Online and stochastic optimization for the harvesting of short rotation coppice. (1st January 2016)

• Record Type: Journal Article
• Authors: Bender, Marco; Tiedemann, Morten; Teuber, Laura
• Is Part Of: Journal of cleaner production. Volume 110(2016:Jan. 01)
• Page Start: 78
• Page End: 84
• Publication Date: 2016-01-01
• Subjects: Online optimization -- Competitive analysis -- Stochastic optimization -- Short rotation coppice -- Economic profitability; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2015.08.120
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Facing an increasing need of biomass and an expected gap between production of wood and utilization, wood from short rotation coppice offers great potential to cover the demand of renewable resources. Short rotation coppice is a form of agricultural land use to provide woodchips for energy supply or wood for industrial applications in a relatively short time. The economic profitability is the most important factor for the adoption of short rotation coppice for energy from biomass. Further on, the profitability of short rotation coppice was found to be most sensitive to the price for biomass and biomass yield. In this work, we present a simplified model for the problem of finding the optimal harvesting and selling policy with respect to a maximum profit by incorporating price and biomass yield as uncertain factors. Since we are dealing with renewable resources, the uncertain nature of these factors (e.g. due to insect infestation, annual weather conditions or unsteady prices) is taken into consideration and the model is especially designed to deal with these uncertainties. In order to incorporate the uncertainties inherent in the determining factors of the problem, the concepts of online optimization and stochastic optimization are applied to a simplified model of the problem. We then derive optimal policies and give explicit results. By means of these mathematical optimization techniques we can prove that our policies are best possible or produce results that are close to an optimal solution.