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Procedia Manufacturing 00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia Manufacturing 24 (2018) 222–228
Procedia Manufacturing 00 (2017) 000–000
4th International Conference on System-Integrated Intelligence
www.elsevier.com/locate/procedia

4th International Conference on System-Integrated Intelligence

Optimization Model to Extend Existing Production Planning and
Control Systems
Optimization Model toforExtend the Use of Additive
Existing ProductionManufacturing
Planning and
ControlTechnologies
Manufacturing Engineering Society
Systems 2017, for the in theUseIndustrial
International Conference
of Additive
Vigo (Pontevedra), Spain
Production
2017, MESIC 2017, 28-30 June
Manufacturing
Technologies in the Industrial Production
Wjatscheslav Baumunga * , Vladislav V. Fominb
Costing models for capacityAlteburgstraße
optimization in Industry 4.0: Trade-off
Wjatscheslav Baumunga * 150,
Reutlingen University,
a
, Vladislav Fominb
V. Germany
72762 Reutlingen,
between Vytautas
usedMagnuscapacity and8,operational
University, Vileikos
b
efficiency
LT-44404 Kaunas, Lithuania
a b
ReutlingenUniversity of Latvia,
University, Raina 19,
Alteburgstraße LV-1586
150, 72762 Riga, LatviaGermany
Reutlingen,
b
Vytautas Magnus University, Vileikos 8, LT-44404 Kaunas, Lithuania b
A. Santana , P. Afonso , A. Zanin , R. Wernke
b
a a,* b
University of Latvia, Raina 19, LV-1586 Riga, Latvia

Abstract
a
University of Minho, 4800-058 Guimarães, Portugal
b
Unochapecó, 89809-000 Chapecó, SC, Brazil
Abstract
The use of additive manufacturing technologies for industrial production is constantly growing. This technology differs from the
known production procedures. The areas of scheduling, detailed and sequence planning are particularly important for additive
The use of additive
production due to the manufacturing technologies
long print times for industrial
and flexible production
use of the is constantly
production growing.production-relevant
area. Therefore, This technology differs from the
variables are
Abstract
known production
considered and used procedures. The areasplanning
for the production of scheduling, detailed
and control and of
(PPC) sequence
additiveplanning are particularly
manufacturing machines.important
For this for additive
purpose, an
production
optimizationduemodelto the long print
is presented timesshows
which and aflexible use of build
time-oriented the production area. Therefore,
space utilization. production-relevant
In the implementation, a nestingvariables
algorithmare
is
Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected,
considered
used to check andtheused for the production
combinability planning
of different modelsandfor control (PPC) ofprint
each individual additive
job. manufacturing machines. For this purpose, an
information basedisonpresented
optimization model a real time
whichbasis
shows and, necessarily,build
a time-oriented much more
space efficient.
utilization. In In
thethis context, capacity
implementation, optimization
a nesting algorithm is
goes
used
© 2018beyond
to check the traditional
the combinability
The Authors. aim
Published of of capacity
by different
Elsevier modelsmaximization, contributing
Ltd. for each individual print job. also for organization’s profitability and value.
Indeed, lean management and continuous improvement approaches suggest capacity
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) optimization instead of
© 2018
© 2018 The
The Authors.
maximization. The
Authors. Published
study of
Published by
by Elsevier
capacity
Elsevier B.V.
optimization
Ltd. and costing models is an important research topic that deserves
Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
contributions
This is an openfrom
Intelligence. accessboth theunder
article practical
the CC and theoretical
BY-NC-ND perspectives.
license This paper presents and discusses a mathematical
(https://creativecommons.org/licenses/by-nc-nd/4.0/ )
Peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence.
model
Selectionforand
capacity
peer-reviewmanagement based onofdifferent
under responsibility costing
the scientific models
committee of (ABC and TDABC).
the 4th International A generic
Conference model has been
on System-Integrated
Intelligence.
Keywords: Additive
developed and it was used to analyze
manufacturing; idleProduction
3d printing; capacityPlanning
and to and
design strategies
Control towards optimization
(PPC); scheduling; the maximization of organization’s
value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity
Keywords: Additive manufacturing; 3d printing; Production Planning and Control (PPC); scheduling; optimization
optimization might hide operational inefficiency.
1. 2017
© Introduction
The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference
1. Introduction
The Additive Manufacturing (AM), also known as 3D printing, is a group of manufacturing technologies that
2017.
automatically applies material in layers until a three-dimensional object is created. Compared to traditional
The Additive
Keywords: Manufacturing
Cost Models; (AM), also
ABC; TDABC; Capacity knownIdle
Management; as Capacity;
3D printing, is a Efficiency
Operational group of
manufacturing technologies that
automatically applies material in layers until a three-dimensional object is created. Compared to traditional

