In the last 25 years, extensive research has been carried out addressing the flexible job
shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to …

This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …

In recent years, significant attentions have been devoted to design of metaheuristic
optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are …

Estimation of landslide susceptibility is still an ongoing requirement for land use
management plans. Here, we proposed two novel intelligence hybrid models that rely on an …

In the actual manufacturing process, the environment of the job shop is complex. There will
be many kinds of uncertainties such as random job arrivals, machine breakdowns, order …

Mathematical models are useful tools for simulation, evaluation, optimal operation and
control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the …

This work addresses the flexible job shop scheduling problem considering new job arrivals
which is a common occurrence in real-world manufacturing enterprises. With growing …

Grey wolf optimization (GWO) algorithm is a new population-oriented intelligence algorithm,
which is originally proposed to solve continuous optimization problems inspired from the …

In this research, a parallel machine sequence-dependent group scheduling problem with
the goal of minimizing total weighted earliness and tardiness is investigated. First, a …

Floods is a natural hazard that occurs over a short time with a high transmission speed.
Flood risk management in many countries employs flood susceptibility modeling to mitigate …