Potential of a Multi-Agent System Approach for Production Control in Smart Factories

Transcription

1 Preprints of the 16th IFAC Symposium on Information Control Problems in Manufacturing Bergamo, Italy. June 11-13, 218 Potential of a Multi- System Approach for ion Control in Smart Factories Matheus E. Leusin *,1. Mirko Kück **,2. Enzo M. Frazzon *,3. Mauricio U. Maldonado *,4. Michael Freitag **,***,5 *Industrial and Systems Engineering Department, Federal University of Santa Catarina, Florianópolis, Brazil ( 1 matheusleusin@gmail.com, 3 enzo.frazzon@ufsc.br, 4 mauricio.uriona@gmail.com) **BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany ( 2 kue@biba.uni-bremen.de, 5 fre@biba.uni-bremen.de) ***Faculty of ion Engineering, University of Bremen, Bremen, Germany Abstract: Digitalization and Industry 4. allow for the development of new approaches to deal with classic industrial problems, such as production planning and control. In this context, Multi- Systems (MAS) are a promising approach to exploit the new technologies in order to achieve improved planning and control performance. This paper applies a MAS approach to control the production in job shop manufacturing systems. Within a simulation study based on a real industrial case, the approach achieved a good performance compared to the standard scheduling approach applied by the considered company. Keywords: Multi- Systems; Smart Factory; ion Planning and Control; Case Study; Data- Exchange. 1. INTRODUCTION Recently, technological evolution has given rise to a new vision to promote computerization of traditional industries and enable intelligent factories what is known as the 4th industrial revolution (Leitão et al., 215). Industry 4. allows for a combination of intelligent and adaptive systems that use shared knowledge among diverse heterogeneous platforms to make computational decisions (Leitão et al., 216; Leitão et al., 215; Vogel-Heuser et al., 215). These types of systems focus on the integration of computation with physical processes (Lee & Seshia, 216; Leitão et al., 216), including the coordination, monitoring and control of physical operations and engineering systems, integrated through a computing and communication core (Boulekrouche et al., 216). This opens up new possibilities for the industrial sector. For instance, the Job Shop Scheduling Problem (JSP), which is one of the traditional dilemmas in industry, as well as one of the most difficult manufacturing problems (Asadzadeh, 215), can now be tackled dynamically. In this context, the use of Multi- Systems (MAS) offers high potential. This approach defines production resources as intelligent agents that negotiate with each other to implement dynamic reconfigurations and achieve high flexibility (Leitão, 29; Wang et al., 216). Due to agents characteristics of autonomy, robustness to failures and ability to dynamically and flexibly create production schedules (Kouider & Bouzouia, 212), MAS allow to achieve computational agility and better reactivity and adaptability in highly dynamic environments such as job shop systems (Merdan et al., 213). As a result, agent technology has been recognized as a promising paradigm for the next generation of manufacturing systems (Kouider & Bouzouia, 212; Lou et al., 21; Shaw, 1988; Shen et al., 26) and as one of the most suitable technologies for solving JSP (Shen, 22). The paper at hand presents an MAS approach for production control in real-time. The performance of the approach is evaluated in a simulation study based on a real manufacturing system. By comparing the performance of the proposed approach with the performance achieved in the real application, the paper aims to reduce the gap between academics and practitioners, identified in Potts and Strusevich (29) and Pinedo (212), for JSP solutions. The remainder of this paper is organized as follows: Section 2 briefly presents the proposed framework describing the MAS approach and data-driven technologies for its application. Section 3 presents the industrial case used to evaluate the MAS approach. Sections 4 and 5 describe the implementation of the MAS approach into a simulation model and a subsequent simulation study. Section 6 discussed the implications of this paper and finally, Section 7 draws the conclusions. 2. MULTI-AGENT SYSTEMS AND DATA-DRIVEN TECHNOLOGIES The concept of Multi- Systems (MAS) was already developed several decades ago. Recently, it has gained wider acceptance for manufacturing applications due to the availability of data-driven technologies that can provide realtime data to agents (Russell & Norvig, 22; Vogel-Heuser et al., 215). An MAS is characterized by the decentralization and parallel execution of activities by a number of autonomous agents (Vogel-Heuser et al., 215; Wooldridge, 29). The premise of such a system is that multiple agents can cooperate and interact based on their individual information and decisions to obtain better overall results. These agents act autonomously in dynamic environments towards a designated purpose (Owliya et al., 213), which