*1.Corresponding
Introduction author. Tel.: +49-7121-271-4057
E-mail address: wjatscheslav.baumung@reutlingen-university.de
* The
Corresponding author.
cost of idle Tel.: +49-7121-271-4057
capacity is a fundamental information for companies and their management of extreme importance
E-mail address:
2351-9789 2018 Thewjatscheslav.baumung@reutlingen-university.de
©production Authors. Published by Elsevier
in modern systems. In general, it isLtd.
defined as unused capacity or production potential and can be measured
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
in several
Selection
ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity
2351-9789and peer-review
© 2018 under responsibility
The Authors. of the scientific
Published by Elsevier Ltd. committee of the 4th International Conference on System-Integrated Intelligence.
This is an open access article under the CC BY-NC-ND license741
* Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 (https://creativecommons.org/licenses/by-nc-nd/4.0/)
E-mailand
Selection address: psafonso@dps.uminho.pt
peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence.

2351-9789 © 2017 The Authors. Published by Elsevier B.V.

Peer-review
2351-9789 © under
2018responsibility
The Authors. of the scientificbycommittee
Published Elsevier of the Manufacturing Engineering Society International Conference 2017.
B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence.
10.1016/j.promfg.2018.06.035
 Wjatscheslav Baumung et al. / Procedia Manufacturing 24 (2018) 222–228 223
2 Wjatscheslav Baumung et al. / Procedia Manufacturing 00 (2018) 000–000

manufacturing, AM offers the advantage that parts can be produced directly and without special tools with a variety
of materials such as plastic, metal or ceramics [1]. The integration of AM into the existing production process promises
great advantages for component geometry and for the production of customer-specific products [2; 3]. With the use
of multiple AM machines, companies face new challenges. The manufacturing data for the production of a part must
be combined with scheduled operational process data, such as the working times of the machine operators. The
operational process data is part of the production system and can be found in production planning and control systems
(PPC). The core task of the PPC is the scheduling, capacity- and quantity-related production program planning, the
demand planning of production and assembly processes as well as the planning and control of external procurement
and in-house production [4]. Within the scope of capacity and schedule planning, the time schedule for planning and
coordination of orders is calculated on the basis of quantity planning, taking into account available capacities [5].
Planning and modifying the production orders, calculating their completion times and locations, and taking into
account machine and personnel availability are crucial in additive production in the areas of detailed scheduling and
sequence planning. Problems, breakdowns or unforeseen order situations can be taken into account dynamically and
immediately in planning with AM for all subsequent production orders without having to prepare specific tool parts,
as in traditional manufacturing. To produce cost-efficiently two- or three-dimensional nesting algorithms can be used
to optimize the space utilization of AM systems [6]. If components cannot be placed on top of each other due to
supporting structures, a two-dimensional nesting algorithm is applied. If a superimposed part placement is possible, a
three-dimensional algorithm is used [7]. While a comprehensive overview of the specific optimization problems has
already been done by e.g., Scheithauer in his book "Introduction to Cutting and Packing Optimization" [8], the
consideration of the maximum space utilization by means of nesting algorithms alone is not sufficient for an
economical AM production due to downtimes in the case of personnel absence. The first paper published on the topic
PPC and AM focuses on costs and highlights the need to plan additive manufacturing machines to reduce process
costs [9]. In contrast, this paper examines the working times of human resources professionals for scheduling additive
manufacturing processes in PPC. Accordingly, the aim of this work is to extend the earlier works on space utilization
optimization by the temporal aspect, whereby a synchronization with the shift plans of workers can be achieved.

