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<dc:creator>Costas-Gual, José</dc:creator>
<dc:creator>Puche Regaliza, Julio César</dc:creator>
<dc:creator>Ponte, Borja</dc:creator>
<dc:creator>Gupta, Mahesh C.</dc:creator>
<dc:date>2023-02</dc:date>
<dc:description>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.</dc:description>
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<dc:identifier>http://hdl.handle.net/10259/7441</dc:identifier>
<dc:language>eng</dc:language>
<dc:publisher>Elsevier</dc:publisher>
<dc:title>An agent-based simulator for quantifying the cost of uncertainty in production systems</dc:title>
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