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Physical modeling of material flows in cyber-physical production systems
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Procedia Manufacturing 28 (2019) 10–17 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
International Conference on Changeable, Agile, Reconfigurable and Virtual Production International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Physical modeling of material flows in cyber-physical production Physical modeling of material flows in cyber-physical production systems Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June systems 2017, Vigo (Pontevedra), Spain Moritz Glatta,a,*, Jan C. Aurichaa Moritz Glatt *, Jan C. Aurich
for Manufacturing Technology and Production Systems (FBK), P.O. 3049 Kaiserslautern, CostingInstitute models for capacity optimization in Box Industry 4.0:Germany Trade-off Institute for Manufacturing Technology and Production Systems (FBK), P.O. Box 3049 Kaiserslautern, Germany between used capacity and operational efficiency a a
A. Santana , P. Afonso , A. Zanin , R. Wernke Abstract Abstract a University of Minho, 4800-058 Guimarães, Portugal Cyber-physical production systems that produce customized products usually show complex material flows. Furthermore – due b Unochapecó, 89809-000 Chapecó, SC, Brazil to customizationproduction – work pieces thatthat areproduce transported within these production systems strongly vary in termsFurthermore of their physical Cyber-physical systems customized products usually show complex material flows. – due properties. In combination, this increases the probability of these physically induced material flow disturbances, a work piece to customization – work pieces that are transported within production systems strongly vary in termse.g. of their physical falling off aInconveyor belt. Inthis case of damaged work piecesoforphysically equipment,induced the occurrence thesedisturbances, events may result increased properties. combination, increases the probability materialofflow e.g. a in work piece delivery and costs. material floworsimulations to predict physically induced falling offtimes a conveyor belt.However, In case oftraditional damaged work pieces equipment, are the unable occurrence of these events may resultdisturbances, in increased Abstract since their discrete-event focuses on chronological aspects are of the material flow. physically induced disturbances, delivery times and costs. approach However,solely traditional material flow simulations unable to predict In consequence, this paper presents solely the4.0", development a computer-based to simulate physical aspects of material flows by since their focuses on of chronological aspects ofbethepushed material flow. Under thediscrete-event concept of approach "Industry production processes willmodel to be increasingly interconnected, using a physics engine. In the first step, the requirements for this simulation model are determined. This includes In consequence, this paper presents the development of a computer-based model to simulate physical aspects of material flows the by information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization identification andengine. definition phenomena that influence typical materialmodel flow are processes. Furthermore, a suitable using a physics In of the physical first step, the requirements for this simulation determined. This includes the goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. physics engineand is selected, thatofmatches requirements andinfluence is able totypical simulate the relevant phenomena. Subsequently, identification definition physicalthephenomena that material flow physical processes. Furthermore, a suitable Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of required input data and parameters are defined. After the theoretical developed, the modelphenomena. is implemented using the physics engine is selected, that matches the requirements and is ableframework to simulateisthe relevant physical Subsequently, maximization. The study of capacity optimization and costing models is an important research topic thatdetermines deserves selected engine. Finally, thearemodel is validated using an framework exemplary material flow the process. The validation required physics input data and parameters defined. After theby theoretical is developed, model is implemented using the contributions both the practical andis theoretical perspectives. This paper and discusses a mathematical if the simulation model isFinally, capable of simulating physical by aspects flows withpresents a flow suitable degree of reality. selected physicsfrom engine. the model validated usinginanmaterial exemplary material process. The validation determines model for capacity management on different costinginmodels and aTDABC). A generic model has been if the simulation model is capable of based simulating physical aspects material(ABC flows with suitable degree of reality. developed it wasPublished used to analyze idleB.V. capacity and to design strategies towards the maximization of organization’s © 2019 Theand Authors. by Elsevier © 2019 2019 The Authors. by Elsevier B.V. value. The trade-off capacity efficiency is highlighted and it is shown that capacity This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © The Authors. Published bymaximization Elsevier B.V. vs operational 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 committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) International Conference on Changeable, Agile, Reconfigurable This is an open access article under thescientific CC BY-NC-ND license optimization might hide operational inefficiency. Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Peer-review under responsibility of the scientific © 2017 TheProduction. Authors. Published by Elsevier B.V.committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Production. and Virtual Production. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference Keywords: Material flow; physical simulation; physics engine; cyber-physical production systems 2017. a
a,*
b
b
Keywords: Material flow; physical simulation; physics engine; cyber-physical production systems Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
Introduction *1.Corresponding author. Tel.: +49-631-205-3722; fax +49-631-205-3304. address:author. [email protected] * E-mail Corresponding Tel.: +49-631-205-3722; fax +49-631-205-3304. E-mail address: [email protected] The cost of idle capacity is a fundamental information for companies and their management of extreme importance 2351-9789 2019 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2351-9789 © 2019 Thearticle Authors. Published by Elsevier B.V. in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual This is an open access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 Production. Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual E-mail address: [email protected] Production. 2351-9789 Published by Elsevier B.V. B.V. 2351-9789 ©©2017 2019The TheAuthors. Authors. Published by Elsevier Peer-review underaccess responsibility of the scientific committee oflicense the Manufacturing Engineering Society International Conference 2017. This is an open article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Production. 10.1016/j.promfg.2018.12.003
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1. Material flows and simulation in cyber-physical production systems Recent advances in manufacturing and computer science, as well as in information and communication technologies have promoted the emergence of cyber-physical production systems (CPPS). Elements of a CPPS like machines or material handling systems are linked within and across all levels of production and contain embedded systems that record and affect real processes with sensors and actuators. The elements of a CPPS are therefore able to acquire information from their surroundings and to act autonomously. Furthermore, these elements interact both with the physical and the digital world and are connected with each other and in global networks [1]. Additionally, elements of a CPPS establish mutual connections for the purpose of autonomous cooperation and collaboration. In consequence, CPPS provide a strong responsiveness towards internal and external changes [2]. These attributes enable the highly flexible and reconfigurable large series production of small batches with the economic efficiency of an automated production [3]. By implementing CPPS, manufacturers aim at coping with the increasing demand for customized products [4]. Despite these potential advantages, CPPS entail an increased degree of complexity compared to traditional production systems [2]. One element that is largely affected by this complexity is the physical material flow. Customization strongly increases the number of variants within the production system, which generally complicates material flows [5]. In addition, two characteristics of CPPS further complicate material flows: Variance of physical work piece properties: Due to increased variance, work pieces that flow through a CPPS may show a vast span of different physical attributes like mass, surface roughness or center of gravity. Therefore, the physical behavior of these work pieces resulting from applying forces during material flow processes varies as well. Variance of paths: Flexibility as a second essential aspect of CPPS adds to that issue: In CPPS, redundant machines negotiate with products and among each other in order to self-organize the production of varying products [6]. This leads to various possible routes that work pieces take during their way through the CPPS (see Fig. 1). On these different routes, the work pieces might show different physical behavior due to different aspects of the factory layout (e.g. ramps or curves).
