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Throughout the years, scheduling problems have been broadly addressed in the literature due to their wide application in practice. Some examples include the production line optimization, the scheduling aircraft landing, the daily nurse care, among others. In this work one investigate the efficiency of applying geometric-based operators in a version of this problem that deals with the schedule of independent tasks for parallel machines, which can be either identical or unrelated. In order to validate this study, a Variable Neighborhood Search approach is proposed and applied to a specific scheduling problem regarding the minimization of the weighted sum of the earliness/tardiness task, a well-known NP-Hard problem. The test instances are solved for either a due date known a priori or not. The algorithm is compared with two other methods from the literature and the results show promising.

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Microwave Imaging is an essential technique for reconstructing the electrical properties of an inaccessible medium. Many approaches have been proposed employing algorithms to solve the Electromagnetic Inverse Scattering Problem associated with this technique. In addition to the algorithm, one needs to implement adequate structures to represent the problem domain, the input data, the results of the adopted metrics, and experimentation routines. We introduce an open-source Python library that offers a modular and standardized framework for implementing and evaluating the performance of algorithms for the problem. Based on the implementation of fundamental components for the execution of algorithms, this library aims to facilitate the development and discussion of new methods. Through a modular structure organized into classes, researchers can design their case studies and benchmarking experiments relying on features such as test randomization, specific metrics, and statistical comparison. To the best of the authors' knowledge, it is the first time that such tools for benchmarking and comparison are introduced for microwave imaging algorithms. In addition, two new metrics for location and shape recovery are presented. In this work, we introduce the principles for the design of the problem components and provide studies to exemplify the main aspects of this library. It is freely distributed through a Github repository that can be accessed from https://andre-batista.github.io/eispy2d/.