2 16th IFAC Symposium - INCOM 218, Bergamo, Italy. June 11-13, 218 enables the system to improve its efficiency and reactivity to adversities, increasing its flexibility, robustness and adaptability (Merdan et al., 213; Reaidy et al., 26). Considering that MAS efficiency is highly related to the decision-making and communication capacity between agents, a large part of its potential depends on data-driven technologies. These technologies enable data collection, transmission and its processing by agents. An example of an MAS approach applied to a real manufacturing setting is the Factory of Autonomous s originating from the German Collaborative Research Center 637 Autonomous Logistics (Veigt et al., 211). Further examples can be found in (Monostori et al., 26) or (Scholz-Reiter & Freitag, 27). One approach for data exchange would be the use a Manufacturing Execution System (MES) as a central data hub to connect an MAS with the physical manufacturing system (Frazzon et al., 218). The process data will be provided by RFID technology as well as computing units at each machine of the production system. Planning data might come from the central Enterprise Resource Planning system (ERP). New production orders are created in the ERP system based on customers demand. Each newly released production order (raw material) is equipped with an RFID tag. RFID technology has been considered as one of the most promising technological innovations to increase visibility and efficiency of data analysis in organizations (Cao et al., 217). By using RFID tags, an object such as a workpiece or a pallet can be detected, individually identified and it can store individual dynamic or static information being available easily and timely (Grüninger et al., 21). In this way, parts and sub-assemblies can store priority values as well as their needed production steps, which they receive from the ERP system through wireless communication. In the same way, machines can store their current status, e.g. being working or idle, as well as their current setup configuration. The stored values of products and machines are changed based on the negotiation of autonomous agents, e.g. processed at the computing units at each machine as described in Section 4. According to the priority values on the RFID tags, each product pursues its individual way through the production system. In this way, job shop production data can be collected and managed in a precise, complete, and real-time manner. 3. THE INDUSTRIAL CASE In order to evaluate the MAS approach described in Section 4, it is applied to a real-world production line for automotive transmission forks. This line has nine different types of machines with distinct functions involving the operations of turning, broaching, heat treatment, straightening, cracking, drilling, grinding, assembly and inspection. The analyzed production line consists of two parallel machines for broaching as well as for heat treatment and one machine for each other operation. Therefore, the total number of machines in the production line is 11. Except for the heat treatment machines, which are shared for the production of other products than those analyzed in this paper (Other s), all machines are exclusively dedicated to five specific product types, denominated as products 414, 415, 416, 419, and 422. The manufacturing company performs a monthly production scheduling with the software Preactor by Siemens. Based on demand forecasts for three months, Preactor creates a detailed production schedule for one month. Running Preactor takes 8 hours on average to calculate a monthly schedule in the considered case, due to the high level of detail and comprehensiveness (all factory products) considered by the software. The finished products are delivered weekly on Thursdays. The company produces for 495 hours per month on average and delivers to customers four times per month, thus every hours of production. 