Fig. 1: Varying material flow routes in CPPS (M=machine tool) (after [6])
This complexity due to the individuality of material flows makes it difficult to predict disturbances that are caused by the physical interaction between work pieces and the respective material handling systems. One example of these disturbances is a work piece that shifts on a braking automated guided vehicle (AGV). Currently, material flows are mainly simulated with discrete-event simulation. This enables the simulation of chronological aspects, but does not allow the prediction of the described disturbances. The latter requires simulation models that are able to describe the physical interaction of rigid bodies. Furthermore, simulation bears potential to improve the operation of CPPS due to the mapping of real factory processes into the digital world, often referred to as “digital factory”. Based on real-time feedback from production,
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the digital factory enables proactive and short-term decision support, using on-line simulation [2]. The economic applicability of on-line simulation is further supported by the continuously decreasing cost of computing power [7]. Simulation requires reliable models, therefore modeling operation and emerging behavior of CPPS elements is a crucial research task [2]. Previous work pointed out that using physical simulation during the operation of a production system could improve lead times [8] and reduce downtimes [9]. In consequence, this paper describes the development of a model that is able to simulate physical aspects of material flows during the operation of a CPPS in order to provide short-term decision support. 2. State of the Art Existing literature presents a number of applications of physical simulation in manufacturing engineering. A major application field is the work on the utilization of physical simulation for virtual commissioning. Zäh et al. [9], as well as Reinhart and Lacour [10] present approaches that simulate physical attributes of material flow intensive production plants for the purpose of virtual commissioning. Hoher et al. as well focus on virtual commissioning and create a hybrid physical material flow model [11]. Besides virtual commissioning, physical simulation is used predominantly for assembly planning. The approach of Strahilov et al. regards automated assembly systems in the automotive industry [12]. Winkes and Aurich integrate physical simulation and virtual reality for the planning of manual assembly systems [13]. Furthermore, physical simulation is used for the planning of engineering changes in production systems [14]. All of the presented approaches use physical simulation for planning purposes in manufacturing and some simulate material flows. However, none of the approaches focuses on the operation phase of production systems. Furthermore, the described material flow models do not consider production systems with customized products and varying material flow paths. Due to this, Section 3 presents the development of a simulation model that overcomes these deficits. 3. Development of physical simulation model The envisioned simulation model needs to be able to realistically simulate relevant physical phenomena that occur during material flow processes within a CPPS. In this context, the term “physical” represents rigid body dynamics. The development of the simulation model is oriented on the standard VDI 3633 “Simulation of systems in materials handling, logistics and production” [15]. The physical behavior of individual material flow processes could as well be solved analytically with methods from engineering mechanics. However, as elaborated in Section 1, CPPS entail a big number of individual material flows, each of them with characteristic and changing physical attributes. Describing each of these processes analytically would require time-intensive modeling and solving of a big number of individual equations, which is not suitable for short-term decision-making in highly variant production environments. These circumstances make the physical aspects of material flows in CPPS problem worth simulating. 3.1. Task definition and target system Based on the description of material flows and the role of simulation in CPPS in Section 1, the simulation model has to meet the following requirements: Physics representation: Relevant physical phenomena that occur during typical material flow processes need to be represented realistically. Computation: The model must allow on-line simulation for short-term evaluation of material flow strategies, which leads to the requirement of real-time simulation. Since CPPS entail a big number of different material flow processes, the simulation must further be executable with an economic amount of computing power.
