Effective production planning requires models that are capable of accounting for the complexity and uncertainty intrinsic to manufacturing systems. While the identification of a globally optimal plan is desirable, a more important requirement is the ability of a model to produce production plans that are sufficiently realistic to be implemented in practice and are robust to perturbations in the...

this paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. a novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. parameters of the fuzzy estimator are adapted to minimize the estimat...

In simulating applications for execution on specific computing systems, the simulation performance figures must be known in a short period of time. One basic approach to the problem of reducing the required simulation time is the exploitation of parallelism. However, in parallelizing the simulation new problems arise. Due to the distributed generation of events causality errors can occur, as a ...

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision maki...

Parallel discrete-event simulation (PDES), simply referred to as parallel simulation, is concerned with the execution of discrete-event simulation on parallel computers. PDES has been recognized as a challenging research field bridging between modeling and simulation, and high-performance computing. By exploiting the potential parallelism in a simulation model, PDES can overcome the limitations...