2. Model for time-based planning of print jobs

For the industrial use of additive manufacturing plants, print jobs must be scheduled to be terminated and started
within staff working times to carry out withdrawal from and material provision to AM equipment. A solution may be
such, that instead of assigning individual print jobs with a certain number of objects to a printer, the workload of
individual printers is dynamically scaled according to the requirements and working hours of the operators. This
implies the capability of PPC system to distribute the objects to be printed among different printers.

2.1. Optimization model

Nesting algorithms enable industrial AM production architecture with multiple printers that can dynamically share
objects for print jobs. Models from different production orders are filtered according to their production parameters
and combined with each other for optimal utilization. Industrial use of additive production plants requires additional
data, such as the working hours of machine operators. For print jobs to be completed and started within working hours,
it is necessary to dynamically allocate the objects to be printed between available printers. This means that print jobs
are not permanently assigned to printers with a predefined number of objects, instead the corresponding objects are
grouped together according to the current demand and availability. The model (see Fig. 1) contains three triggers that
cause different process areas to start. These three triggers are associated with: 1) the sales order, 2) the change in
production resources, and 3) the availability of AM production machines.
224 Wjatscheslav Baumung
Wjatscheslav et al. et
Baumung / Procedia Manufacturing
al. / Procedia 00 (2018)
Manufacturing 000–000
24 (2018) 222–228 3

Fig. 1. Optimization model for time-based planning of print jobs

The first trigger (see Fig. 1 – Trigger 1) is an event in the customer order. The event can be a newly received sales
order, a change to an existing sales order, or the complete removal of a sales order. When a new sales order is received,
it is first added to the sales order list and a new process start is initiated. Subsequently, a grouping and sorting process
is triggered (see Fig. 1 - section group- and sorting (5)). This first reads the information from the PPC record such as
the 3D model, the number of pieces required from the parts list, the production time per model, the type of
manufacturing process and, if necessary, the special configuration for printing (see Fig. 1 - section data storage order
management (4)). The grouping is carried out according to the manufacturing process and the special configurations.
From this process stage onwards, each grouping runs through the process independently of the other groups. Within
the groupings, the models are sorted according to their completion date. Within this sorting, a further sorting is carried
out according to the printing time per model. The result is a grouped and sorted list of the models to be printed, which
are then copied to the temporary model list.
For the calculation of possible production times, additional data such as the available employee times is required
(see Fig. 1 - section data storage production resources (6)). These employee operating times include the time required
for removal, material supply or maintenance and are dependent on the number of production machines to be controlled
and the manufacturing technologies used. These activities must be defined and planned as variables on the basis of
their character and the machinery used. In addition, the buffer times as well as the maintenance times of the various
AM production facilities must be taken into account when calculating the available production time. Note that the
available production time represents the completion of a print job up to the next available employee working hours.
The buffer time is a defined period that is scheduled as a buffer to the completion of a print job. This calculation takes
forward and backward scheduling into account (see Fig. 1 - section determining the production time (7)). In forward
scheduling, the production time is calculated from the start of an individual work slot, while in backward scheduling
the production time is taken into account or counted back from the latest work slot to completion. Depending on the
duration and nature of the activity, several production facilities, sequentially or jointly, may carry out different types
of activities within a work time slot. To calculate the print jobs, all available production times must first be transmitted
(see Fig. 1 - section data storage print job management (9)). These represent the time span up to one or more working
hours, in the course of which work time slots can be skipped. In order to make the best possible economic use of
additive manufacturing facilities, the longest possible time frame up to a work time slot must be found (see Fig. 1 -
section create print jobs (8)). Consequently, the production time has to be calculated first, taking into account the
sorting and combinability of the individual models added per printer, at maximum space utilization. Based on the
maximum print time, the print time to the nearest possible work time slot must be reached. The combinations of
potential objects must be used to iterate and check with which models combinability is possible. To do this, a new
print job is first created for a grouping. The model with the longest printing time and the next completion date is then
selected on the basis of the sorting. The model is then checked to determine if it fits into the construction space and
further models can be added by means of the compatibility check according to the same sorting.
 Wjatscheslav Baumung et al. / Procedia Manufacturing 24 (2018) 222–228 225
4 Wjatscheslav Baumung et al. / Procedia Manufacturing 00 (2018) 000–000