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Flexibility: The model must be able to cope with the high number of variants in a CPPS. In consequence, the modelling effort to integrate work pieces with varying physical properties into the simulation model must be relatively low. Adaption to factory infrastructure: The model must be flexibly applicable to different and changing material handling systems. Furthermore, physical characteristics of respective analyzed factory layouts need to be considered. 3.2. System analysis To define the system that is to be simulated, a bottom-up approach is chosen. This is contrary to most traditional approaches that focus on the overall material flow system and do not consider physical attributes. High-level approaches of this kind are suitable for simulating organizational and chronological aspects of the material flows. However, physical phenomena in material flows occur on the operational level of material flows. This necessitates a bottom-up approach that considers elementary physical material flow processes with a high level of detail. In this context, material flow can be seen as the movement of discrete objects on transport ways or conveyors in steady and unsteady time intervals [16]. The scope of the proposed model therefore focuses on the actual transport processes and excludes processes like storing or commissioning. In consequence, the individual material flow processes contain two kinds of physical elements: Building blocks: Active elements that perform transport processes, like conveyors or vehicles. Production planning and control define their operating mode. Objects: Passive elements that are being transported, like finished products or work pieces. Their behavior is defined by the respective building block. The sequence structure of the material flow processes is determined by several parameters such as the type of building blocks, path or values for velocities and acceleration. Based on the system analysis, a formalized model is developed. Simulation Model
CPPS
Production order
Production control
Factory layout
Material handling systems
Geometric data of work pieces Surface roughnesses
Scheduled Material flows
Objects: Physical and geometrical data of work pieces
Physical simulation
Paths of material flow processes Building blocks: Physical models of material handling systems
Physical aspects
Physical properties
FBK/038-005
Fig. 2: Conceptual model
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3.3. Model formalization Fig. 2 depicts the formalized simulation model. The core of the model, the physical simulation, incorporates three sub-elements: Building blocks, objects and individual paths of material flow processes. All these elements process data from the CPPS. Data about physical properties of real material handling systems are modeled in the building blocks. The objects incorporate geometric data and physical attributes like surface roughness from the respective production orders. The actual paths that objects take during material flow are obtained from production orders and production control. Production control further allocates objects to specific material handling systems. Furthermore, the factory layout defines the topology of the paths. 3.4. Implementation Based on the conceptual model, an executable software simulation model is implemented (see Fig. 3). For physical simulation of rigid bodies that meets the computation requirement in Section 3.1, a physics engine was selected. Physics engines are software tools that simulate movements of a system as a result of forces acting on that system [17]. Even though physics engines provide less accuracy than finite element analysis (FEA), today’s industrial standard for solving engineering problems [18], they are able to simulate physical behavior of rigid bodies in real-time. Therefore, physics engines match the requirements and fulfil the general criterion, that modelling accuracy should not be as detailed as possible, but as detailed as necessary to fulfil the given targets [15].
CAD
STL URDF
Physical attributes
pyBullet
Parameters FBK/038-006
Fig. 3: Implementation work flow
In a qualitative evaluation of several physics engines, Boeing et al. identified Bullet Physics [19] as the best performing engine among non-commercial physics engines [17]. Consequently, Bullet was chosen for the implementation. As seen in Fig. 2, the inclusion of objects and building blocks into the physical simulation is an essential step. Therefore, 3D computer-aided design (CAD) files of objects and building blocks are converted to into the STL file format. This includes the tessellation, a method that simplifies the geometries by converting their surfaces into triangular meshes [20]. STL conversion is featured in most commercial CAD applications and therefore serves as a universal interface. Afterwards, this geometrical data is imported to Universal Robot Description Format (URDF) files, where they are enriched with physical attributes. URDF is mainly used to model robots, therefore it further allows to connect distinct geometric models with joints, using certain constraints [21]. In terms of computation power, it is recommended to model the building blocks as simple as possible. However, it is essential, that functional surfaces that are likely to affect the physical behavior of the material flow are represented accurately. The physical attributes implemented include mass, friction coefficients and inertia tensors. The latter one can usually be derived in CAD software. The result of this second step are URDF files that serve as geometric and physical model of both building blocks and objects. Finally, the created URDF files are loaded into a scene in pyBullet, a wrapper that allows to access Bullet Physics using the Python programming language. In the respective scene, general parameters like gravitation are defined. Afterwards, the physical behavior of objects and building blocks can be simulated in various scenarios, as seen in Section 3.5.
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3.5. Example In order to demonstrate the implementation work flow, a material flow process that involves an AGV was modeled and simulated. AGVs are driverless vehicles that are independently addressable. They are used for material handling in a factory, allowing flexible routes and adaption to different kinds of transported work pieces [22]. Due to their flexibility, AGVs are seen as one of the enabling technologies for CPPS [23]. Based on CAD files, the AGV was included in a URDF file. The bed of the AGV was modeled as the functional surface with a friction coefficient. As an object, a simple block was modeled to represent the transported work piece. In the scene, a rapid braking of the AGV is simulated. The modeled deceleration was 1 m/s² which is a typical figure in industry [24]. Fig. 4 shows that the object shifts off the AGV and falls to the factory floor as a result of the sudden braking. In a real CPPS, this behavior could potentially harm employees or cause damages. Therefore, parameters of the transport process can be changed according to the results of the simulation. Furthermore, the example simulation fulfilled the requirement for real-time capability. Being simulated at 60 frames per second (16.67 ms/frame) with an Intel Core [email protected] CPU, the median calculation time per frame was 1.18 ms and the maximum calculation time per frame was 8.29 ms. Consequently, the calculation time of every frame was lower than its runtime. In consequence, this example shows how physical simulation can support short-term decision support (see Section 1).
