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It is interesting to compare the impact of various control strategies on the resulting performance measures. As it will turn out, transient strategies yield better performance results than stationary strategies do. In addition, the difference will be investigated between using the mean accumulated reward in finite 24 rate ~ e w a r d No 0 N~ Rj i A 0 i E {1, 2 , 3 } , j E {1,2} Figure 2: Model editor of PENELOPE. time as an optimization criterion and applying the mean accumulated reward until absorption as a criterion.

An initial state, i ~ E 7~. - A boolean function defining whether an event is active (can occur) in a state, - Active : ~ • 7~ -+ {True, False}. - A function defining the effect of the occurrence of an active event on a state: N e w : ~ x ~ -~ f~. Fig. 1 and 2 show the two GSPNs used throughout this paper to illustrate the effect of our techniques (ignore for the moment the dashed boxes). T h e first G S P N models a kanban system, from [2]. It is composed of four instances of essentially the same sub-GSPN.

This is a search of the graph implicitly defined by the model. Function ChooseRemove chooses an element from its argument (a set of states), removes it, and returns it. Procedure SearchInsert searches the first argument (a state) in the second argument (a set of states) and, if not found, it inserts the state in its third argument (also a set of states). T h e "reachability graph" (T~, A) has as nodes the reachable states, and an arc from i to j iff there is an event c such that Active(e, i) = T r u e and N e w ( e , i) = j.

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