Heuristic-based neural networks for stochastic dynamic lot sizing problem

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.

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► This paper presents the optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with classical learning algorithms in comparison with genetic and bee algorithms-based learning approaches.
► The training phase was also facilitated with Taguchi experimental design approach.
► These approaches have been examined with three domain-specific heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB).
► It is concluded that RLUC was the most successful heuristic to calculate the cost and the neural network model trained with bee algorithm outperforms the other models with better prediction result.
► Hence, the best paired heuristics to help decision makers could are found as RLUC and neural network with bee algorithm.