Fig. 4: Visualization of the simulation process in Bullet Physics (a) Regular movement of AGV; (b) Rapid braking of AGV, work piece shifts on load bed; (c) Work piece shifts off load bed; d) AGV stands still, work piece lies on the floor
4. Summary and Outlook CPPS show material flow processes with varying physical properties. In order to predict the physical behavior of these material flows, a physical simulation model was developed. According to the defined requirements, the model is able to process input data that is commonly available in CPPS and can perform simulations in real-time. The objects in the simulation model are generated based on CAD data that is easily accessible in CPPS. Therefore, the comparably low number of distinct material handling systems in a CPPS are the only elements of the simulation model, which require manual modeling. The example showed that the model enabled the visual representation of a
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real transport process using an AGV. However, the described model needs to be improved to be able to fulfil all of the listed requirements. Particularly, the described model requires validation. Primarily, this includes operational validation, to determine whether the output of the simulation provides sufficient accuracy in terms of the models intended purpose. In this case, predictive validation seems reasonable, which means forecasting the behavior of a material flow process via simulation and then comparing the results with the experimental behavior of real processes [25]. Furthermore, the complexity of representable material flows needs to be increased. The described example showed the path of an AGV on a straight surface. In real CPPS scenarios, the paths show a higher degree of complexity. Further improvements have to focus on the representation of physical attributes of the factory layout. Another challenge is imposed by the acquisition of data from CPPS. The input data of the simulation model is acquired via universal file formats, which serve as interfaces. However, the process of data acquisition via the connection to information systems of a CPPS like manufacturing execution systems (MES) has to be described in detail. Finally, the usability of the approach has to be increased. Currently, to perform the described implementation work flow, a series of different software has to be used manually. Since the performed steps are relatively recurring, they seem suitable for the integration into an application that provides a graphical user interface (GUI). Acknowledgement This research was funded by the German research foundation (DFG) within the IRTG 2057 “Physical Modeling for Virtual Manufacturing Systems and Processes”. References [1] acatech - National Academy of Science and Engineering, 2011. Cyber-Physical Systems: Driving force for innovation in mobility, health, energy and production, Berlin, Heidelberg. Springer, 2011. [2] Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S. et al. Cyber-physical systems in manufacturing. CIRP Annals 65, 2016, pp. 621–641. [3] Saldivar, A.A.F., Li, Y., Chen, W.-n., Zhan, Z.-h. et al., 2015. Industry 4.0 with cyber-physical integration: A design and manufacture perspective, in 2015 21st International Conference on Automation and Computing (ICAC), IEEE / Institute of Electrical and Electronics Engineers Incorporated, pp. 1–6. [4] Monostori, L. Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP 17, 2014, pp. 9–13. [5] Lödding, H. Handbook of manufacturing control: Fundamentals, description, configuration, Heidelberg. Springer, 2013. [6] Wang, S., Wan, J., Di Li, Zhang, C. Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks 12, 2016, pp. 1–10. [7] Sandler, R.L. Ethics and Emerging Technologies, Basingstoke. Palgrave Macmillan, 2013. [8] Glatt, M., Aurich, J.C. Physical simulation of cyber-physical production systems (Physiksimulation cyber-physischer Produktionssysteme): Planning and control of cyber-physical production systems using physical simulation (Planung und Steuerung cyber-physischer Produktionssysteme durch physikalische Simulation). wt Werkstatttechnik online 108, 2018, pp. 217–220. [9] Zäh, M.F., Spitzweg, M., Lacour, F.-F. Application of a Physical Model for the Simulation of the Material Flow of a Manufacturing Plant (Einsatz eines Physikmodells zur Simulation des Materialflusses einer Produktionsanlage). it - Information Technology 50, 2008, pp. 192– 198. [10] Reinhart, G., Lacour, F.-F., 2009. Physically based Virtual Commissioning of Material Flow Intensive Manufacturing Plants, in 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2009), Herbert Utz Verlag, München, pp. 377–386. [11] Hoher, S., Schindler, P., Göttlich, S., Schleper, V. et al., 2012. System Dynamic Models and Real-time Simulation of Complex Material Flow Systems, in Enabling Manufacturing Competitiveness and Economic Sustainability: Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Montreal, Canada, 2-5 October 2011, Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, pp. 316–321. [12] Strahilov, A., Ovtcharova, H.C.J., Bar, T., 2012. Development of the physics-based assembly system model for the mechatronic validation of automated assembly systems, in Proceedings of the 2012 Winter Simulation Conference (WSC), IEEE, pp. 1–11. [13] Winkes, P.A., Aurich, J.C. Method for an Enhanced Assembly Planning Process with Systematic Virtual Reality Inclusion. Procedia CIRP 37, 2015, pp. 152–157.
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[14] Cichos, D., Aurich, J.C. Integration of a Physical Model into the Realization of Engineering Changes in Manufacturing Systems. Applied Mechanics and Materials 869, 2017, pp. 159–166. [15] Association of German Engineers (Verein Deutscher Ingenieure e.V). Simulation of systems in materials handling, logistics and production Fundamentals, Berlin. Beuth Verlag 03.100.10, 2014 (VDI 3633 Sheet 1). http://www.vdi.eu/nc/guidelines/vdi_3633_blatt_1simulation_von_logistik_materialfluss_und_produktionssystemen_grundlagen_/. Accessed 9 April 2018. [16] Arnold, D., Furmans, K. Materialfluss in Logistiksystemen, 6th edn., Berlin, Heidelberg. Springer-Verlag Berlin Heidelberg, 2009. [17] Boeing, A., Bräunl, T., 2007. Evaluation of real-time physics simulation systems, in Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, ACM, New York, NY, pp. 281. [18] Rao, S.S. The finite element method in engineering, Oxford. Butterworth-Heinemann, 2017. [19] Bullet Real-Time Physics. https://pybullet.org/wordpress/. Accessed 9 April 2018. [20] Qiu, Z.M., Wong, Y.S., Fuh, J.Y.H., Chen, Y.P. et al. Geometric model simplification for distributed CAD. Computer-Aided Design 36, 2004, pp. 809–819. [21] Lentin, J. Learning robotics using Python: Design, simulate, program, and prototype an interactive autonomous mobile robot from scratch with the help of Python, ROS, and Open-CV!, Birmingham, UK. Packt Publishing, 2015. [22] Ali, M., Khan, W.U. Implementation Issues of AGVs in Flexible Manufacturing System: A Review. Global Journal of Flexible Systems Management 11, 2010, pp. 55–61. [23] Wan, J., Cai, H., Zhou, K., 2015. Industrie 4.0: Enabling technologies, in Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, IEEE, pp. 135–140. [24] Boehning, M., 2014. Improving safety and efficiency of AGVs at warehouse black spots, in 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, pp. 245–249. [25] Sargent, R.G., 2012. Verification and validation of simulation models, in Proceedings of the 2012 Winter Simulation Conference (WSC), IEEE, pp. 162–176.
