Abstract
The planning process of wood remanufacturing operations encompasses challenging characteristic, including divergent co-production (one log tree may produce several different products), alternative processes (different receipts exist to produce the same products), short order cycle, dynamic market behaviour (with highly varying demand) and imperfect raw materials (due to its biological nature, the yield vary considerably). To deal with this complexity, in this paper random demand is modeled as scenario tree and three new predictive multi-stage stochastic programming models are developed with multiple objective functions. After implementing them employing datasets from a wood remanufacturing partner in Canada, the proposed models are compared to a reactive re-planning approach. The obtained results indicate that the new models exhibit higher quality solutions in comparison with their corresponding deterministic two-stage models. We also determine the number of stages for which the multi-stage programs provide better planning than the re-planning approach.
Highlights
In the wood remanufacturing sector price-based competition, sawmills are primary processing plants seeking to develop value-added products, which are forwarded to secondary and tertiary plants called wood remanufacturing mills
The proposed predictive multi-stage stochastic models are compared to a reactive rolling-horizon based re-planning method, aiming to identify the number of stages for which the multi-stage programs provide better planning than the re-planning approach. This allows for an evaluation of the gain of using predictive stochastic programming models instead of a reactive planning approach that increases the system nervousness and instability
This article developed three new multi-stage stochastic programming models to deal with the challenging characteristics of the production planning process of wood remanufacturing industry
Summary
In the wood remanufacturing sector price-based competition, sawmills are primary processing plants seeking to develop value-added products, which are forwarded to secondary and tertiary plants called wood remanufacturing mills. An important result consists in determining the number of stages for which the multi-stage programs provide better planning than the re-planning approach This is the first time this kind of problem has been modelled through stochastic programming in the wood remanufacturing sector, and compared to a reactive planning approach. This allows for an evaluation of the gain of using predictive stochastic programming models instead of a reactive planning approach that increases the system nervousness and instability Another important contribution of the paper is that the proposed approach is applied for a real case study of wood remanufacturing production planning, which is presented
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