A Goal Programming-Monte Carlo Simulation Methodology for Modeling Process Quality Control

• Autorzy: Morán-Zabala Jean P. , Cogollo-Flórez Juan M.

Identyfikatory

DOI

10.24425/mper.2023.146024

• Języki publikacji: EN

Abstrakty

EN

Quality profiling seeks to know the quality characteristics of products and processes to improve customer satisfaction and business competitiveness. It is required to develop new techniques and tools that upgrade and complement the traditional analysis of process variables. This article proposes a new methodology to model quality control of the process and product quality characteristics by applying optimization and simulation tools. The application in the production process of carbonated beverages allowed us to identify the most influential variables on the gas content and the degrees Brix of beverage.

Słowa kluczowe

EN

goal programming; Monte Carlo simulation; process optimization; process simulation; process quality profiling; quality control modeling

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2023
• Tom: Vol. 14, No. 2
• Strony: 70--78
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Morán-Zabala Jean P.

• Department of Quality and Production, Instituto Tecnológico Metropolitano – ITM, Colombia

autor

Cogollo-Flórez Juan M.

• Department of Quality and Production, Instituto Tecnológico Metropolitano – ITM, Colombia

Bibliografia

• Alazemi F.K.A., Ariffin M.K.A.B.M., Mustapha F.B. and Supeni, E.E.B. (2022), A New Fuzzy TOPSISBased Machine Learning Framework for Minimizing Completion Time in Supply Chains, International Journal of Fuzzy Systems, No. 3, Vol. 24, pp. 1669-1695, doi: 10.1007/s40815-021-01226-3.
• Amorim L.F., de Paiva A.P., Balestrassi P.P. and Ferreira J.R. (2022), Multi-objective optimization algorithm for analysis of hardened steel turning manufacturing process, Applied Mathematical Modelling, Vol. 106, pp. 822–843, doi: 10.1016/j.apm.2022.02.011.
• Aouni B. and Kettani O. (2001), Goal programming model: A glorious history and a promising future, European Journal of Operational Research, No. 2, Vol. 133, pp. 225–231, doi: 10.1016/S0377-2217(00)00294-0.
• Aouni B., Martel J.M. and Hassaine A. (2009), Fuzzy Goal Programming Model: An Overview of the Current State-of-the Art, Journal of Multi-Criteria Decision Analysis, Vol. 16, pp. 149–161, doi: 10.1002/mcda.448.
• Belhoul L., Galand L. and Vanderpooten D. (2014), An efficient procedure for finding best compromise solutions to the multi-objective assignment problem, Computers & Operations Research, Vol. 49, pp. 97-106, doi: 10.1016/j.cor.2014.03.016.
• Chanda E. and Dagdelen K. (1995), Optimal blending of mine production using goal programming and interactive graphics systems, International Journal of Surface Mining, Reclamation and Environment, No. 4, Vol. 9, pp. 203–208, doi: 10.1080/09208119508 964748.
• Chang N.B. and Wang S. (1997), A fuzzy goal programming approach for the optimal planning of metropolitan solid waste management systems, European Journal of Operational Research, No. 2, Vol. 99, pp. 303-321, doi: 10.1016/S0377-2217(96)00024-0.
• Cherif M., Chabchoub H. and Aouni B. (2008), Quality control system design through the goal programming model and the satisfaction functions, European Journal of Operational Research, No. 3, Vol. 186, pp. 1084–1098, doi: 10.1016/j.ejor.2007.04.025.
• Delice E. and Zülal G. (2013), Determining design requirements in QFD using fuzzy mixed-integer goal programming: Application of a decision support system, International Journal of Production Research, No. 21, Vol. 51, pp. 6378–6396, doi: 10.1080/00207543.2013.803625.
