In this work we are interested in performance evaluation of an AS/RS system. This evaluation is based on two very important parameters: the cycle time and the energy cost. We aim to perform a modelling of the cycle time of the machine used in such a system, under random conditions, in order to estimate the average cycle time, particularly the multiple cycle time where the machine can perform several tasks and thus take different possible paths. Each of these paths represents distances travelled with a speed and each will have an operating cost in terms of time and implicitly of throughput rate and especially of energy cost. For such a system, considered as a discrete event system DES, our idea is to model the cycle time of the machine using a robust and formal tool, that of timed TPN, in order to represent the different distances travelled and express the consumed times, the formulas obtained will make it possible to know the respective energy costs and thus perform an optimisation of the path in order to optimize the different costs, particularly energy consumption, with all the consequences that this will have on the overall performance of the system, in particular in terms of efficiency and productivity, but also in terms of improving the design, i.e. the space and money invested. Our work is applied to a Mobile Rack AS/RS but we also aim to extend it to other classes and configurations of AS/RS, because this word is the first step of a whole work which aims to build a learning knowledge base for an AI tool dedicated to improve conception, management and optimisation for AS/RS in terms of cycle time, energy and throughput but also for cyber security issues in connected AS/RS.
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Yassine Boudghene Stambouli
Samir Hamaci
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Stambouli et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69c37c33b34aaaeb1a67f00a — DOI: https://doi.org/10.1051/e3sconf/202669902008/pdf