Abstract
As an interdisciplinary field, enterprise engineering can benefit from a wide spectrum of methods, including optimization methods used in Operational Research (OR). However, current optimization approaches (e.g. linear programming) in operations research take a narrow, mathematical view on the problem by simplifying the problem and often making assumptions. As a result, the obtained solutions may significantly differ from what could be obtained by applying a comprehensive enterprise engineering approach. To reduce the discrepancy between solutions obtained from an optimization approach and an enterprise engineering approach, and to obtain a more accurate solution of the problem, in this paper we propose to combine DEMO (an enterprise engineering approach) and Linear Programming (an OR approach). This combination not only helps to obtain accurate results but also to capture crucial views. We hypothesize that these approaches, in a combined manner, capture both the structure and behavior of the enterprise business (business problem), thus providing multiple views. This paper discusses a combination approach and investigates the benefits via an illustrative example.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beer, S.: Decision and Control: The Meaning of Operational Research and Management Cybernetics. Classic Beer Series. Wiley, Chichester (1994)
Williams, H.P.: How Important are Models to Operational Research? IMA J. Management Math. 2(2), 189–195 (1989)
Sarker, R.A., Newton, C.S.: Optimization Modelling: A practical Approach. Manufactoring & Industrial Engineering. CRC Press, New York (2008)
Pochet, Y., Wolsey, L.A.: Mixed Integer Programming Algorithms and Decomposition Approaches. In: Production Planning by Mixed Integer Programming, pp. 185–206 (2006)
Pinto, T.N., BarboÌsa-PoÌvoa, A., Novais, A.: Design of Multipurpose Batch Plants: A Comparative Analysis between the STN, m-STN, and RTN Representations and Formulations. Industrial & Engineering Chemistry Research 47(16), 6025–6044 (2008)
Dietz, J.L.G.: DEMO: Towards a discipline of organisation engineering. European Journal of Operational Research (2001)
Dietz, J.L.G., Hoogervorst, J.A.P.: Enterprise ontology in enterprise engineering. In: Proceedings of the ACM Symposium on Applied Computing, pp. 572–579 (2008)
Barjis, J.: A Business Process Modeling and Simulation Method Using DEMO. In: Enterprise Information Systems, pp. 254–265 (2008)
Dietz, J.L.G.: Enterprise Ontology. Enterprise Ontology (2006)
Bunge, M.: Treatise on Basic Philosophy. Ontology II: A World of Systems, vol. 4. Springer, Heidelberg (1979)
Denning, P., Medina-Mora, R.: Completing the loops. In: ORSA/TIMS Interfaces (1995)
Staggemeier, A.T., Clark, A.R.: A survey of lot-sizing and scheduling models. In: 23rd Annual Symposium of the Brazilian Operational Research Society (SOBRAPO), pp. 938–947. Campos do Jordao SP, Brazil (2001)
Dresbach, S.: Modeling by Construction: A New Methodology for Constructing Models for Decision Support. In: Hawaii International Conference on System Sciences, p. 178 (1996)
Töpfer, A.: Erfolgreich Forschen, Ein Leitfaden fuehr Bachelor-, Master-Studierende und Doktoranden. Springer, Lehrbuch (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ettema, R.W. (2010). Applying DEMO in Operations Research: Lot Sizing and Scheduling. In: Barjis, J. (eds) Enterprise and Organizational Modeling and Simulation. EOMAS 2010. Lecture Notes in Business Information Processing, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15723-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-15723-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15722-6
Online ISBN: 978-3-642-15723-3
eBook Packages: Computer ScienceComputer Science (R0)