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An approach is proposed for incorporating the variations in timber growth and yield due to climate change uncertainty into the forest harvesting decision process. A range of possible climate scenarios are transformed by a forest growth and yield model into tree growth scenarios, which in turn are integrated into a multistage stochastic model that determines the timber cut in each future period so as to maximize net present value over the planning horizon. For comparison purposes, a deterministic model using a single average climate scenario is also developed. The performance of the deterministic and stochastic formulations are tested in a case study of a medium-term forest planning problem for a Eucalyptus forest in Portugal where climate change is expected to severely impact production in the coming years. Experiments conducted using 32 climate scenarios demonstrate the stochastic model's superior results in terms of present value, particularly in cases of relatively high minimum timber demand. The model should therefore be useful in supporting forest planners' decisions under climate uncertainty. Key words: forestry, stochastic decision models, forest planning, climate change, uncertainty. Cet article propose une approche qui permet d'incorporer dans le processus de decision concernant la recolte de bois les variations de croissance et de rendement dues a l'incertitude engendree par le changement climatique. Une variete de scenarios climatiques potentiels sont transformes a l'aide d'un modele de croissance et de rendement de la foret en trois scenarios de croissance qui sont ensuite integres dans un modele stochastique multietape lequel determine le calendrier de recolte durant chaque periode future en maximisant la valeur actualisee nette sur l'horizon de planification. Un modele deterministe utilisant un seul scenario climatique moyen a aussi ete developpe pour servir de comparaison. La performance des formulations deterministe et stochastique a ete testee dans une etude de cas portant sur un probleme de planification forestiere a moyen terme pour une foret d'eucalyptus au Portugal oU le changement climatique devrait severement affecter la production dans les annees a venir. Des experiences realisees en utilisant 32 scenarios climatiques demontrent la superiorite des resultats du modele stochastique en termes de valeur actualisee, particulierement dans les cas oU la demande minimum de bois est relativement elevee. Le modele devrait par consequent etre utile pour aider les gestionnaires forestiers a prendre des decisions dans un contexte d'incertitude due au climat. [Traduit par la Redaction] Mots-cles : foresterie, modele decisionnel stochastique, planification forestiere, changement climatique, incertitude., 1. Introduction Uncertainty and risk play an important role in the development of forest management planning (Pasalodos et al. 2013; Yousefpour et al. 2012). Typical examples of these phenomena are [...]

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners—This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

Increasing wildfire activity globally has become an urgent issue with enormous ecological and social impacts. In this work, we focus on analyzing and quantifying the influence of landscape topology, understood as the spatial structure and interaction of multiple land-covers in an area, on fire ignition. We propose a deep learning framework, Deep Fire Topology, to estimate and predict wildfire ignition risk. We focus on understanding the impact of these topological attributes and the rationale behind the results to provide interpretable knowledge for territorial planning considering wildfire ignition uncertainty. We demonstrate the high performance and interpretability of the framework in a case study, accurately detecting risky areas by exploiting spatial patterns. This work reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications to develop robust landscape management plans. We discuss potential extensions and applications of the proposed method, available as an open-source software. • We show the impact of different landscape topologies on wildfire ignitions. • We develop a novel and interpretable deep learning framework. • The model detects and highlights low/high-risk land-cover topological patterns. • We obtain accurate fire occurrence predictions only using land-cover data. • Our framework can be applied in any field exploiting computer vision. [ABSTRACT FROM AUTHOR]