A DECOMPOSITION METHOD FOR PRODUCTION PLANNING UNDER DEMAND UNCERTAINTY IN WOOD REMANUFACTURING - Rafiei, Rezvan; Nourelfath, Mustapha; Santa-Eulalia, Luis Antonio (International Journal of Industrial Engineering: Theory, Applications and Practice, 2022)
Wood remanufacturers grapple with several challenging characteristics, such as divergent co-production, alternative processes, short order cycles, dynamic market, and imperfect raw materials. Production planning is a difficult task with such complex characteristics, especially when demand is random. In this paper, the production planning problem in wood remanufacturing mills is formulated as two-stage stochastic programming with recourse to the randomness arising from the exact required products volume resulting from the different market segments. A Benders’ decomposition algorithm is proposed as a solution method. Some enhancement techniques, namely, lower-bound inequalities, multi-cut framework, and Pareto-optimality cuts, are applied to accelerate the convergence of the Benders’ decomposition algorithm. The computational results indicate that the Benders’ decomposition algorithm is efficient in solving small problems; however, the accelerated Benders’ decomposition outperforms the Benders’ decomposition on larger problems with a large number of scenarios.