Given the environmental impacts produced by the growing increase in waste electrical and electronic equipment (WEEE) and their current inadequate management, this article proposes a mathematical model to define the best location for installing WEEE collection points. The objective is to minimize the cost of the reverse logistics system concerning transportation, installation, opportunity cost, and distance between points and demand. We used a heuristic created from the greedy randomized adaptive search procedure and genetic algorithm meta-heuristics to solve the model, with part of the model variables being defined by another heuristic or by the JuMP v.0.21.2 and CLP Solver v.0.7.1 packages, to guarantee an optimal response to a subproblem of these variables. The model and its solver were written in the Julia Programming Language and executed in two test scenarios. In the first, three vehicles with small loads must collect at five points. In the second, a vehicle with greater available capacity must collect at five points. The results obtained show that the mathematical model and the heuristic are adequate to solve the problem. Thus, we understood that the proposed method contributes to the literature, given the criticality of the current scenario concerning the management of WEEE, and it can assist managers and public policymakers when providing inputs for decision-making related to the choice of the best location for installing collection points.
Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization's tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees' formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon-Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS.
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