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This paper addresses a forest harvesting problem with adjacency constraints, including additional environmental constraints to protect wildlife habitats and minimize infrastructure deployment costs. To this end, we propose an integer programming model to include those considerations during the optimization of the harvest regime of a Mexican forest. The model considered was based on the Unit Restriction Model, a benchmark approach that merges the management units before the optimization process. The resulting model, namely the Green Unit Restriction Model (GURM) and the benchmark model (URM) from the literature were tested with the forest Las Bayas, using information obtained from the SiPlaFor project from Universidad Juárez. The proposed model was solvable in all tested instances. Furthermore, a sensitivity analysis study over a core data set of test instances was carried out on the different parameters of the GURM model to determine optimal configurations for the specific case study. Several environmental measures were assessed in our experimental work. The parameters evaluated were the distance value between pairs of units harvested in the same period, the distance value between those considered natural reserve units, the timber volume to be harvested, the green-up period, and the minimum forest reserve area. An interesting observation from the experiments was that the maximum area inversely affected the URM and GURM models; larger regions resulted in a reduced number of management units in the URM model, thus reducing the computational time to solve the instance of the problem, but in this case, at the expense of a reduced profit. One of the interesting findings was that, in all experiments under all different factors, harvesting every 5 or 6 years yields better profits than harvesting every 10 or 12 years. The current standard in the Mexican system is to harvest every 10 years.

In the technician dispatching problem, a given number of repair teams must visit different locations to provide service support. Considering that there is a fixed vehicle capacity and variations in the demand, not all requests can be satisfied on time and therefore some of them must be delayed. Most implementations of the dispatching problem consider a penalty that might vary depending on the customers to internalize that they have heterogeneous costs for being postponed. In this research we analyze how such variations in costs affect the outcome of service planning in the context of an efficient technician dispatching problem. We focus our analysis on two objectives: first, to understand how cost heterogeneity affects the performance of optimal solutions, and second to illustrate how a firm could implement an ad-hoc methodology even in cases where only observable customers’ features can be traced. Specifically, we explore how the distribution of costs affects optimal solutions of allocating teams during a daily operation of the service provider, and then we propose a Markovian model to capture cost-heterogeneity for the case where the cost of failure can be traced to observable operational characteristics. In this model we explicitly consider the cost faced by the customer by having inferior service quality. Our results indicate that when customers are sufficiently different, transportation and total penalty costs decrease gaining in operational efficiency. Moreover, results from the Markovian model indicate that firms can take advantage of these operational gains even in cases where only few customer characteristics are observed.

Methods that estimate the niche of a species by calculating a convex hull or an elliptical envelope have become popular due to their simplicity and interpretation, given Hutchinson’s conception of the niche as an n-dimensional hypervolume.It is well known that convex hulls are sensitive to outliers and do not have the ability to differentiate between regions of low and high concentration of presences, while the elliptical envelopes may contain large regions of niche space that are not relevant for the species. Thus, when the goal is to estimate the realized niche of the species, both methods may overestimate the niche.We present a methodology that combines both the convex hull and the elliptical envelope methods producing an n-dimensional hypervolume that better fits the observed density of species presences, making it a better candidate to model the realized niche. Our method, called the CHE approach, allows defining regions of iso-suitability as a function of the significance levels inherited from the method (Mahalanobis distance model, minimum covariance determinant, or minimum volume ellipsoid) used to fit an initial elliptical envelope from which we then discard regions not relevant for the species by calculating a convex hull.We applied the CHE approach to a case study of twenty-five species of bats present in the Iberian Peninsula, fitting a hypervolume for each species and comparing them to both the convex hulls and elliptical envelopes obtained with the same data and different values of n. We show that as the number of variables used to define the niche space increases, both the convex hull and elliptical envelope models produce overly large hypervolumes, while the size of the hypervolume fitted with the CHE approach remains stable. As a consequence, similarity measures that account for the niche overlap among different species may be inflated when using convex hulls or elliptical envelopes to model the niche; something that does not occur under the CHE approach.