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SIMULATING CONTINUOUS TIME
PRODUCTION FLOWS IN FOOD INDUSTRY
BY MEANS OF DISCRETE EVENT
SIMULATION
FABIO BURSI, ANDREA
FERRARA, ANDREA GRASSI, CHIARA
RONZONI

10/8/2013
FABIO BURSI - THE 10° INTERNATIONAL MULTIDISCIPLINARY MODELING
& SIMULATION MULTICONFERENCE
2
Outline
1. Context, Aim and Core;
2. Simulation Units: Base Unit;
3. The Base Unit Model;
4. Additional Parameters;
5. The Base Unit Model: Schema;
6. In-depth Description of two objects:
a) Failures and repairs object;
b) Working speed and accumulation object.

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Context, Aim and Core
Context
◦ Continuous flow processes: food and beverages industry;
◦ Automated high speed lines: automated packaging lines;
Aim
◦ New modelling framework that aims to reproduce the behavior of a continuous
time stochastic process;
Core
◦ Define a generalized model able to represent a continuous time process by
using a discrete event approach, in which the events are signals related to the
changes of state of the simulation units.

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Simulation Units: Base Unit
All simulation units can be modelled by a generalized base unit able
to represent the main categories of working units found in real
applications, such as:
◦ Work centres continuously operating on the flow;
◦ Buffers;
◦ Conveying units.
Performance measures:
◦ Throughput;
◦ Efficiency;
◦ Stay time in different status;
◦ …

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The Base Unit Model
The base unit model behaviours are:
◦ Failures and repairs (FR):
◦ represents the operational state of the unit and is related to the Time-To-Failure (TTF)
and the Time-To-Repair (TTR) profiles.
◦ Working speed and accumulation (WSA):
◦ makes possible, on one hand, to model the variation in working speed as a
consequence of state changes in the upstream or in the downstream flow and, on the
other hand, to model the variation in internal accumulation level.
◦ Throughput time (T):
◦ represents a delay that has to be applied to a signal exiting from the considered unit.

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Additional Parameters
The proposed modelling approach provides the capability to include
additional parameters:
◦ Temperature of the product;
◦ Concentration of pollutant substances;
◦ …
In food industry, it is critical to trace this kind of parameters because
they are related to the process control policies and the product
traceability strategies, as well as product waste and net efficiency
estimation.

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The Base Unit Model: Schema
The base unit model is
composed by:
◦ Failures and repairs (FR);
◦ Working speed and
accumulation (WSA);
◦ Throughput time (T);
◦ N additional parameter
models (PARAMk);
◦ Interface (I).Input Signal: {flow parameter, actual working
speed, n additional parameter functions}

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Failures and Repairs Object Model
• Entering state: up;
• Two different cases can
be found:
1. A working speed change
signal comes from the
WSA object (operation
dependent);
States: up – set – up;
2. An internal state change
happens;
States: up – down – set – up;

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Working speed and accumulation object
model
1. Entering state: init;
2. Wait – update: signal from FR
object (state up);
3. Wait – update: arrives a external
signal;
4. Wait – boundary: when a
boundary state is reached;
5. Wait – update: signal from FR
object (state down);

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Conclusions and Future Development
Conclusions:
◦ The proposed approach:
◦ Allows to save computation time;
◦ Avoids the need to model all production flow;
◦ Ensures the accurate behaviour of the system;
◦ The base unit model is suitable for all industrial plants.
Future Development:
◦ To extend the proposed model with control policies of the considered machines
of the production system.

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…Thanks for your kind
attention

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SIMULATING CONTINUOUS TIME PRODUCTION FLOWS IN FOOD INDUSTRY BY MEANS OF DISCRETE EVENT SIMULATION

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SIMULATING CONTINUOUS TIME PRODUCTION FLOWS IN FOOD INDUSTRY BY MEANS OF DISCRETE EVENT SIMULATION