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International Conference on Changeable, Agile, Reconfigurable and Virtual Production International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Physical modeling of material flows in cyber-physical production Physical modeling of material flows in cyber-physical production systems Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June systems 2017, Vigo (Pontevedra), Spain Moritz Glatta,a,*, Jan C. Aurichaa Moritz Glatt *, Jan C. Aurich
for Manufacturing Technology and Production Systems (FBK), P.O. 3049 Kaiserslautern, CostingInstitute models for capacity optimization in Box Industry 4.0:Germany Trade-off Institute for Manufacturing Technology and Production Systems (FBK), P.O. Box 3049 Kaiserslautern, Germany between used capacity and operational efficiency a a
A. Santana , P. Afonso , A. Zanin , R. Wernke Abstract Abstract a University of Minho, 4800-058 Guimarães, Portugal Cyber-physical production systems that produce customized products usually show complex material flows. Furthermore – due b Unochapecó, 89809-000 Chapecó, SC, Brazil to customizationproduction – work pieces thatthat areproduce transported within these production systems strongly vary in termsFurthermore of their physical Cyber-physical systems customized products usually show complex material flows. – due properties. In combination, this increases the probability of these physically induced material flow disturbances, a work piece to customization – work pieces that are transported within production systems strongly vary in termse.g. of their physical falling off aInconveyor belt. Inthis case of damaged work piecesoforphysically equipment,induced the occurrence thesedisturbances, events may result increased properties. combination, increases the probability materialofflow e.g. a in work piece delivery and costs. material floworsimulations to predict physically induced falling offtimes a conveyor belt.However, In case oftraditional damaged work pieces equipment, are the unable occurrence of these events may resultdisturbances, in increased Abstract since their discrete-event focuses on chronological aspects are of the material flow. physically induced disturbances, delivery times and costs. approach However,solely traditional material flow simulations unable to predict In consequence, this paper presents solely the4.0", development a computer-based to simulate physical aspects of material flows by since their focuses on of chronological aspects ofbethepushed material flow. Under thediscrete-event concept of approach "Industry production processes willmodel to be increasingly interconnected, using a physics engine. In the first step, the requirements for this simulation model are determined. This includes In consequence, this paper presents the development of a computer-based model to simulate physical aspects of material flows the by information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization identification andengine. definition phenomena that influence typical materialmodel flow are processes. Furthermore, a suitable using a physics In of the physical first step, the requirements for this simulation determined. This includes the goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. physics engineand is selected, thatofmatches requirements andinfluence is able totypical simulate the relevant phenomena. Subsequently, identification definition physicalthephenomena that material flow physical processes. Furthermore, a suitable Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of required input data and parameters are defined. After the theoretical developed, the modelphenomena. is implemented using the physics engine is selected, that matches the requirements and is ableframework to simulateisthe relevant physical Subsequently, maximization. The study of capacity optimization and costing models is an important research topic thatdetermines deserves selected engine. Finally, thearemodel is validated using an framework exemplary material flow the process. The validation required physics input data and parameters defined. After theby theoretical is developed, model is implemented using the contributions both the practical andis theoretical perspectives. This paper and discusses a mathematical if the simulation model isFinally, capable of simulating physical by aspects flows withpresents a flow suitable degree of reality. selected physicsfrom engine. the model validated usinginanmaterial exemplary material process. The validation determines model for capacity management on different costinginmodels and aTDABC). A generic model has been if the simulation model is capable of based simulating physical aspects material(ABC flows with suitable degree of reality. developed it wasPublished used to analyze idleB.V. capacity and to design strategies towards the maximization of organization’s © 2019 Theand Authors. by Elsevier © 2019 2019 The Authors. by Elsevier B.V. value. The trade-off capacity efficiency is highlighted and it is shown that capacity This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © The Authors. Published bymaximization Elsevier B.V. vs operational 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 committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) International Conference on Changeable, Agile, Reconfigurable This is an open access article under thescientific CC BY-NC-ND license optimization might hide operational inefficiency. Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Peer-review under responsibility of the scientific © 2017 TheProduction. Authors. Published by Elsevier B.V.committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Production. and Virtual Production. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference Keywords: Material flow; physical simulation; physics engine; cyber-physical production systems 2017. a
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Keywords: Material flow; physical simulation; physics engine; cyber-physical production systems Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
Introduction *1.Corresponding author. Tel.: +49-631-205-3722; fax +49-631-205-3304. address:author. [email protected] * E-mail Corresponding Tel.: +49-631-205-3722; fax +49-631-205-3304. E-mail address: [email protected] The cost of idle capacity is a fundamental information for companies and their management of extreme importance 2351-9789 2019 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2351-9789 © 2019 Thearticle Authors. Published by Elsevier B.V. in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual This is an open access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 Production. Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual E-mail address: [email protected] Production. 2351-9789 Published by Elsevier B.V. B.V. 2351-9789 ©©2017 2019The TheAuthors. Authors. Published by Elsevier Peer-review underaccess responsibility of the scientific committee oflicense the Manufacturing Engineering Society International Conference 2017. This is an open article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual Production. 10.1016/j.promfg.2018.12.003
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1. Material flows and simulation in cyber-physical production systems Recent advances in manufacturing and computer science, as well as in information and communication technologies have promoted the emergence of cyber-physical production systems (CPPS). Elements of a CPPS like machines or material handling systems are linked within and across all levels of production and contain embedded systems that record and affect real processes with sensors and actuators. The elements of a CPPS are therefore able to acquire information from their surroundings and to act autonomously. Furthermore, these elements interact both with the physical and the digital world and are connected with each other and in global networks [1]. Additionally, elements of a CPPS establish mutual connections for the purpose of autonomous cooperation and collaboration. In consequence, CPPS provide a strong responsiveness towards internal and external changes [2]. These attributes enable the highly flexible and reconfigurable large series production of small batches with the economic efficiency of an automated production [3]. By implementing CPPS, manufacturers aim at coping with the increasing demand for customized products [4]. Despite these potential advantages, CPPS entail an increased degree of complexity compared to traditional production systems [2]. One element that is largely affected by this complexity is the physical material flow. Customization strongly increases the number of variants within the production system, which generally complicates material flows [5]. In addition, two characteristics of CPPS further complicate material flows: Variance of physical work piece properties: Due to increased variance, work pieces that flow through a CPPS may show a vast span of different physical attributes like mass, surface roughness or center of gravity. Therefore, the physical behavior of these work pieces resulting from applying forces during material flow processes varies as well. Variance of paths: Flexibility as a second essential aspect of CPPS adds to that issue: In CPPS, redundant machines negotiate with products and among each other in order to self-organize the production of varying products [6]. This leads to various possible routes that work pieces take during their way through the CPPS (see Fig. 1). On these different routes, the work pieces might show different physical behavior due to different aspects of the factory layout (e.g. ramps or curves).
