  The scheduling of production resources (such as associating jobs to machines)
plays a vital role for the manufacturing industry not only for saving energy
but also for increasing the overall efficiency. Among the different job
scheduling problems, the JSSP is addressed in this work. JSSP falls into the
category of NP-hard COP, in which solving the problem through exhaustive search
becomes unfeasible. Simple heuristics such as FIFO, LPT and metaheuristics such
as Taboo search are often adopted to solve the problem by truncating the search
space. The viability of the methods becomes inefficient for large problem sizes
as it is either far from the optimum or time consuming. In recent years, the
research towards using DRL to solve COP has gained interest and has shown
promising results in terms of solution quality and computational efficiency. In
this work, we provide an novel approach to solve the JSSP examining the
objectives generalization and solution effectiveness using DRL. In particular,
we employ the PPO algorithm that adopts the policy-gradient paradigm that is
found to perform well in the constrained dispatching of jobs. We incorporated
an OSM in the environment to achieve better generalized learning of the
problem. The performance of the presented approach is analyzed in depth by
using a set of available benchmark instances and comparing our results with the
work of other groups.