4. FRAMEWORK IMPLEMENTATION This section describes a Multi- System approach for real-time production control in an autonomous and adaptive way. For this, six types of agents are proposed: i) an Order Fulfilment, responsible for creating production orders based on the ERP system demand; ii) s, which have the individual information about the products to be manufactured; iii) Machine s, which contain information about each machine in the job shop; iv) Supervisor s, responsible for determining a production sequence based on a certain number of machines and jobs; v) Coordinator s, responsible for coordinating Supervisor s based on global system information; and vi) an AI, responsible for optimizing the sequence generated by the Coordinator. In order to apply the MAS, it was implemented into a simulation model using Anylogic software (Borshchev, 213). The simulation model starts with loading demand data originating from an ERP system. An Order Fulfilment reads this data and creates new agents ( s) for each product to be manufactured. Each contains an individual priority number. When a machine becomes idle, the next job to be processed is chosen from the machine s input buffer based on the priority numbers. In addition to its priority number, each contains information about the required production steps as well as its current manufacturing status. At each completed production step, s receive information regarding which machine will perform the next production operation, calculated by a Coordinator and shared with the other agents. For choosing a machine, Supervisor s update Coordinator s with real-time information about the status of all available s and Machine s on the job shop. Then, the Coordinator applies a decision rule consisting of two steps. In the first step, the Coordinator verifies the machines current setup types (each type of product has its own setup type). For this, the agent verifies (i) which is the next production operation to be executed for each product, and (ii) which machines are available to carry out this step. The Coordinator checks if one or more machines are already configured to produce the considered product, sending it directly to the chosen machine in case of compatibility. The second step determines that s are sent to the machines with the lowest number of

3 Work in progress 16th IFAC Symposium - INCOM 218, Bergamo, Italy. June 11-13, 218 products in their input buffer. This step is only applied if there are no matching preconfigured setups in any of the machines for the analyzed product. Finally, Machine s modify the sequence of products waiting in their buffer queue, passing products with higher priority forward and products with lower priority to the end of the queue. Figure 1 shows a schematic overview of the communication between agents in the developed MAS. The decisions made by the Coordinator can be assessed by s through a common memory what is known as blackboard communication. Machine 1 Machine 2 Machine n Information flow Coordinator Supervisor Priority value, production steps and current status of product Current status of machine Current status of products and machines Sequencing decisions Figure 1: Communication between the agents within the developed Multi- System. 5. SIMULATION STUDY In order to evaluate the performance of the developed MAS approach, a simulation study was conducted. Based on real demand data from the industrial case described in Section 3, four different scenarios with different order release patterns were defined. The performances of the four different MASbased scenarios were compared with each other as well as with the real performance achieved by the company following the production scheduling computed by Preactor. Each simulation of a complete scenario took around 4 minutes to run on a standard personal computer. It has to be noted that this execution time refers to the simulation of how agents behave during a month of production, only for purposes of investigation and performance evaluation of the MAS approach. In a real application, agents decisions are made simultaneously to the production execution in realtime. In the first scenario, the Order Fulfilment created s following exactly the same production schedule computed by Preactor. In this case, the performance difference between the real production that was performed at the company and the MAS approach in Scenario 1 is determined by the priority defined for each product type (which impacts the choice of Machine s among those products waiting in their individual queue; here, the values were arbitrarily chosen between 1 and 6) and by the two decision rules applied by the Coordinator to select a machine for each. In the remaining three scenarios, the Order Fulfilment used the same monthly demand to create s but at different times of the month. In Scenario 2, the Order Fulfilment created the full weekly demand for each type of s at the beginning of each week. In Scenario 3, the Order Fulfilment divided the weekly demand for each type of product into two equal parts released at the beginning and in the middle of each week. In Scenario 4, the Order Fulfilment divided the weekly demand into three equal parts. Figure 2 shows the performances of the MAS approach for simulating the four different scenarios as well as the performance of the real Preactor schedule computed by the company regarding the Work in progress (WIP) of the