Fig. 1: Varying material flow routes in CPPS (M=machine tool) (after [6])
This complexity due to the individuality of material flows makes it difficult to predict disturbances that are caused by the physical interaction between work pieces and the respective material handling systems. One example of these disturbances is a work piece that shifts on a braking automated guided vehicle (AGV). Currently, material flows are mainly simulated with discrete-event simulation. This enables the simulation of chronological aspects, but does not allow the prediction of the described disturbances. The latter requires simulation models that are able to describe the physical interaction of rigid bodies. Furthermore, simulation bears potential to improve the operation of CPPS due to the mapping of real factory processes into the digital world, often referred to as “digital factory”. Based on real-time feedback from production,
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the digital factory enables proactive and short-term decision support, using on-line simulation [2]. The economic applicability of on-line simulation is further supported by the continuously decreasing cost of computing power [7]. Simulation requires reliable models, therefore modeling operation and emerging behavior of CPPS elements is a crucial research task [2]. Previous work pointed out that using physical simulation during the operation of a production system could improve lead times [8] and reduce downtimes [9]. In consequence, this paper describes the development of a model that is able to simulate physical aspects of material flows during the operation of a CPPS in order to provide short-term decision support. 2. State of the Art Existing literature presents a number of applications of physical simulation in manufacturing engineering. A major application field is the work on the utilization of physical simulation for virtual commissioning. Zäh et al. [9], as well as Reinhart and Lacour [10] present approaches that simulate physical attributes of material flow intensive production plants for the purpose of virtual commissioning. Hoher et al. as well focus on virtual commissioning and create a hybrid physical material flow model [11]. Besides virtual commissioning, physical simulation is used predominantly for assembly planning. The approach of Strahilov et al. regards automated assembly systems in the automotive industry [12]. Winkes and Aurich integrate physical simulation and virtual reality for the planning of manual assembly systems [13]. Furthermore, physical simulation is used for the planning of engineering changes in production systems [14]. All of the presented approaches use physical simulation for planning purposes in manufacturing and some simulate material flows. However, none of the approaches focuses on the operation phase of production systems. Furthermore, the described material flow models do not consider production systems with customized products and varying material flow paths. Due to this, Section 3 presents the development of a simulation model that overcomes these deficits. 3. Development of physical simulation model The envisioned simulation model needs to be able to realistically simulate relevant physical phenomena that occur during material flow processes within a CPPS. In this context, the term “physical” represents rigid body dynamics. The development of the simulation model is oriented on the standard VDI 3633 “Simulation of systems in materials handling, logistics and production” [15]. The physical behavior of individual material flow processes could as well be solved analytically with methods from engineering mechanics. However, as elaborated in Section 1, CPPS entail a big number of individual material flows, each of them with characteristic and changing physical attributes. Describing each of these processes analytically would require time-intensive modeling and solving of a big number of individual equations, which is not suitable for short-term decision-making in highly variant production environments. These circumstances make the physical aspects of material flows in CPPS problem worth simulating. 3.1. Task definition and target system Based on the description of material flows and the role of simulation in CPPS in Section 1, the simulation model has to meet the following requirements: Physics representation: Relevant physical phenomena that occur during typical material flow processes need to be represented realistically. Computation: The model must allow on-line simulation for short-term evaluation of material flow strategies, which leads to the requirement of real-time simulation. Since CPPS entail a big number of different material flow processes, the simulation must further be executable with an economic amount of computing power.