In order to check the compatibility of the models with each other, it is verified whether the available printing time
is sufficient and if the space available for the respective models is sufficient. For this purpose, there are numerous
different algorithms which allow a 2-dimensional or 3-dimensional arrangement. The appropriate algorithm must be
selected depending on the manufacturing process. If the compatibility check algorithm fails, the model is skipped and
checked with the next model until the space is fully utilized within the production time. Afterwards, a machine code
is created by means of the model combination and the print configuration, which forms the print job. The exact
duration of the production process is read from this data. If this differs from the available production time, the next
shorter production period is targeted as already described. If the duration of the print job matches the production
period, it is saved as a temporary print job and the models are removed from the Temporary models stock. The process
then restarts for the next production period or printer configuration. If "AM Machine availability" is triggered (see
Fig. 1 – Trigger 3), the printer must use the first temporary print job and remove it from the list of "Temporary print
jobs" (see Fig. 1 - section data storage print job management (9)). The system checks again whether the planned print
start is within the defined buffer range. If this differs from a temporary print job, the entire job creation process must
be restarted and then the first temporary print job must be sent to the printer and removed from the list. Production is
complete after a print job has been completed and the parts removed (see Fig. 1 - section production (10)). The models
are then removed from the print job list and the grouped and sorted models stock (see Fig. 1 - section data storage
order management (11)). Either all models of the print job are removed completely or in case of a failed quality check
only the successful models are removed from the grouped and sorted models stock. In the event of a failed quality
check, the event "change of production resource" (see Fig. 1 – Trigger 2) is triggered after the models have been
removed. If all models for a sales order are printed, the sales order is removed. Following the completed print job, the
"maintenance" process follows, which includes all steps, depending on the printer type, until the printer is fully
operational again. The trigger "AM machine available" is then activated and the process is continued for the production
line using the temporary print jobs.

2.2. Implementation

The calculation requires information about the print jobs of the models, working times and available printers as
shown in Table 1. On the one hand, the information on available working and printing times allows employees to plan
their work, and on the other hand, they can carry out checks or maintenance of the printers at defined intervals. As
soon as a change occurs in the stored data, a trigger causes a new schedule.

Table 1. Required data for planning print jobs

Type Data Description
Name Model name with path
Count Number of models required
Priority Priority within a given time frame
Print orders Configuration All usable printer configurations
Material All usable materials for the order
Incoming date Date and time of order receipt
Completion date Date and time for latest completion
Name Worker's name or role
Working hours
Work slots All available working times, including start and end
Worker
time with date and time.
Name Printer name
Configuration Usable configurations for the printer
Printer Workspace Maximum usable installation space with specification
of X, Y and Z
All available working times, including start and end
Work slots
time with date and time.
 Wjatscheslav Baumung et al. / Procedia Manufacturing 00 (2018) 000–000 5
226 Wjatscheslav Baumung et al. / Procedia Manufacturing 24 (2018) 222–228

