Mexican oregano is a non-timber forest product harvested in natural vegetation and represents an important source of income for rural families. Recent reports have highlighted decreases in natural populations caused by increased harvest intensity. Oregano leaf harvesting is a complex problem, involving different components and views, and has a clear spatial dimension. We proposed an analytical framework based on multi-criteria-multi-objective analyses. GIS tools were used as the platform for managing, displaying and analyzing ecological and socioeconomic information from different sources in order to evaluate land suitability of three different management strategies for two competing land objectives: oregano Harvest and oregano Regeneration. The incorporation of environmental evaluation criteria in the analysis allowed the identification of new potential oregano harvesting areas which were neither reported by harvesters, nor registered during harvesting trips. Socio-economic criteria, such as land tenure, highlighted the fact that a substantial proportion of current oregano harvesting areas are located outside ejido limits resulting in potential conflicts for resource access. The proposed Balanced oregano management strategy, in which the same proportion of suitable area (50%) was assigned to both objectives, represents the most favorable management strategy. This option allows harvesters to continue earning an income from oregano leaf harvest; and at the same time helps in the selection of the best areas for oregano regeneration. It also represents a management strategy with a smaller impact on oregano populations and on the harvesters ́ income, as well as lower monitoring costs. The proposed analytical frame-work may contribute to advance the application of systematic approaches for solving decision-making problems in areas where oregano leaves and other NTFP are harvested.
MULTIFILE
Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
Food production has put enormous strain on the environment. Supply chain network design provides a means to frame this issue in terms of strategic decision making. It has matured from a field that addressed only operational and economic concerns to one that comprehensively considers the broader environmental and social issues that face industrial organizations of today. Adding the term “green” to supply chain activities seeks to incorporate environmentally conscious thinking in all processes in the supply chain. The methodology is based on the use of Life Cycle Assessment, Multi-objective Optimization via Genetic Algorithms and Multiple-criteria Decision Making tools (TOPSIS type). The approach is illustrated and validated through the development and analysis of an Orange Juice Supply Chain case study modelled as a three echelon GrSC composed of the supplier, manufacturing and market levels that in turn are decomposed into more detailed subcomponents. Methodologically, the work has shown the development of the modelling and optimization GrSCM framework is useful in the context of eco-labelled agro food supply chain and feasible in particular for the orange juice cluster. The proposed framework can help decision makers handle the complexity that characterizes agro food supply chain design decision and that is brought on by the multi-objective nature of the problem as well as by the multiple stakeholders, thus preventing to make the decision in a segmented empirical manner. Experimentally, under the assumptions used in the case study, the work highlights that by focusing only on the “organic” eco-label to improve the agricultural aspect, low to no improvement on overall supply chain environmental performance is reached in relative terms. In contrast, the environmental criteria resulting from a full lifecycle approach is a better option for future public and private policies to reach more sustainable agro food supply chains.
