The generation of energy from renewable sources is a fundamental aspect for the sustainable development of society, and several energy sources such as solar, biomass, biogas, and wind must be used to the maximum to meet existing needs. In Chile, there are villages that are off-grid. A real case study is presented in this research. To meet the needs of this village we have proposed a mathematical optimization model using a CPLEX optimizer to generate the necessary energy power while minimizing the cost of energy (COE). In this study, different scenarios have been evaluated with respect to the existing energy availabilities, for example, in different periods of the year, demonstrated in terms of economic costs, the viability of resources such as biomass and biogas, and the viability of the energy production of wind power given the associated high costs. Finally, the effect of the use of renewable energy in consideration of CO2 emissions is studied in our research.

No abstract

No abstract

In this work, the Cumulative Vehicle Routing Problem (CumVRP) is studied. It is a routing optimization problem, in which the objective is to construct a set of vehicle routes with the minimum cumulative cost in terms of distance and weight over a traveled arc. The CumVRP can be defined with hard and soft time windows constraints for incorporating customer service. To tackle this problem, a matheuristic approach based on combining mathematical programming and an iterative metaheuristic algorithm Greedy Randomized Adaptive Search Procedure (GRASP) is proposed. In each step of our approach, a feasible solution (set of routes) is built using GRASP, and, afterward, the solution is optimized using a MILP optimizer. The main objective of this research is to analyze the trade-off between the environmental cost produced by the delivery of goods complying with the limits of time windows and the customer's dissatisfaction when these limits are violated at a certain time limit previously defined. The results show that the environmental cost is reduced if the violation of the upper limits of the customers' time windows is allowed. These violations generate a cost associated with penalties that are well balanced with respect to the reduction of emissions.

This article is about the current agricultural scenario, where large‐scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so‐called greenhouse gases and increasing the carbon footprint. Land use optimization and land parcel allocation are essential areas of agriculture research that currently represent relevant challenges and are classified as combinatorial optimization problems. In this context, the Selection and Allocation of Land Parcels Problem (SA‐LPP) is proposed; its goal is to optimize the selection and allocation of land parcels with rectangular shapes in small areas available for food production. We propose a reformulation of the SA‐LPP as a variant of the two‐dimensional orthogonal packing problem (2OPP), called Group‐2OPP. This problem was solved through a Mixed‐Integer Linear Programming (MILP) model, but due to the model complexity, we also propose a Greedy Randomized Adaptive Search Procedure metaheuristic approach. Some sensitivity analyses were performed as well to evaluate the impact of parameters on the solutions. Computational results show that the proposed metaheuristic outperforms the MILP model in terms of solution quality and computational times.