From the imported data, the available printing times are calculated on the basis of the operators working times and
the printing time per model. The calculation of the printing time per model is done by slicing with the CuraEngine by
the stored print configurations for each model. The print configurations is exported from Cura and used for planning
with a written converter. Slicing divides the model from the stereolithography (STL) file format into individual layers
and generates a machine code with printing time. The models are then sorted by priority and remaining time to
completion. The models were automatically filtered to the printers based on the print configurations stored for the
jobs. For each printer, models are then added based on the available print times until the available print time is reached.
As a result, in the positive case of the combinability check, a STL file with all models is generated. Finally, slicing is
performed via the newly generated STL file to check whether the available print time can be fulfilled in the given
combination.

3. Results

In order to check the functionality of the proposed optimization model, test objects were defined in two groups. The
first group had different geometric shapes for checking the functioning of the nesting algorithm (see Fig. 2 (a)) and
the second group for checking compliance with the specified print times. In the second grouping, all objects had the
same base area, but with different heights (see Fig. 2 (b)). For these simulations, two scenarios with expected results
were defined. In the first scenario, the available print time is sufficient to place all models in the build space, the
expected result is a scheduled print job that is finished before the end of production time and offers space for further
models. In the second scenario, the available printing time is not sufficient to fill the entire installation space. The
expected result is that on the basis of the parameters of the individual models, such as priority and completion date,
the order for the models was chosen and saved as finished print jobs, where the remaining printing time is not
sufficient for any further existing models.
The implementation was evaluated by test objects and a print job for a 42-part project with a specified order of
completion. All models required for the project with the number of pieces and the calculated printing time can be
found in the Appendix A.1. The test objects were used to check the functionality of the nesting algorithm and
compliance with print times. For the nesting algorithm, different geometric shapes were used and for maintaining
the print times, objects with the same base area and different heights were chosen. For the project, the sequence and
completion date for each model was determined on the basis of the bill of material (BOM) structure. For the
simulation, an operator with fixed working hours for the printers was defined (see Table 2) and two printers which
are available for the entire period. It was assumed that the planned removal time of the manufactured parts is always
thirty minutes per printer. The results of the planned orders can be seen in Table 3. The models associated with the
print jobs can be viewed in Appendix A.2. A complete schedule with forward scheduling could be calculated for
prints with job IDs 1, 2 and 3. Due to the workload, the work time slots can be skipped on Tuesday and Wednesday.
Forward scheduling was not possible for the job with ID 4, since the completion time exceeds the employee's
availability on Friday. Therefore, the implementation has switched to backward scheduling, which takes into
account all new models that arrive before the start date and can be added to the print job if applicable.

Fig. 2. (a) Geometric shapes for checking the nesting algorithm; (b) Models for testing compliance of defined print times
 Wjatscheslav Baumung et al. / Procedia Manufacturing 24 (2018) 222–228 227
6 Wjatscheslav Baumung et al. / Procedia Manufacturing 00 (2018) 000–000

Different filter configurations for infill settings and colors are taken into account by the implementation for the
planning, but to provide a better overview, a uniform infill setting and color was selected for all objects. As an
alignment in the diagonal resulted in poor results with linear parts, the nesting algorithm was specified to be 90° for
possible rotations. The reduction of the degree allows for more rotations and thus an increase in space utilization,
but also for a longer calculation time of the algorithm. The tests showed that Fused Deposition Modeling (FDM)
printers are prone to errors when using the maximum print area. During the maintenance of the printers, test
printouts were used in addition to the replacement of the spare parts to determine the maximum usable print area and
redefined in the table for printer data.

