Mathematics > Optimization and Control
[Submitted on 2 Oct 2022 (v1), last revised 22 Nov 2023 (this version, v3)]
Title:Rolling horizon policies for multi-stage stochastic assemble-to-order problems
View PDFAbstract:Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behavior. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterized by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling.
Submission history
From: Daniele Giovanni Gioia [view email][v1] Sun, 2 Oct 2022 11:34:31 UTC (252 KB)
[v2] Tue, 21 Mar 2023 11:53:16 UTC (166 KB)
[v3] Wed, 22 Nov 2023 05:59:58 UTC (175 KB)
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