{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:25:36Z","timestamp":1777490736293,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2003,12,1]],"date-time":"2003-12-01T00:00:00Z","timestamp":1070236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Concurrent Engineering"],"published-print":{"date-parts":[[2003,12]]},"abstract":"<jats:p>High-tech products, like automobiles and aircrafts, consist of many components. To evaluate component designs, simulation tools are frequently used. However, component complexity often results in very large simulation times, so the number of evaluated designs is limited. Further, relations among components make the design of the final product a very hard task that can only be accomplished by a proper coordination of all the simulation tools. The first part of this paper gives a framework to deal with simulation-based product design for cases where there are multiple coupled simulation tools and large simulation times. We call our approach Collaborative Metamodeling (CMM). The CMM approach focuses on the construction of metamodels for components, in order to gain insight in the behavior of components and the final product. This knowledge is used in finding an optimal and robust product design. The second part of this paper deals with the coordination of simulation tools. Therefore, we define three coordination methods: Parallel simulation, Sequential simulation, and Sequential modeling. To compare these three methods, we focus on five aspects: use of precedent information, coordination complexity, simulated product designs, flexibility, and throughput time. For the throughput time aspect we derive mathematical formulas and give relations between the throughput times of the three coordination methods. At the end of this part we summarize the results and give recommendations on the choice of a suitable coordination method. The third part of this paper contains a case-study, in which the CMM approach is applied to the design process of a color picture tube.<\/jats:p>","DOI":"10.1177\/1063293x03039895","type":"journal-article","created":{"date-parts":[[2004,4,21]],"date-time":"2004-04-21T19:33:37Z","timestamp":1082576017000},"page":"267-278","source":"Crossref","is-referenced-by-count":13,"title":["Collaborative Metamodeling: Coordinating Simulation-based Product Design"],"prefix":"10.1177","volume":"11","author":[{"given":"Bart","family":"Husslage","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edwin","family":"van Dam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dick","family":"den Hertog","sequence":"additional","affiliation":[{"name":"Department of Econometrics and Operations Research, Tilburg University,                        P.O. Box 90153, 5000 LE Tilburg, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Stehouwer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erwin","family":"Stinstra","sequence":"additional","affiliation":[{"name":"Centre for Quantitative Methods, P.O. Box 414, 5600 AK Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2003,12,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1080\/002075499190374"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"2. Barton, R.R. (1998). Simulation Metamodels, In: Proceedings of the 1998 Winter Simulation Conference, pp. 167\u2013176, Washington, USA .","DOI":"10.1109\/WSC.1998.744912"},{"key":"atypb3","unstructured":"3. Bazaara, M.S., Jarvis, J.J. and Sherali, H.D. (1990). Linear Programming and Network Flows, John Wiley & Sons, New York ."},{"key":"atypb4","unstructured":"4. Birge, J.R. and Murty, K.G. (eds), (1994). Mathematical Programming: State of the Art, University of Michigan Press, Ann Arbor, MI ."},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1007\/BF01197708"},{"key":"atypb6","unstructured":"6. Brekelmans, R., Driessen, L., Hamers, H. and den Hertog, D. (2001). A New Sequential Optimization Approach to Product and Process Design Involving Expensive Simulations , In: Proceedings of the Third ASMO UK\/ ISSMO Conference on Engineering Design Optimization: Product and Process Improvement, pp. 49\u201352 ."},{"key":"atypb7","doi-asserted-by":"crossref","unstructured":"7. Conn, A.R., Scheinberg, K. and Toint, P.L. (1997). Recent Progress in Unconstrained Nonlinear Optimization without Derivatives , Mathematical Programming, 79(1\u20133): 397\u2013414 .","DOI":"10.1007\/BF02614326"},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1137\/0804044"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.6.2.207"},{"key":"atypb10","doi-asserted-by":"crossref","unstructured":"10. Glover, F., Kelly, J.P. and Laguna, M. (1996). New Advances and Applications of Combining Simulation and Optimization , In: Proceedings of the 1996 Winter Simulation Conference, pp. 144\u2013152 .","DOI":"10.1145\/256562.256595"},{"key":"atypb11","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"atypb12","unstructured":"12. Kleijnen, J.P.C. (1987). Statistical Tools for Simulation Practitioners, Marcel Dekker, Inc., New York ."},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(98)00392-0"},{"key":"atypb14","doi-asserted-by":"crossref","unstructured":"14. Kodiyalam, S. and Sobieszczanski-Sobieski, J. (2001). Multidisciplinary Design Optimization\u2013Some Formal Methods, Framework Requirements, and Application to Vehicle Design , International Journal of Vehicle Design, Special Issue: 3\u201322 .","DOI":"10.1504\/IJVD.2001.001904"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1109\/17.509986"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1080\/00207549008942841"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(94)00035-T"},{"key":"atypb18","unstructured":"18. Papalambros, P.Y. (2001). Analytical Target Cascading in Product Development , In: Proceedings of the Third ASMO UK\/ISSMO Conference on Engineering Design Optimization, pp. 3\u201316 , Harrogate, UK."},{"key":"atypb19","unstructured":"19. Pinter, J.D. (1996). Global Optimization in Action, Kluwer Academic Publishers, Dordrecht ."},{"key":"atypb20","unstructured":"20. Powell, M.J.D. (2000). UOBYQA: Unconstrained Optimization by Quadratic Approximation, Numerical Analysis Report DAMTP 2000\/NA14, University of Cambridge."},{"key":"atypb21","doi-asserted-by":"crossref","unstructured":"21. Sobieszczanski-Sobieski, J. and Hafka, R.T. (1996). Multidisciplinary Aerospace Design Optimization: Survey of Recent Developments , Presented at the 34th AIAA Aerospace Sciences Meeting and Exhibit.","DOI":"10.2514\/6.1996-711"},{"key":"atypb22","unstructured":"22. Stehouwer, H.P. and den Hertog, D. (1999). Simulationbased Design Optimisation: Methodology and Applications , In: Proceedings of the First ASMO UK\/ ISSMO Conference on Engineering Design Optimization, Ilkley, UK."},{"key":"atypb23","unstructured":"23. Stinstra, E.D. and den Hertog, D. (2003). Robust Optimization Using Computer Experiments, (Working paper)."},{"key":"atypb24","unstructured":"24. Stinstra, E.D., Stehouwer, H.P. and Van Der Heijden, J. (2003). 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