springsim 2014 - tampa bay

ON THE GENERALIZATION OF CONTINUOUS-TIME STOCHASTIC PROCESSES

SIMULATION FOR INDUSTRIAL PRODUCTION MODELING

Fabio Bursi, Andrea Ferrara, Andrea Grassi, Chiara Ronzoni

Chiara Ronzoni - Spring Simulation Multiconference '14 - Tampa (FL)

• The objective

• The base unit model

• The working speed and accumulationobject

• The logic control object

• The throughtput time object

• Conclusions


OUTLINE

THE OBJECTIVE

• New modeling framework for the simulation of• Flow manufacturing processes

• Manufacturing processes characterized by a high production rate

• Typical approaches in simulating industrial processes:• Flow discretization

• Identification of dummy units of material


Escape from system

complexity and

Less accuracy

Computational effort

proportional to the

productione rate

VS

THE OBJECTIVE


• Extention of a previous paper:“Simulating Continuous Time Production Flows In Food Industry By Means

Of Discrete Event Simulation”. Proceedings of the International Conference on Modeling and Applied

Simulation, 2013 ISBN 978-88-97999-23-2 Affenzeller, Bruzzone, De Felice, Del Rio,

Frydman, Massei, Merkuryev, Eds.

• Modeling framework able to:• Represent the main units of a prduction system

• Trigger events corresponding to a status change or parameters

variations

• Manage "physical signals"

• Broadcast its functions to the downstream/upstream unit

THE OBJECTIVE

• Logical signals• Connect couples of units

• Do not follow the production flow

• Are triggered by parameters variations


W1 W2

W4W3

B1

B3

Physical flow

Logical flow

THE OBJECTIVE

• Logical signals• Connect couples of unit

• Do not follow the production flow

• Are triggered by parameters variations


0

0.5

1

1.5

2

2.5

values

time

Parameter k

A logical signal is triggeredThreshold 1

Threshold 2


THE BASE UNIT MODEL

• It can behave as:

• Work centers

• Buffers

• Conveying units

• It is able to manage:

• Failures and repairs

• Working speed and accumulation

• Throughput time

• It changes the behaviour simply by the setting of variables

• It has the capability to generate and receive logical signals

• 2 type of signals:

• Physical signal

• Logical signal

• Dedicated I/O ports for each signal type

• Matrix of dependancies manages the paths


THE BASE UNIT MODEL


THE BASE UNIT MODEL

• 2 type of signals:

• Physical signal

• Logical signal

• Dedicated I/O ports for each signal type

• Matrix of dependancies manages the paths

WORKING SPEED AND ACCUMULATION OBJECT



LOGIC CONTROL OBJECT


LOGIC CONTROL OBJECT

• m represents the base unit from which the input logical signal has been

sent;

• method is the events flow that need to be executed when an input

logical signal is received ;

• param is the involved parameter;

• t is the threshold achieved by the sending base unit;

• w = {up,down} represents the direction with which the threshold is

reached.


THROUGHTPUT TIME OBJECT

• „V‟ is a finite-dimensional vector where all thresholds values and physical

variables of the parameters are stored.

• „l‟ is the vector V minimum value. It represents the minimun distance in

time to the next event.

CONCLUSIONS

• First modeling framework that can be used in the Continuous-Time Stochastic Processes simulations.

• It can behave as:• Workstations

• Buffers

• Conveying units

• It can manage:• Failures and repairs

• Working speed and accumulation

• Throughput time

• Extention of this modeling framework by the introduction of logical signals in order to allow the system analyst to model the control mechanism to be deployed in the system.


THANK YOU FOR YOUR KINDATTENTION

Chiara Ronzoni – [email protected]


springsim 2014 - tampa bay

Engineering