On Capacity Measurement in Two Classes of Shop Floor Problems with Multifunctional Parallel Machines

  • Virginia Ecaterina Oltean
  • Theodor Borangiu
  • Silviu Răileanu
Part of the Studies in Computational Intelligence book series (SCI, volume 762)


Capacity measurement is of crucial importance in business and manufacturing and is intimately related to both finite and infinite capacity planning. There is a vast literature on this subject and capacity may be defined in various ways. This paper investigates, within two small scale examples, some issues regarding capacity measurement in a shop floor with multifunctional parallel machines that have to process a specified quantity of products of different types, and with specific operations requirements, in a specified working time. In the first example, each operation type has a specific operation unit time, independently on the working machine, while in the second example each machine can execute any operation from its capabilities portfolio in a unique operation’s unit time, with a unique associated operation unit cost. The study emphasizes, in the first example, that the capacity measurement depends not only on machines capabilities, on products requirements and on the imposed working time, but also on the allocation strategy of groups of machines to groups of products, while the second example shows that, in case of machines with unique operation unit time for all operation types, the maximal number of operations executable in given working time is a valid capacity measure. The discussed examples may serve as starting point for defining capacity planning procedures for more complex scenarios that can be tested using dedicated software tools, targeting industrial applications.


  1. 1.
    Chen, J.C., Chen, K.-H., Lin, C.-H., Chen, C.-W., Yang, C.-L.: A study of heuristic capacity planning algorithm for weapon production system. Int. J. Electron. Bus. Manag. 9(1), 46–57 (2011)Google Scholar
  2. 2.
    Schönsleben, P.: Integral logistics management: Planning and control of comprehensive business process. St. Lucie Press, Boca Raton, London (2003)CrossRefGoogle Scholar
  3. 3.
    Jiang, J.C., Chen, K.H., Wee, H.M.: A dynamic scheduling model of parallel-machine group for weapon production. Int. J. Adv. Manuf. Technol. 36(11–12), 1202–1209 (2008)CrossRefGoogle Scholar
  4. 4.
    Wortman, J.C., Euwe, M.J., Taal, M., Wiers, V.C.S.: A review of capacity planning techniques within standard software package. Prod. Plann. Control 7(2), 117–128 (1996)CrossRefGoogle Scholar
  5. 5.
    Chen, J.C., Chen, K.H., Wu, J.J., Chen, C.W.: A study of the flexible job shop scheduling problem with parallel machines and reentrant process. Int. J. Adv. Manuf. Technol. 39(3–4), 344–354 (2008)CrossRefGoogle Scholar
  6. 6.
    Bermon, S., Hood, S.J.: Capacity optimization planning system. Interfaces 29(5), 31–50 (1999)CrossRefGoogle Scholar
  7. 7.
    Murray, M.: Measuring capacity in manufacturing. In: The Balance, Small Business—Supply Chain Management (2016). Accessed 1 July 2017
  8. 8.
    Production capacity law and legal definition. USLegal Dictionary. Accessed 1 July 2017
  9. 9.
    Oltean, V.E., Răileanu, S., Borangiu, Th.: Some aspects concerning a network constrained approach for a class of job-shop process planning. In: Petre, E., Brezovan, M. (eds.) Proceedings of the 20th International Conference on Systems Theory, Control and Computing (ICSTCC), pp. 584–589. Sinaia, Romania, 13–15 Oct 2016Google Scholar
  10. 10.
    Wolsey, L.A.: Integer Programming. Wiley (1998)Google Scholar
  11. 11.
    Optimization modeling with IBM ILOG OPL, Instructor Workbook (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Virginia Ecaterina Oltean
    • 1
  • Theodor Borangiu
    • 1
  • Silviu Răileanu
    • 1
  1. 1.Department of Automation and Applied InformaticsUniversity Politehnica of BucharestBucharestRomania

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