The parallel machine scheduling problem (PMSP) involves the optimized assignment of a set of jobs to a collection of parallel machines, which is a proper formulation for the modern manufacturing environment. Deep reinforcement learning (DRL) has been widely employed to solve PMSP. However, the...

As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach to solve a PMSP considering...

As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach to solve a PMSP considering...

Meeting the challenge of ensuring dependable and secure logistics systems in changing competitive environments necessitates ongoing adaptation of logistics networks. Therefore, suit-able methods of modeling and simulation are needed to support the adaptation process. The mesoscopic approach...

Meeting the challenge of ensuring dependable and secure logistics systems in changing competitive environments necessitates ongoing adaptation of logistics networks. Therefore, suit-able methods of modeling and simulation are needed to support the adaptation process. The mesoscopic approach...