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This paper presents a novel approach to the strategic planning of the supply chain for lime cultivation. The uncertainty associated with lime production due to factors such as transportation, crime, and water availability, among others, represents different challenges. To address these challenges, the authors propose the use of machine learning tools coupled with deterministic optimization to obtain optimal solutions for the supply chain for future years. This paper highlights the use of justice schemes to ensure an equitable distribution of lime production between farmers and learning machines to quantify the insecurity impact on lime production in Michoacan. The problem can be solved using a Mixed-Integer Linear Programming (MILP) model that focuses on maximizing economic benefits, minimizing the use of irrigation water in crops, and minimizing Carbon Dioxide Equivalent (CO 2eq) emissions within the supply chain. The results demonstrate that the proposed approach can effectively address the challenges facing the lime production supply chain, resulting in an optimized and equitable lime production and distribution between farmers using the Social Welfare Justice scheme. Scenario A presents a utopian viewpoint with maximum benefits and minimum CO 2eq generated emissions, corresponding to $ 4.08 × 109 and 1.94 × 106 tons of CO 2eq , respectively. This paper contributes to the growing body of literature on the use of Artificial Intelligence (AI) in supply chain management, specifically in the optimization of supply chain processes. [Display omitted] [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most biofuels today come from sources that have some relationship with products of the human diet, which has generated controversy regarding their feasibility for industrial-scale production. This work presents a mathematical optimization model for installing bioethanol and biodiesel processing plants involving unconventional bioresources. The considered bioresources are not part of the human diet. The proposed mathematical model considers the maximization of the annual profit considering the installation of biofuel plants and the distribution of these biofuels in areas that present a greater demand for gasoline or diesel to replace fossil fuels. A case study from Mexico was considered since its wide biodiversity. The first scenario presents the optimal solution where there is high feasibility of bioethanol and biodiesel production, obtaining a production of 8.14 × 107 L/year and 2.18 × 108 L/year, respectively. In addition, the feasibility is shown considering the variation in the temporality of the availability of the bioresources for the generation of biofuels. The proposed model is flexible, and it can be used to analyze other case studies. [ABSTRACT FROM AUTHOR]

In recent years electricity sectors worldwide have undergone major transformations, referred to as the "energy transition". This has required energy planning to quickly adapt to provide useful inputs to the regulation activity so that a cost-effective electricity market emerges to facilitate the integration of renewables. This paper analyzes the role of system planning and regulations on two specific elements in the energy market design: the concept of firm capacity and the presence of distributed energy resources, both of which can be influenced by regulation. We assess the total cost of different regulatory mechanisms in the Brazilian and Mexican systems using optimization tools to determine optimal long-term expansion for a given regulatory framework. In particular, we quantitatively analyze the role of the current regulation in the total cost of these two electricity systems when compared to a reference "efficient" energy planning scenario that adopts standard cost-minimization principles and that is well suited to the most relevant features of the new energy transformation scenario. We show that two very common features of regulatory designs that can lead to distortions are: (i) renewables commonly having a lower "perceived cost" under the current regulations, either due to direct incentives such as tax breaks or due to indirect access to more attractive contracts or financing conditions; and (ii) requirements for reliability are often defined more conservatively than they should be, overstating the hardships imposed by renewable generation on the existing system and underestimating their potential to form portfolios. [ABSTRACT FROM AUTHOR]

Abstract: Energy use attributed to residential buildings makes up nearly 16% of the total energy consumption in Mexico, yet a larger portion of the Mexican Official Standards for Energy (NOM-ENER) are specific to the commercial building sector which only makes up 3% of the total energy consumption in Mexico. Furthermore the NOM-ENER standards related to residential buildings are developed through a component based approach where energy efficiency guidelines are outlined for individual pieces of equipment with no interactions between these components taken into consideration. In this paper, a holistic and integrative energy analysis approach is considered to improve energy efficiency in residential buildings. Specifically, the study presented in this paper investigates the interactions between various energy efficiency measures and thermal comfort measures for residential buildings in Salamanca, Guanajuato using detailed simulation and optimization procedures. The results from the optimization analysis suggest a combination of improved appliance efficiencies, increased levels of roof and wall thermal insulation and improved water heating system efficiencies is required to achieve a minimum cost solution which results in nearly 52% annual energy savings for new homes. [Copyright &y& Elsevier]

Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous studies for the optimization of operations in one of the most important multi-reservoir systems in Mexico, this work uses SDP, centred on the interest in the convergence criterion used in the optimization process. In the first trial, following the recommendations in the literature consulted, the difference in the absolute value of two consecutive iterations was taken and compared against a set tolerance value and a discount factor. In the second trial, it was decided to take the squared difference of the two consecutive iterations. In each of the trials, the computational time taken to obtain the optimal operating policy was quantified, along with whether the optimal operating policy was obtained by meeting the convergence criterion or by reaching the maximum number of iterations. With each optimization policy, the operation of the system under study was simulated and four variables were taken as evaluators of the system behaviour. The results showed few differences in the two operation policies but notable differences in the computation time used in the optimization process, as well as in the fulfilment of the convergence criterion. [ABSTRACT FROM AUTHOR]

Abstract: Conserving nature in the presence of humans is especially challenging in areas where livelihoods are largely based on locally available natural resources. The restoration of forests in such contexts calls for the identification of sites and actions that improve biodiversity protection, and ensure the provision of and accessibility to other forest-related ecosystem services. This paper introduces an integer-linear programming (ILP) approach to identify reforestation priorities that achieve such goals. Applications of ILP to nature conservation are many, but only a few of them deal with the problem of restoration, and none of the available models considers the basic needs of the local population. Given constraints on a restoration budget, the potential conversion of productive lands and the travel time to reach harvestable forest, the model maximises the amount of reforestation area (weighted by priority values) and minimises the harvesting of existing forest, while ensuring the conservation of landscape diversity, the satisfaction of timber demands and the stabilisation of erosion-prone land. As an input, suitability maps, generated through a combination of ecological criteria, are used to prioritise the selection of reforestation sites. An application to a 430 km2 area in Central Chiapas (Mexico) resulted in compact patches and thus a manageable reforestation plan. Acceptable trade-offs were found between the amount of soil stabilisation possible and the prioritisation goals, while uncertainty in the prioritisation scores did not significantly affect the results. We show that restoration actions can be spatially designed to benefit both nature and people with minimal losses on both sides. [Copyright &y& Elsevier]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper uses simple hydro-economic optimization to investigate a wide range of regional water system management options for northern Baja California, Mexico. Hydro-economic optimization models, even with parsimonious model formulations, enable investigation of promising water management portfolios for supplying water to agricultural, environmental and urban users. CALVIN, a generalized hydro-economic model, is used in a case study of Baja California. This drought-prone region faces significant challenges to supply water to agriculture and its fast growing border cities. Water management portfolios include water markets, wastewater reuse, seawater desalination and infrastructure expansions. Water markets provide the flexibility to meet future urban demands; however conveyance capacity limits their use. Wastewater reuse and conveyance expansions are economically promising. At current costs desalination is currently uneconomical for Baja California compared to other alternatives. Even simple hydro-economic models suggest ways to increase efficiency of water management in water scarce areas, and provide an economic basis for evaluating long-term water management solutions. [Copyright &y& Elsevier]