PyMOSO Software Featured in Launch of INFORMS Journal
PyMOSO, a software package created as part of Purdue IE graduate Dr. Kyle Cooper’s Ph.D. thesis, was recently featured in the launch of the INFORMS Journal on Computing Software and Data Repository: https://informsjoc.github.io/
The PyMOSO software package was developed to provide researchers and practitioners with off-the-shelf access to state-of-the-art algorithms for solving multi-objective simulation optimization problems with integer decision variables. These complex problems arise whenever decision-makers wish to optimize multiple simultaneous objective functions, all of which are defined implicitly through a Monte Carlo simulation model. The algorithms available in the PyMOSO software package, R-PERLE and R-MinRLE, were developed as part of Dr. Cooper’s thesis work. In particular, the R-PERLE algorithm for bi-objective simulation optimization problems is designed for algorithmic efficiency and has provable guarantees on its efficiency and convergence. R-PERLE, as implemented in PyMOSO, is capable of solving important problems in a wide variety of application areas including aviation, environment and sustainability, healthcare, manufacturing, and supply chain management.