Development Corp. in close cooperation with the scheduling staff at West Point, and that has been used successfully since 2001. It uses mathematical optimization in several modules, and some of the techniques proposed are new. It is fast and flexible, and allows for human interaction, such as adding initially unexpected constraints, coming for instance from instructors' preferences and dislikes, as well as their hierarchical rankings. It is robust and can be used by people familiar with the organization at West Point, without the need for them to be technically-trained. Overall, using the course and student information databases, it is an effective decision support system that calls optimization tools in an unobtrusive way. © 2009 Springer Science+Business Media, LLC.
Bussieck M.R.,GAMS Development Corporation |
Dirkse S.P.,GAMS Development Corporation |
Vigerske S.,GAMS Development Corporation
Journal of Global Optimization | Year: 2014
In this paper we describe PAVER 2.0, an environment (i.e. a process and a suite of tools supporting that process) for the automated performance analysis of benchmarking data. This new environment improves on its predecessor by addressing some of the shortcomings of the original PAVER (Bussieck et al. in Global optimization and constraint satisfaction, lecture notes in computer science, vol 2861, pp 223-238. Springer, Berlin, 2003. doi:10. 1007/978-3-540-39901-8-17) and extending its capabilities. The changes serve to further the original goals of PAVER (automation of the visualization and summarization of benchmarking data) while making the environment more accessible for the use of and modification by the entire community of potential users. In particular, we have targeted the end-users of optimization software, as they are best able to make the many subjective choices necessary to produce impactful results when benchmarking optimization software. We illustrate with some sample analyses conducted via PAVER 2.0. © Springer Science+Business Media New York 2013.