Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Constraint Model of Space Planning
Constraint Model of Space Planning
Artificial Intelligence
Three Methods to Automate the Space Allocation Process in UK Universities
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A multi-level genetic algorithm for a multi-stage space allocation problem
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.00 |
The allocation of office space in any large institution is usually a problematical issue, which often demands a substantial amount of time to perform manually. The result of this allocation affects the lives of whoever makes use of the space. In the higher education sector in the UK, space is becoming an increasingly precious commodity. Student numbers have risen significantly over the last few years and as a result, university departments have grown in size. In addition, universities have come under increasing financial pressure to ensure that space is utilized as efficiently and effectively as possible. However, space utilization is only one issue to take into account when measuring whether or not a particular allocation is of a sufficient high quality. The problem of space allocation is further complicated by the fact that no standard procedure is practiced throughout the higher education sector. Most institutions have their own standards and requirements, which are often very different to other institutions. Different levels of authority control the domains of rooms and resources in different institutions. The most common situation is where a central university office controls a number of faculties, each managing a number of departments. This paper will focus specifically on applying optimization methods to departmental room allocation for non-residential space in the higher education sector. It will look at the use of three methods (hill-climbing, simulated annealing and genetic algorithms) to automatically generate solutions to the problem. The processing power of computers and the repetitive search nature of this problem means that there is great potential for the automation of this process. The paper will conclude by discussing and comparing these methods and showing how they cope with a highly constrained problem.