1. Proceedings
  2. I3M
  3. 2018
  4. EMSS
  5. 10.46354/i3m.2018.emss.035

An agent based modelling approach for the Office Space Allocation problem

  • a Alexandra Dediu  ,
  • b Dario Landa Silva  ,
  • c Peer-Olaf Siebers  
  • a , b ASAP Research Group, School of Computer Science, University of Nottingham, UK,
  • c IMA Research Group, School of Computer Science, University of Nottingham, UK
Cite as
Dediu A., Landa Silva D., Siebers P. (2018). An agent based modelling approach for the Office Space Allocation problem. Proceedings of the 30th European Modeling & Simulation Symposium (EMSS 2018), pp. 255-264. DOI: https://doi.org/10.46354/i3m.2018.emss.035

Abstract

This paper describes an agent based simulation model to create solutions for the office space allocation (OSA)
problem. OSA is a combinatorial optimization problem concerned with the allocation of available office space to a set of entities such as people. The objective function in the OSA problem involves the minimization of space misuse and the minimization of soft constraints violations. Several exact and heuristic algorithms have been proposed to tackle this problem. This paper proposes a rather different approach by decomposing the problem into smaller goals, which are delegated to individual agents each representing an entity in the problem. Agents have an internal decision making process which guides them throughout their search process for a better allocation (room). That is, agents seek to satisfy their individual requirements in terms of room space and constraints. Computational experiments show that the agent based model exhibits competitive performance in terms of solution quality and diversity when compared to neighborhood search heuristics.

References

  1. Adewumi, A. O., and Ali, M. M., 2010. A multi-level genetic algorithm for a multi-stage space allocation
    problem. Mathematical and Computer Modelling, 51(1-2), 109-126.
  2. Awadallah, M. A., Khader, A. T., Al-Betar, M. A., and Woon, P. C., 2012. Office-space-allocation problem using harmony search algorithm. In International Conference on Neural Information Processing (pp. 365-374). Springer, Berlin, Heidelberg.
  3. Barbati, M., Bruno, G., and Genovese, A., 2012. Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications, 39(5), 6020-6028.
  4. Bolaji, A. L. A., Michael, I., and Shola, P. B., 2017. Optimization of Office-Space Allocation Problem Using Artificial Bee Colony Algorithm. In International Conference in Swarm Intelligence (pp. 337-346). Springer, Cham.
  5. Castillo, F., Riff, M. C., and Montero, E., 2016. New Bounds for Office Space Allocation using Tabu Search. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (pp. 869-876). ACM.
  6. Daniels, A., and Parsons, M. G., 2006. An agent base approach to space allocation in general arrangements. Proceedings of the 9th IMDC, Ann Arbor, MI.
  7. Kincaid, R., Gates, R., and Gage, R., 2007. Space allocation optimization at nasa langley research center. In Proceedings of the Seventh Metaheuristics International Conference, Montreal,
    Canada.
  8. Landa Silva, J. D., 2003. Metaheuristic and multiobjective approaches for space allocation. Thesis (PhD). The University of Nottingham. Landa-Silva, D., and Burke, E. K., 2007. Asynchronous cooperative local search for the office-spaceallocation problem. INFORMS Journal on Computing, 19(4), 575-587.
  9. Lopes, R., and Girimonte, D., 2010. The office-spaceallocation problem in strongly hierarchized organizations. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 143-153). Springer, Berlin,
    Heidelberg.
  10. Macal, C. M., and North, M. J., 2010. Tutorial on agentbased modelling and simulation. Journal of
    simulation, 4(3), 151-162.
  11. Persson, J. A., Davidsson, P., Johansson, S. J., and Wernstedt, F., 2005. Combining agent-based approaches and classical optimization techniques. In Third European Workshop on Multi-Agent Systems.
  12. Polyakovsky, S., and M’Hallah, R., 2007. An agentbased approach to knapsack optimization problems.
    In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 1098-1107). Springer,Berlin, Heidelberg.
  13. Pourdehnad, J., Maani, K. and Sedehi, H., 2002. System dynamics and intelligent agent-based simulation:
    where is the synergy. In Proceedings of the 20 International Conference of the System Dynamics Society.
  14. Sabar, M., Montreuil, B., & Frayret, J. M., 2009. A multiagent- based approach for personnel scheduling in
    assembly centers. Engineering Applications of Artificial Intelligence, 22(7), 1080-1088.
  15. Schelling, T. C., 1971. Dynamic models of segregation. Journal of mathematical sociology, 1(2), 143-186.
  16. Siebers PO, Aickelin U, Celia H and and Clegg C, 2007. A Multi-Agent Simulation of Retail Management
    Practices. In: Proceedings of the 2007 Summer Computer Simulation Conference (pp. 959-966), San Diego, CA, USA.
  17. Trung, N. T., Tuan, T. N., and Anh, D. T., 2009. Informed simulated annealing for optimizing dorm room assignments. In Intelligent Information and Database Systems, 2009. ACIIDS 2009. First AsianConference on (pp. 265-270). IEEE.
  18. Ülker, Ö., and Landa-Silva, D., 2010. A 0/1 integer programming model for the office space allocation
    problem. Electronic Notes in Discrete Mathematics, 36, 575-582.
  19. Ülker, Ö., and Landa-Silva, D., 2011. Designing difficult office space allocation problem instances with
    mathematical programming. In International Symposium on Experimental Algorithms (pp. 280-291). Springer, Berlin, Heidelberg.
  20. Ülker, Ö., and Landa-Silva, D., 2012. Evolutionary local search for solving the office space allocation
    problem. In Evolutionary Computation (CEC), 2012 IEEE Congress on (pp. 1-8). IEEE.
  21. Ülker, Ö., 2013. Office space allocation by using mathematical programming and meta-heuristics. Thesis (PhD). The University of Nottingham.