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Flexibility: The model must be able to cope with the high number of variants in a CPPS. In consequence, the modelling effort to integrate work pieces with varying physical properties into the simulation model must be relatively low. Adaption to factory infrastructure: The model must be flexibly applicable to different and changing material handling systems. Furthermore, physical characteristics of respective analyzed factory layouts need to be considered. 3.2. System analysis To define the system that is to be simulated, a bottom-up approach is chosen. This is contrary to most traditional approaches that focus on the overall material flow system and do not consider physical attributes. High-level approaches of this kind are suitable for simulating organizational and chronological aspects of the material flows. However, physical phenomena in material flows occur on the operational level of material flows. This necessitates a bottom-up approach that considers elementary physical material flow processes with a high level of detail. In this context, material flow can be seen as the movement of discrete objects on transport ways or conveyors in steady and unsteady time intervals [16]. The scope of the proposed model therefore focuses on the actual transport processes and excludes processes like storing or commissioning. In consequence, the individual material flow processes contain two kinds of physical elements: Building blocks: Active elements that perform transport processes, like conveyors or vehicles. Production planning and control define their operating mode. Objects: Passive elements that are being transported, like finished products or work pieces. Their behavior is defined by the respective building block. The sequence structure of the material flow processes is determined by several parameters such as the type of building blocks, path or values for velocities and acceleration. Based on the system analysis, a formalized model is developed. Simulation Model
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Objects: Physical and geometrical data of work pieces
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Paths of material flow processes Building blocks: Physical models of material handling systems
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Fig. 2: Conceptual model
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3.3. Model formalization Fig. 2 depicts the formalized simulation model. The core of the model, the physical simulation, incorporates three sub-elements: Building blocks, objects and individual paths of material flow processes. All these elements process data from the CPPS. Data about physical properties of real material handling systems are modeled in the building blocks. The objects incorporate geometric data and physical attributes like surface roughness from the respective production orders. The actual paths that objects take during material flow are obtained from production orders and production control. Production control further allocates objects to specific material handling systems. Furthermore, the factory layout defines the topology of the paths. 3.4. Implementation Based on the conceptual model, an executable software simulation model is implemented (see Fig. 3). For physical simulation of rigid bodies that meets the computation requirement in Section 3.1, a physics engine was selected. Physics engines are software tools that simulate movements of a system as a result of forces acting on that system [17]. Even though physics engines provide less accuracy than finite element analysis (FEA), today’s industrial standard for solving engineering problems [18], they are able to simulate physical behavior of rigid bodies in real-time. Therefore, physics engines match the requirements and fulfil the general criterion, that modelling accuracy should not be as detailed as possible, but as detailed as necessary to fulfil the given targets [15].
CAD
STL URDF
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Fig. 3: Implementation work flow
In a qualitative evaluation of several physics engines, Boeing et al. identified Bullet Physics [19] as the best performing engine among non-commercial physics engines [17]. Consequently, Bullet was chosen for the implementation. As seen in Fig. 2, the inclusion of objects and building blocks into the physical simulation is an essential step. Therefore, 3D computer-aided design (CAD) files of objects and building blocks are converted to into the STL file format. This includes the tessellation, a method that simplifies the geometries by converting their surfaces into triangular meshes [20]. STL conversion is featured in most commercial CAD applications and therefore serves as a universal interface. Afterwards, this geometrical data is imported to Universal Robot Description Format (URDF) files, where they are enriched with physical attributes. URDF is mainly used to model robots, therefore it further allows to connect distinct geometric models with joints, using certain constraints [21]. In terms of computation power, it is recommended to model the building blocks as simple as possible. However, it is essential, that functional surfaces that are likely to affect the physical behavior of the material flow are represented accurately. The physical attributes implemented include mass, friction coefficients and inertia tensors. The latter one can usually be derived in CAD software. The result of this second step are URDF files that serve as geometric and physical model of both building blocks and objects. Finally, the created URDF files are loaded into a scene in pyBullet, a wrapper that allows to access Bullet Physics using the Python programming language. In the respective scene, general parameters like gravitation are defined. Afterwards, the physical behavior of objects and building blocks can be simulated in various scenarios, as seen in Section 3.5.
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3.5. Example In order to demonstrate the implementation work flow, a material flow process that involves an AGV was modeled and simulated. AGVs are driverless vehicles that are independently addressable. They are used for material handling in a factory, allowing flexible routes and adaption to different kinds of transported work pieces [22]. Due to their flexibility, AGVs are seen as one of the enabling technologies for CPPS [23]. Based on CAD files, the AGV was included in a URDF file. The bed of the AGV was modeled as the functional surface with a friction coefficient. As an object, a simple block was modeled to represent the transported work piece. In the scene, a rapid braking of the AGV is simulated. The modeled deceleration was 1 m/s² which is a typical figure in industry [24]. Fig. 4 shows that the object shifts off the AGV and falls to the factory floor as a result of the sudden braking. In a real CPPS, this behavior could potentially harm employees or cause damages. Therefore, parameters of the transport process can be changed according to the results of the simulation. Furthermore, the example simulation fulfilled the requirement for real-time capability. Being simulated at 60 frames per second (16.67 ms/frame) with an Intel Core [email protected] CPU, the median calculation time per frame was 1.18 ms and the maximum calculation time per frame was 8.29 ms. Consequently, the calculation time of every frame was lower than its runtime. In consequence, this example shows how physical simulation can support short-term decision support (see Section 1).
