Wydział Elektryczny
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Przeglądaj Wydział Elektryczny wg Temat "automated order system"
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Pozycja Open Access Porównanie systemów sterowania zapasami ze zmiennym opóźnieniem dostaw i zaburzonym zapotrzebowaniem: cyklicznego oraz z regulatorem PD i predyktorem Smitha(Społeczna Akademia Nauk, 2016) Orłowski, Przemysław; Chołodowicz, Ewelina; Zachodniopomorski Uniwersytet Technologiczny w Szczecinie; Zachodniopomorski Uniwersytet Technologiczny w SzczecinieIn this paper, we use a mathematical model of an inventory system with time-varying delivery delay and two control systems in order to compare their properties with different variance of noise for the goods flow optimization problem in the inventory systems. Control systems structures based on mathematical discrete equations are given for optimization: a Periodic Inventory System with adaptive maximal inventory level and Proportional-derivative Inventory Control System with Smith predictor and adaptive reference stock proposed by the authors in previous works. Parameters were selected for each of the control system structures through solving bictriterial optimization task for a specific scenario of variable market demand using SPEA2 in Matlab/Simulink. In this article, we mainly highlight which control structure reaches better performance taking account probabilistic disturbance in market demand.Pozycja Open Access Zastosowanie sterowania feedback-feedforward i predyktora Smitha do optymalizacji przepływu towarów w magazynie ze zmiennym w czasie opóźnieniem(Społeczna Akademia Nauk, 2016) Orłowski, Przemysław; Zachodniopomorski Uniwersytet Technologiczny w SzczecinieThe paper proposes the use of the automatic control system to control the system for supplementary orders for the warehouse in the presence of time-varying market demand and time-varying delay. A discrete-time, time-varying dynamic model of the ware-house system is assumed for the analysis. For the given model it is proposed a feedback-feedforward control system with the Smith predictor. The settings of the control system are evaluated by minimizing cost function using particle swarm optimization. The work contains the results of calculations and computer simulations for the assumed scenario for the market demand.