Discrete-Event Stochastic Systems with Copula Correlated Input Processes

2021 ◽  
pp. 1-30
Author(s):  
Lei Lei ◽  
Jian-Qiang Hu ◽  
Chenbo Zhu
Keyword(s):  
Stochastic Systems ◽  
Discrete Event

Information-efficient robotic control

Robotica ◽  
1994 ◽  
Vol 12 (2) ◽  
pp. 157-164 ◽  
Author(s):  
Oded Maimon ◽  
Mark Last
Keyword(s):  
Stochastic Systems ◽  
Autonomous Robots ◽  
Discrete Event ◽  
Information Economics ◽  
Robotic Systems ◽  
Robotic Control ◽  
New Approach ◽  
Discrete Event Control ◽  
Available Information

SUMMARYThe work demonstrates a new approach to design of a level of intelligent control of robotic systems. The analytical model results in operational decisions. The structure of these decisions make them readily available to be implemented as an expert system. The approach is applied to a case study of mobile supervisory robots. The model presented here was motivated by manufacturing robotic systems and a type of autonomous robots that collect information at different sites for safety and other control purposes. The robot actions are directly affected by the obta~ned data. At each site the amount of available information (and thus the correctness of the robot decision) can be increased if the robot keeps collecting data at that site for a longer period of t~me. This means a delay in reacting and in reaching next site and accordingly, a decrease in the general amount of robot's information on the whole system.The method of finding an economic amount of information collected by a robot at each site is based on the theory of controlled discrete event stochastic systems developed in our earlier works. This theory combines he basic concepts of discrete event control extended to stochastic systems with some aspects of information economics.

Resource Allocation and Job Dispatching for Unreliable Flexible Flow Shop Manufacturing System

2012 ◽  
Vol 445 ◽  
pp. 947-952
Author(s):  
Umar M. Al-Turki ◽  
Haitham Saleh ◽  
Tamer Deyab ◽  
Yasser Almoghathawi
Keyword(s):  
Resource Allocation ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Stochastic Systems ◽  
Flow Shop ◽  
Discrete Event ◽  
Batch Size ◽  
Objective Functions ◽  
Flexible Flow Shop

Resource allocation, product batching and production scheduling are three different problems in manufacturing systems of different structures such as flexible flow shop manufacturing systems. These problems are usually dealt with independently for a certain objective function related to production efficiency and effectiveness. Handling all of them in an integrated manner is a challenge facing many manufacturing systems in practice and that challenge increases for highly complicated and stochastic systems. Random arrival of products, machine setup time requirements, unexpected machine breakdowns, and multiple conflicting objective functions are some of the common complications in such systems. This research attempts to study the integrated problem under the mentioned complications with various objective functions. The decisions parameters are the batch size, the number of machines at each workstation, and the dispatching policy. Discrete event simulation is used as an optimization tools. The system is modeled using the ARENA software and different scenarios are tested for optimum parameter selection under different conditions.

THE IMPACT OF INTELLIGENT TOOLS ON SIMULATION METHODOLOGY

1996 ◽  
Vol 06 (01) ◽  
pp. 135-158 ◽  
Author(s):  
PAUL A. SAVORY ◽  
GERALD T. MACKULAK
Keyword(s):  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Simulation Analysis ◽  
Discrete Event ◽  
Computer Software ◽  
Simulation Environment ◽  
Simulation Methodology ◽  
Intelligent Simulation ◽  
Intelligent Tools ◽  
The Impact

Simulation is one of the most effective techniques for analyzing stochastic systems. Recent computer software and hardware advances have had an important impact on the traditional discrete-event simulation methodology. Intelligent simulation environments consisting of integrated sets of “intelligent” tools for performing simulation studies have emerged. These tools significantly impact the methodology of a simulation analysis. This paper defines these intelligent tools and discusses how they alter the simulation paradigm by illustrating the development of a simulation model using an intelligent simulation environment. Special emphasis is on how an intelligent simulation environment provides a responsive analysis technique for studying manufacturing systems.

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