line considered for one month of production. The maximum WIP of the MAS approach for the four scenarios were 16,469, 18,776, 7,589, and 5,364 for scenarios 1, 2, 3, and 4 respectively compared to 13,26 for the real schedule. The time in which the WIP was (that is, the whole line was idle) were 17, 176, 168, and 163 hours for scenarios 1, 2, 3, and 4, respectively compared to 183 hours for the real schedule. There is a steep oscillation in the number of products being manufactured during the month, with extreme values oscillating between 13,26 and. This oscillation was validated by employees of the company, who explained that - typically - there is a greater allocation of labor for this line at the beginning of the month, generating high WIP and high inventories of finished products Real Schedule MAS Scenario 1 MAS Scenario 2 MAS Scenario 3 MAS Scenario 4 Figure 2: Performance of the MAS approach regarding WIP. With regard to the number of setup activities performed in the analyzed period, Figure 3 presents the performance of the MAS approach for each scenario in comparison to the real Preactor schedule. In total, 36, 68, 38, 64, and 96 setup operations were performed for the real production schedule as well as scenarios 1, 2, 3, and 4 respectively. Regarding setup activities, Azzouz et al. (217) highlight that most

4 Work in progress Produced % of demand Number of setup activities executed Percentage of machine utilization 16th IFAC Symposium - INCOM 218, Bergamo, Italy. June 11-13, 218 research in job shop scheduling ignores this type of operation or considers it as part of the processing time. Despite this, in real situations, setup operations can have a great impact on production and thus should be taken into account Real schedule MAS Scenario 1 MAS Scenario 2 MAS Scenario 3 MAS Scenario 4 Figure 3: Performance of the MAS approach regarding setup operations. Figure 4 shows the customers demand in comparison to the quantity of products made in each week. All the analyzed scenarios were able to meet the total monthly customer demand during the considered period. However, the production of each product type varied considerably mainly in Scenario 1 and in the case of the real production schedule. 4% 35% 3% 25% 2% 15% 1% 5% % s 414 s 415 s 416 s 419 s 422 Other s Week Real Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4 Figure 4: Comparison between demand and produced goods. Figure 5 presents the average utilization of the 11 machines of the considered job shop. Again, the real production schedule and the MAS approach in Scenario 1 show similar performances, with utilizations ranging from 33.8 to 9.6% and from 32.9% to 9.1%. In the remaining three scenarios, the MAS approach shows similar performances with fairly constant utilizations between 18.9 and 18.4% for Scenario 2, 19.5 and 19.1% for Scenario 3 and 18.2 and 17.7% for Scenario 4. 35% 3% 25% 2% 15% 1% 5% Real schedule MAS Scenario 1 MAS Scenario 2 MAS Scenario 3 MAS Scenario 4 Figure 5: Performance of the MAS approach regarding machines utilization. Finally, two hypothetical situations of removing and adding machines were created to analyze the adaptability of the agents to a machine breakdown as well as the usefulness of the MAS simulation as a managerial tool for decision-making if new machines are included into the system. Scenario 2 was chosen as benchmark for both hypothetical cases due to its high average WIP. In the first case, a breakdown of a heat treatment machine at time 1 was simulated (Figure 6). A small change in performance in relation to the scenario without machine breakdown can be seen in Figure 6. It has to be mentioned that the perceived difference in performance between the two scenarios is solely due to the reduction of production capacity caused by the individual machine breakdown. This can be concluded since the same behavior of decrease is seen for both scenarios in the four weeks presented in Figure 6, which means that the breakdown that occurred at time 1 did not affect the agents performance to adapt and to choose the next available machine to perform the same operation MAS Scenario 2 Heat Treatment Breakdown Scenario Figure 6: Performance of the MAS approach in a hypothetical machine breakdown situation. Figure 7 shows the results of the second hypothetical scenario, simulating the addition of one assembly machine, one crack inspection machine and one heat treatment machine individually, so that the total sum of machines is always 12. It can be seen that the addition of an assembly machine brings major benefits to the job shop performance under the MAS approach. In addition to reducing the maximum WIP from 18,776 to 17,642, the addition of such a machine to the job shop still allows an extra idle capacity of 58 hours per week compared to Scenario 2.

