Rezvan Rafiei
Business School, Université de Sherbrooke, Sherbrooke, Canada, 2500, boulevard de l'Université Sherbrooke, Québec, QC, J1K 2R1, Canada.

Mustapha Nourelfath
Department of Mechanical Engineering, Université Laval, Quebec, Canada, Local 3344, Pavillon Adrien-Pouliot, Université Laval, Québec, QC, Canada.

Luis Antonio De Santa-Eulalia
Business School, Université de Sherbrooke, Sherbrooke, Canada, 2500, boulevard de l'Université Sherbrooke, Québec, QC, J1K 2R1, Canada.

DOI https://doi.org/10.33889/IJMEMS.2021.6.2.032

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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.

Keywords- Multi-stage stochastic programming, Production planning, Demand uncertainty, Co-production, Wood remanufacturing sector.

Citation

Rafiei, R., Nourelfath, M., & Santa-Eulalia, L. A. D. (2021). Dealing with Stochasticity in Wood Remanufacturing Operations Planning. International Journal of Mathematical, Engineering and Management Sciences, 6(2), 522-540. https://doi.org/10.33889/IJMEMS.2021.6.2.032.