Switching robust neural network control of perishable inventory with fixed shelf life products under time-varying uncertain demand

Abstract

Managing the inventories of perishable items becomes a more challenging task when the demand for such items is uncertain and has intermittent, lumpy or erratic character. In this study practical problem of perishable inventory products, and control is presented and solved. This is an extended version of the research presented in [Cholodowicz E, Orlowski P (2022) Robust control of perishable inventory with uncertain lead time using neural networks and genetic algorithm. In: International Conference on Computational Science. Springer, pp 46–59]. The objective of this study is to find order quantities for the perishable inventory system, that are optimal in terms of stock level, fill rate and outdating. To find the solution, a new approach is developed. The proposed approach is a combination of an artificial neural network, control theory and hybrid optimization. The proposed controller consists of robust neural network controllers which are tuned for selected perturbation bound. Weights switching operation is performed based on the demand uncertainty estimation. The validity of the results is tested on intermittent, lumpy, smooth and erratic demand data series coming from the food industry. One of the key findings of this study is that applying the proposed Switching Robust Neural Network controller provides a significant decrease in cost function in comparison to Robust Neural Network controllers. It is found that the biggest decrease is for smooth demand and the smallest one for the lumpy demand pattern. What distinguishes our work from the current state of the art is the fact that we do not limit that demand is deterministic and constant, we propose a method which calculates the optimal order quantities to minimize costs under different patterns of demand which is uncertain.