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Historically, height-diameter models have mainly been developed for mature trees; consequently, few height-diameter models have been calibrated for young forest stands. In order to develop equations predicting the height of trees with small diameters, 46 individual height-diameter models were fitted and tested in young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations between the ages of 4 to 8 years, measured from 182 plots in New Brunswick, Canada. The models were divided into 2 groups: a diameter group and a second group applying both diameter and additional stand-or tree-level variables (composite models). There was little difference in predicting tree height among the former models (Group I) while the latter models (Group II) generally provided better prediction. Based on goodness of fit (R 2 and MSE), prediction ability (the bias and its associated prediction and tolerance intervals in absolute and relative terms), and ease of application, 2 Group II models were recommended for predicting individual tree heights within young black spruce and jack pine forest stands. Mean stand height was required for application of these models. The resultant tolerance intervals indicated that most errors (95%) associated with height predictions would be within the following limits (a 95% confidence level): [-0.54 [-17.1%, 18.6%] for jack pine. The recommended models are statistically reliable for growth and yield applications, regeneration assessment and management planning.Key words: composite model, linear model, model calibration, model validation, prediction interval, tolerance interval RÉSUMÉ Historiquement, les modèles hauteur-diamètre ont été élaborés surtout pour des arbres mûrs; en conséquence, rares sont les modèles hauteur-diamètre qui ont été calibrés pour les jeunes peuplements forestiers. Dans le but d' élaborer des équa-tions prédisant la hauteur d'arbres de petit diamètre, 46 modèles différents de hauteur-diamètre ont été ajustés et mis à l' essai dans des jeunes plantations d' épinette noire (Picea mariana) et de pin gris (Pinus banksiana) âgées entre 4 et 8 ans, dont les données provenaient de 182 parcelles situées au Nouveau-Brunswick au Canada. Les modèles ont été divisés en deux groupes : un groupe basé sur le diamètre et un deuxième groupe utilisant à la fois le diamètre et des variables additionnelles provenant du peuplement ou de l'arbre (modèles composites). Nous avons obtenu peu de différence dans la pré-diction de la hauteur des arbres parmi les modèles antérieurs (Groupe I) tandis que les modèles subséquents (Groupe II) ont permis d' obtenir généralement une meilleure prédiction. En fonction de l'homogénéité de la distribution (R 2 et carré de l' erreur moyenne), de la capacité de prédiction (le biais et ses intervalles associés de prédiction et de tolérance en termes absolus et relatifs) et la facilité d'utilisation, deux modèles du Groupe II sont recommandés pour la prédiction de la hauteur d'arbres individuels dans le cas de jeunes peuplements d' épinette n...

With the COVID-19 outbreak hitting the world, the frequency and severity of port congestion caused by various factors are increasing, challenging the stability of international supply chains. Thus, it is necessary to conduct an in-depth study on congestion risks to reduce their adverse impacts on congestion. Although traditional criticality analysis techniques may be capable of ranking port congestion risk in common scenarios, new risk analysis methods are urgently required to tackle uncertainty along with the COVID-19 pandemic. This paper develops a methodology designed for the identification and prioritization of port congestion risk during the pandemic. First, a novel congestion risk assessment model is established by extending the risk prioritization index (RPI) suggested by failure mode and effects analysis (FMEA). Next, the combination of fuzzy Bayesian reasoning, AHP and the variation coefficient method is incorporated into the model in a complementary way to facilitate the treatment of uncertainty and quantitative analysis of the congestion under the different influence of risk factors in ports. Finally, the mode introduces a set of risk utility values for calculating the RPI for prioritization. A real case study and a sensitivity analysis were carried out to illustrate and validate the proposed model. The results proved that the applied method is feasible and functional. In the illustrative example, the top three risk factors are “Interruption of railways/barges services”, “Skilled labor shortage” and “Shortage of truck-drivers/drayage truck”. The findings obtained from this paper could provide useful insights for risk prevention and mitigation.

The satisfaction of requirements and preferences of shippers is critical to enable the practicability of solutions that are derived from intermodal transportation routing problems. This study aims to propose a decision process to help shippers participate better in routing decisions. First, we considered shippers’ requests on transportation cost, timeliness, reliability, and flexibility to construct a multi-objective optimization model. Then, to solve the interactive optimization method that was proposed, NSGA-III was applied to obtain the Pareto front and dominance-based rough set approach to model the preference information. Finally, a case study was conducted and an expert was invited as decision-maker to demonstrate the applicability of the proposed model and the effectiveness of the interactive method for shippers. The results are expected to provide shippers with more rational transportation schemes and insights for the sustainable development of intermodal transportation.