2026-06-30T01:11:21Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/74412024-05-10T10:37:31Zcom_10259_5645com_10259_5086com_10259_2604col_10259_5646

Costas-Gual, José Puche Regaliza, Julio César Ponte, Borja Gupta, Mahesh C. Agent-based modelling Model-driven decision support system Petri nets Product-mix problem Simulation Theory of constraints Product-mix problems, where a range of products that generate different incomes compete for a limited set of production resources, are key to the success of many organisations. In their deterministic forms, these are simple optimisation problems; however, the consideration of stochasticity may turn them into analytically and/or computationally intractable problems. Thus, simulation becomes a powerful approach for providing efficient solutions to real-world productmix problems. In this paper, we develop a simulator for exploring the cost of uncertainty in these production systems using Petri nets and agent-based techniques. Specifically, we implement a stochastic version of Goldratt’s PQ problem that incorporates uncertainty in the volume and mix of customer demand. Through statistics, we derive regression models that link the net profit to the level of variability in the volume and mix. While the net profit decreases as uncertainty grows, we find that the system is able to effectively accommodate a certain level of variability when using a Drum-Buffer-Rope mechanism. In this regard, we reveal that the system is more robust to mix than to volume uncertainty. Later, we analyse the cost-benefit trade-off of uncertainty reduction, which has important implications for professionals. This analysis may help them optimise the profitability of investments. In this regard, we observe that mitigating volume uncertainty should be given higher consideration when the costs of reducing variability are low, while the efforts are best concentrated on alleviating mix uncertainty under high costs. 2023-02-13 2023-02-13 2023-02 info:eu-repo/semantics/article 1569-190X http://hdl.handle.net/10259/7441 10.1016/j.simpat.2022.102660 eng Simulation Modelling Practice and Theory. 2023, V. 123, 102660 https://doi.org/10.1016/j.simpat.2022.102660 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117021GB-I00/ES/ACELERANDO LA TRANSICIÓN HACIA REDES DE ECONOMÍA CIRCULAR RESILIENTE: PREVISIÓN, CONTROL DE INVENTARIOS Y PRODUCCIÓN, LOGÍSTICA INVERSA Y DINÁMICA DE LA CADENA DE SUMINISTRO/ http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier