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<field name="value">Costas-Gual, José</field>
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<field name="value">Puche Regaliza, Julio César</field>
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<field name="value">Ponte, Borja</field>
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<field name="value">Gupta, Mahesh C.</field>
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<field name="value">2023-02-13T11:41:36Z</field>
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<field name="value">2023-02-13T11:41:36Z</field>
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<field name="value">2023-02</field>
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<field name="value">1569-190X</field>
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<field name="value">http://hdl.handle.net/10259/7441</field>
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<field name="value">10.1016/j.simpat.2022.102660</field>
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<field name="value">Product-mix problems, where a range of products that generate different incomes compete for a&#xd;
limited set of production resources, are key to the success of many organisations. In their&#xd;
deterministic forms, these are simple optimisation problems; however, the consideration of stochasticity may turn them into analytically and/or computationally intractable problems. Thus,&#xd;
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&#xd;
production systems using Petri nets and agent-based techniques. Specifically, we implement a&#xd;
stochastic version of Goldratt’s PQ problem that incorporates uncertainty in the volume and mix&#xd;
of customer demand. Through statistics, we derive regression models that link the net profit to the&#xd;
level of variability in the volume and mix. While the net profit decreases as uncertainty grows, we&#xd;
find that the system is able to effectively accommodate a certain level of variability when using a&#xd;
Drum-Buffer-Rope mechanism. In this regard, we reveal that the system is more robust to mix&#xd;
than to volume uncertainty. Later, we analyse the cost-benefit trade-off of uncertainty reduction,&#xd;
which has important implications for professionals. This analysis may help them optimise the&#xd;
profitability of investments. In this regard, we observe that mitigating volume uncertainty should&#xd;
be given higher consideration when the costs of reducing variability are low, while the efforts are&#xd;
best concentrated on alleviating mix uncertainty under high costs.</field>
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<field name="value">This article was financially supported by the State Research Agency of the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/50110 0 011033), via the project SPUR, with grant ref. PID2020–117021GB-I00. In addition, the authors greatly appreciate the valuable and constructive feedback received from the Editorial team of this journal and two anonymous reviewers in the different stages of the review process.</field>
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<field name="value">Simulation Modelling Practice and Theory. 2023, V. 123, 102660</field>
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<field name="value">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/</field>
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<field name="value">Agent-based modelling</field>
<field name="value">Model-driven decision support system</field>
<field name="value">Petri nets</field>
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<field name="value">An agent-based simulator for quantifying the cost of uncertainty in production systems</field>
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