Any stochastic model is very easy derived from a known deterministic model.
You can add stochasticity by introducing rules that cause model parameters to be drawn randomly from known statistical distributions and introducing background effects, such as a certain percentage loss in seeds, if you are studying seed dispersal for example. A deterministic model has no such randomness :)
This question deals with your modelling approach against the related problem. If the modelling framework allows you to solve the related problem analytically, the answer is yes. Conversely, you need some approximations (Gaussian, conjugate-distributions, etc.) or mcmc techniques. In this case, the systems will become more complicated.
Just found two nice links that may help clarifying the issue:
http://www.dcs.gla.ac.uk/~srogers/teaching/mscbioinfo/SysBio2.pdf
http://www.inrialpes.fr/schoolleshouches07/pres/Gonze_LesHouches_1.pdf
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https://books.google.dz/books?id=CQVZAgAAQBAJ&pg=PR10&lpg=PR10&dq=Difference+between+Stochastic+and+Deterministic+Systems+(Mathematically-Physically)?&source=bl&ots=zOBZW7yJqt&sig=0p6Jf8SL8ztQbr-nOLpSpUuDGxA&hl=fr&sa=X&ved=0ahUKEwj-w9yj6_3ZAhVMjiwKHRB2A48Q6AEIWjAL#v=onepage&q=Difference%20between%20Stochastic%20and%20Deterministic%20Systems%20(Mathematically-Physically)%3F&f=false
The issue is more complicated because we haven't yet developed mine production scheduling models with a direct utilization of ODEs. We use optimization methods of Operations Research with a definition of an economic objective function in most cases. Some researchers, however, use classical formulations of stochastic programming ignoring a lot of important factors of mine production scheduling. This is an engineering rather mathematical problem.
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