simulating continuous time production flows in food industry by means of discrete event simulation

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

FABIO BURSI, ANDREA FERRARA, ANDREA GRASSI, CHIARA RONZONI

04/09/2023 FABIO BURSI - THE 10° INTERNATIONAL MULTIDISCIPLINARY MODELING & SIMULATION MULTICONFERENCE

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Outline1. 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

simulating continuous time production flows in food industry by means of discrete event simulation

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