Table 2. Staff available working time for printers Table 3. Planed and scheduled print jobs

Weekday Time Job-ID: 1 2 3 4

Monday 08:00 – 09:00 Printer-ID: 1 2 1 2
Tuesday 08:00 – 09:00 Start: Monday Monday Thursday Saturday
Wednesday 08:00 – 09:00 08:30:00 09:00:00 10:30:00 09:39:29

Thursday 10:00 – 11:00 Finish: Thursday Thursday Friday Monday

09:19:59 09:14:03 12:18:10 07:30:00
Friday 13:00 – 14:00
Sliced 72:49:59 h 72:14:03 h 25:48:10 h 45:50:31 h
Saturday - print time:
Sunday - Time of Thursday Thursday Friday Monday
Monday 08:00 – 09:00 removal: 10:00 – 10:30 10:30 – 11:00 13:00 – 13:30 08:00 – 08:30

4. Conclusion

This paper presents and analyses an optimization model for the planning of AM systems using nesting algorithms
for time-oriented work space utilization. Production-relevant variables for the production planning and control of
additive manufacturing machines are identified and tested experimentally during implementation. The maximum use
of space was combined with the focus on synchronizing workers shift schedules. The optimization model was
divided into the areas grouping and sorting, calculation of production times and creation of print jobs. The
calculated production time takes into account the sorting and combinability of the individual parts by applying an
nesting algorithm for maximum space utilization while observing the specified production time. Certain triggers
represent the external influencing factors and cause either a partial area or the entire process to be executed. With
the presented evaluation example, the feasibility of the optimization model was demonstrated, in which the
downtimes were minimized by a time-oriented space utilization. In the test printouts it has been noticed that the
calculated printing time does not exactly correspond to the actual printing time. This problem was solved by
adjusting the buffer time. However, further research is required for accurate planning to ensure that the calculated
print time corresponds to the real one. Further research could take into account the material stock on the AM
machines in the optimization model and use it for selecting the operator slot.

Appendix A. Project parts and planned jobs

A.1. Project parts

Table 4. Required components for the project with quantity and printing time per part

Quantity Name Calculated print time per model

4 IE_Foot_Bottom.stl 04:27:00
2 Bottom_CornerF2.stl 04:29:54
2 BottomM_CornerF2.stl 04:27:03
2 Lock_CornerF2.stl 02:53:22
228 Wjatscheslav Baumung et al. / Procedia Manufacturing 24 (2018) 222–228
Wjatscheslav Baumung et al. / Procedia Manufacturing 00 (2018) 000–000 7

2 LockM_CornerF2.stl 02:54:05
2 F-Roller.stl 11:19:56
2 F-RollerM.stl 11:17:43
4 F-RollerPlate.stl 00:30:17
4 Spacer_CornerF2.stl 00:39:57
2 Top_CornerF2.stl 03:50:38
2 TopM_CornerF2.stl 03:52:18
2 F-Nut_Trap.stl 02:22:20
1 F-ToolMount.stl 08:55:49
1 F-Z-Lower.stl 04:52:16
1 F-Z-Motor.stl 04:38:59
2 F-XYZ_T8.stl 09:42:18
2 F-XY.stl 22:55:59
1 F-Spacer.stl 01:29:19
4 F-RollerMount.stl 06:25:09

A.2. Planned jobs

Table 5. Parts with quantity to the planned jobs

Job-ID Parts with Quantity
1 F-RollerM.stl * 2; F-RollerPlate.stl * 1; Spacer_CornerF2.stl * 4; Top_CornerF2.stl * 2; TopM_CornerF2.stl * 2
F-Nut_Trap.stl * 2; F-ToolMount.stl * 1; F-Z-Lower.stl * 1; F-Z-Motor.stl * 1; F-Spacer.stl * 1; F-RollerMount.stl * 1
2 IE_Foot_Bottom.stl * 4; Bottom_CornerF2.stl * 2; BottomM_CornerF2.stl * 2; Lock_CornerF2.stl * 2
LockM_CornerF2.stl * 2; F-Roller.stl * 2; F-RollerPlate.stl * 3
3 F-XYZ_T8.stl * 2; F-RollerMount.stl * 1
4 F-XY.stl * 2

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