Fig. 4: Visualization of the simulation process in Bullet Physics (a) Regular movement of AGV; (b) Rapid braking of AGV, work piece shifts on load bed; (c) Work piece shifts off load bed; d) AGV stands still, work piece lies on the floor
4. Summary and Outlook CPPS show material flow processes with varying physical properties. In order to predict the physical behavior of these material flows, a physical simulation model was developed. According to the defined requirements, the model is able to process input data that is commonly available in CPPS and can perform simulations in real-time. The objects in the simulation model are generated based on CAD data that is easily accessible in CPPS. Therefore, the comparably low number of distinct material handling systems in a CPPS are the only elements of the simulation model, which require manual modeling. The example showed that the model enabled the visual representation of a
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real transport process using an AGV. However, the described model needs to be improved to be able to fulfil all of the listed requirements. Particularly, the described model requires validation. Primarily, this includes operational validation, to determine whether the output of the simulation provides sufficient accuracy in terms of the models intended purpose. In this case, predictive validation seems reasonable, which means forecasting the behavior of a material flow process via simulation and then comparing the results with the experimental behavior of real processes [25]. Furthermore, the complexity of representable material flows needs to be increased. The described example showed the path of an AGV on a straight surface. In real CPPS scenarios, the paths show a higher degree of complexity. Further improvements have to focus on the representation of physical attributes of the factory layout. Another challenge is imposed by the acquisition of data from CPPS. The input data of the simulation model is acquired via universal file formats, which serve as interfaces. However, the process of data acquisition via the connection to information systems of a CPPS like manufacturing execution systems (MES) has to be described in detail. Finally, the usability of the approach has to be increased. Currently, to perform the described implementation work flow, a series of different software has to be used manually. Since the performed steps are relatively recurring, they seem suitable for the integration into an application that provides a graphical user interface (GUI). Acknowledgement This research was funded by the German research foundation (DFG) within the IRTG 2057 “Physical Modeling for Virtual Manufacturing Systems and Processes”. References [1] acatech - National Academy of Science and Engineering, 2011. Cyber-Physical Systems: Driving force for innovation in mobility, health, energy and production, Berlin, Heidelberg. Springer, 2011. [2] Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S. et al. Cyber-physical systems in manufacturing. CIRP Annals 65, 2016, pp. 621–641. [3] Saldivar, A.A.F., Li, Y., Chen, W.-n., Zhan, Z.-h. et al., 2015. Industry 4.0 with cyber-physical integration: A design and manufacture perspective, in 2015 21st International Conference on Automation and Computing (ICAC), IEEE / Institute of Electrical and Electronics Engineers Incorporated, pp. 1–6. [4] Monostori, L. Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP 17, 2014, pp. 9–13. [5] Lödding, H. Handbook of manufacturing control: Fundamentals, description, configuration, Heidelberg. Springer, 2013. [6] Wang, S., Wan, J., Di Li, Zhang, C. Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks 12, 2016, pp. 1–10. [7] Sandler, R.L. Ethics and Emerging Technologies, Basingstoke. Palgrave Macmillan, 2013. [8] Glatt, M., Aurich, J.C. Physical simulation of cyber-physical production systems (Physiksimulation cyber-physischer Produktionssysteme): Planning and control of cyber-physical production systems using physical simulation (Planung und Steuerung cyber-physischer Produktionssysteme durch physikalische Simulation). wt Werkstatttechnik online 108, 2018, pp. 217–220. [9] Zäh, M.F., Spitzweg, M., Lacour, F.-F. Application of a Physical Model for the Simulation of the Material Flow of a Manufacturing Plant (Einsatz eines Physikmodells zur Simulation des Materialflusses einer Produktionsanlage). it - Information Technology 50, 2008, pp. 192– 198. [10] Reinhart, G., Lacour, F.-F., 2009. Physically based Virtual Commissioning of Material Flow Intensive Manufacturing Plants, in 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2009), Herbert Utz Verlag, München, pp. 377–386. [11] Hoher, S., Schindler, P., Göttlich, S., Schleper, V. et al., 2012. System Dynamic Models and Real-time Simulation of Complex Material Flow Systems, in Enabling Manufacturing Competitiveness and Economic Sustainability: Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Montreal, Canada, 2-5 October 2011, Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, pp. 316–321. [12] Strahilov, A., Ovtcharova, H.C.J., Bar, T., 2012. Development of the physics-based assembly system model for the mechatronic validation of automated assembly systems, in Proceedings of the 2012 Winter Simulation Conference (WSC), IEEE, pp. 1–11. [13] Winkes, P.A., Aurich, J.C. Method for an Enhanced Assembly Planning Process with Systematic Virtual Reality Inclusion. Procedia CIRP 37, 2015, pp. 152–157.
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[14] Cichos, D., Aurich, J.C. Integration of a Physical Model into the Realization of Engineering Changes in Manufacturing Systems. Applied Mechanics and Materials 869, 2017, pp. 159–166. [15] Association of German Engineers (Verein Deutscher Ingenieure e.V). Simulation of systems in materials handling, logistics and production Fundamentals, Berlin. Beuth Verlag 03.100.10, 2014 (VDI 3633 Sheet 1). http://www.vdi.eu/nc/guidelines/vdi_3633_blatt_1simulation_von_logistik_materialfluss_und_produktionssystemen_grundlagen_/. Accessed 9 April 2018. [16] Arnold, D., Furmans, K. Materialfluss in Logistiksystemen, 6th edn., Berlin, Heidelberg. Springer-Verlag Berlin Heidelberg, 2009. [17] Boeing, A., Bräunl, T., 2007. Evaluation of real-time physics simulation systems, in Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, ACM, New York, NY, pp. 281. [18] Rao, S.S. The finite element method in engineering, Oxford. Butterworth-Heinemann, 2017. [19] Bullet Real-Time Physics. https://pybullet.org/wordpress/. Accessed 9 April 2018. [20] Qiu, Z.M., Wong, Y.S., Fuh, J.Y.H., Chen, Y.P. et al. Geometric model simplification for distributed CAD. Computer-Aided Design 36, 2004, pp. 809–819. [21] Lentin, J. Learning robotics using Python: Design, simulate, program, and prototype an interactive autonomous mobile robot from scratch with the help of Python, ROS, and Open-CV!, Birmingham, UK. Packt Publishing, 2015. [22] Ali, M., Khan, W.U. Implementation Issues of AGVs in Flexible Manufacturing System: A Review. Global Journal of Flexible Systems Management 11, 2010, pp. 55–61. [23] Wan, J., Cai, H., Zhou, K., 2015. Industrie 4.0: Enabling technologies, in Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, IEEE, pp. 135–140. [24] Boehning, M., 2014. Improving safety and efficiency of AGVs at warehouse black spots, in 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, pp. 245–249. [25] Sargent, R.G., 2012. Verification and validation of simulation models, in Proceedings of the 2012 Winter Simulation Conference (WSC), IEEE, pp. 162–176.