• Gholizadeh H. and Tajdin A. (2019), Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming, Journal of Industrial and Production Engineering, No. 4, Vol. 36, pp. 258–270, doi: 10.1080/21681015.2019.1646330.
• Gijo E.V. and Ravindran G. (2008), Quality in the construction industry: an application of DOE with goal programming, Total Quality Management, No. 12, Vol. 19, pp. 1249–1255, doi: 10.1080/14783360802351 686.
• İç Y.T., Elaldi F., Pakdil F. and İpek N.E. (2012), Design of experiment and goal programming application for GMAW process, Welding Journal, No. 4, Vol. 91, pp. 106–112.
• Jacobs F.R. and Chase R. (2021), Operations and supply chain management (16th ed), McGraw-Hill, USA.
• Ji W. and AbouRizk S.M. (2018), Simulation-Based Analytics for Quality Control Decision Support: Pipe Welding Case Study, Journal of Computing in Civil Engineering, No. 3, Vol. 32, pp. 1–9, doi: 10.1061/(asce)cp.1943-5487.0000755.
• Kazemzadeh R., Bashiri M., Atkinson A. and Noorossana R. (2008), A general framework for multiresponse optimization problems based on goal programming, European Journal of Operational Research, No. 2, Vol. 189, pp. 421–429, doi: 10.1016/j.ejor.2007.05.030.
• Mohammed G.T. and Hordofa B.G. (2016), The Modified Sequential Linear Goal Programming Method for Solving Multiple Objectives Linear Programming Problems, Pure and Applied Mathematics Journal, No. 1, Vol. 5, pp. 1–8, doi: 10.11648/j.pamj. 20160501.11.
• Musa A. (2020), Goal programming model for optimal water allocation of limited resources under increasing demands, Environment, Development and Sustainability, No. 4, Vol. 23, pp. 5956–5984, doi: 10.1007/s10668-020-00856-1.
• Nuriani D. and Gunawan I. (2020), Recycling Solid Waste of Coconut Oil Industry: A Response SurfaceGoal Programming Approach, Jurnal Optimasi Sistem Industri, No. 2, Vol. 19, pp. 111–121, doi: 10.25077/josi.v19.n2.p111-121.2020.
• Ozdemir A. (2021), An I-optimal experimental designembedded nonlinear lexicographic goal programming model for optimization of controllable design factors, Engineering Optimization, No. 3, Vol. 53, pp. 392-407, doi: 10.1080/0305215X.2020.1732365.
• Salazar E. and Alzate W. (2018), Application of the Monte Carlo simulation in the projection of statement of profit or loss: A case study, Espacios, Vol. 39, No. 51, pp. 11–18.
• Schniederjans M. and Karuppan C. (1995), Designing a quality control system in a service organization: A goal programming case study, European Journal of Operational Research, No. 2, Vol. 81, pp. 249–258, doi: 10.1016/0377-2217(94)00239-9.
• Sengupta S. (1981), Goal Programming Approach to a Type of Quality Control Problem, Journal of the Operational Research Society, No. 3, Vol. 32, pp. 207-211, doi: 10.1057/jors.1981.43.
• Tansel Iç Y., Yurdakul M., Dengiz B. and Saşmaz T. (2022), Analysis of the Robustness of the Operational Performance Using a Combined Model of the Design of Experiment and Goal Programming Approaches for a Flexible Manufacturing Cell, In Press, Journal of Advanced Manufacturing Systems, doi: 10.1142/S0219686723500038.
• Valdés-Manuel J.I. and Cogollo-Flórez J.M. (2022), Monitoring overdispersed process in clinical laboratories using control charts, DYNA, No. 224, Vol. 89, pp. 28-33, doi: 10.15446/dyna.v89n224.103666.
• Wang Y., Zhao S., Zhou Z., Botterud A., Xu Y. and Chen R. (2016), Risk adjustable day-ahead unit commitment with wind power based on chance constrained goal programming, IEEE Transactions on Sustainable Energy, No. 2, Vol. 8, pp. 530–541, doi: 10.1109/TSTE.2016.2608841.
• Zhang Y., Cheng Y. and Su J. (1993), Application of goal programming in open pit planning, International Journal of Surface Mining, Reclamation and Environment, No. 1, Vol. 7, pp. 41–45, doi: 10.1080/0920811930896468.