The supply chain of spare parts is the intersection between the supply chain, the after-sales

and the maintenance services. Some authors have tried to define improvement paths in terms

of models to satisfy the performance criteria. In addition, other authors are directed towards

the integration of risk management in the demand forecasting and the stock management

(performance evaluation) through probabilistic models. Among these models, the probabilistic

graphical models are the most used, for example, Bayesian networks and petri nets.

Performance evaluation is done through performance indicators.

To measure the appreciation of the supply of the spare parts stock, this paper focuses on the

performance evaluation of the system by petri nets. This evaluation will be done through

an analytical study. The purpose of this study is to evaluate and analyze the performance of

the system by proposed indicators. First, we present a literature review on Petri nets which

is the essential tool in our modeling. Secondly, we present in the third section the analytical

study of the model based on bath deterministic and stochastic petri networks. Finally, we

present an analysis of the proposed model compared to the existing ones.

This paper highlights the storage charging and discharging issue. The study objective is to manage the energy inputs and outputs of the principal grid at the same time in order to maximize profit while decreasing costs, as well as to ensure the availability of energy according to demand and the decisions to either save or search for energy. A fuzzy logic control model is applied in MATLAB Simulink to deal with the system’s uncertainties in scheduling the storage battery technology and the charging- discharging. The results proved that the fuzzy logic model has the potential to efficiently lower fluctuations and prolong the lifecycle.

In this article we present an industrial application of our mathematical model that integrates

planning and scheduling. Our main objective is to concretize our model and compare the

reel results with the theoretical ones. Our application is realized on a conditioning line of

pharmaceutical products at the ECAM EPMI production laboratory. For this reason and to

save time, we used Witness simulation tool. It gives an overall idea of how the line works,

the Makespan of each simulation and it highlights areas for improvement. We looked for

the best resulting sequence which corresponds to the minest Makespan and total production

cost. Then this sequence is applied on the conditioning line of pharmaceutical products for

simulation. On the other hand, we program our mathematical model with the parameters of

the conditioning line under python in version 3.6 and we adopt a simulation/optimization

coupling approach to verify our model.