5 Work in progress 16th IFAC Symposium - INCOM 218, Bergamo, Italy. June 11-13, MAS Scenario 2 2 Assembly Machines 2 Cracks Inspection Machines 3 Heat Treatments Machines Figure 7: Performance of the MAS approach in a hypothetical machine acquisition situation. 6. DISCUSSION This section discusses the results of the simulation study presented in the previous section. It has to be noted that the real production data achieved by the company after computing a schedule with Preactor show oscillations between high production peaks at the beginning of the months and lower values at the end of the month regarding WIP, finished products inventories and machine utilizations. As a result, the production system needs a larger space to accommodate the high number of finished goods and WIP at peak periods, leading to higher production costs. The option to work with higher WIP levels, however, favors reducing setup activities by producing a quantity of a product far above the weekly demands, resulting in even less flexibility to adapt to changing customer demands. Applying the MAS approach based on the same production schedule changes the results, since dispatching rules are used for the production. Including a new demand could enable these products to be produced immediately by increasing their priority value, or to shift products to the end of the current production week queue by using a priority value below the products being made. The dispatching rules applied calculate a static priority value, but the MAS application also enables the inclusion of dynamic priority values for each product type. Scenarios 2-4 show the flexibility of the MAS approach clearly. In Scenario 2, the option to produce the weekly demand at once allowed a better performance in relation to the machine setup operations, which were very close to the best performance seen for the real production schedule of the company. This option however generated a high WIP, although it generated a lower inventory of finished products compared to the real production schedule and to Scenario 1. In Scenarios 3 and 4, the WIP is reduced dramatically at the cost of increasing setup operations. However, Scenario 4 deserves to be highlighted in comparison to the others due to the reduced WIP over the analyzed period of time, which had its peak of 67,4% lower than that of the real production schedule. were simulated. Firstly, the approach showed applicability to react on machine breakdowns. Here, the importance of the agents' autonomy to adapt to adverse situations is perceived. The product agents in the broken machine s input buffer are naturally oriented to the next available machine to perform the same operation. This is due to the advantages provided by real-time data-exchange and the agility made possible by the use of dispatching rules. In the second hypothetical scenario, one can see the usefulness of the MAS simulation to make decisions not necessarily only focused on production scheduling, but on system s overall performance when new machines are integrated into the manufacturing system. Moreover, some further insights related to industrial applications can be pointed out. It turns out that production control involving only makespan minimization as objective should not be taken as a golden rule for all industrial cases. The emergence of new technological development brings light to the exploration of other possibilities. As seen in this case, better resource utilization and adaptability both to job shop disturbances and to changing customer demands may be more beneficial to industrial performance than the individual makespan reduction. In this context, planning and control methods can benefit from data-driven technologies, which allow real-time production monitoring and decision-making. Digitalization and data-exchange across all company layers should lead to the integration of all its processes enabling industrial performance improvements that are not limited to job shop production and control but can comprise also other tasks and processes such as transport planning and inventory control. 7. CONCLUSION This paper proposed a Multi- System approach for realtime production control in job shop manufacturing systems. In order to evaluate the performance of the approach, it was implemented into a simulation model based on a real industrial production line of automotive transmission forks. The approach achieved an improved performance regarding important key performance indicators such as work-inprogress and machine utilization compared to the real production schedule computed by the company of the industrial application. Moreover, the MAS approach offers flexibility by the use of individual priority definitions for the products. This allows the production system to meet customer demand changes, which may eventually choose to advance the delivery of a particular type of product instead of another. It is also worth mentioning the feasibility of using the approach in a simulation model to make strategic decisions about the job shop, as verified in one of the analyzed experiments. Overall, this paper showed the high potential of MAS approaches to exploit the possibilities offered by the new technological developments in the context of Industry 4.. After evaluating the performance of the MAS approach in the real industrial application, two further hypothetical scenarios

6 16th IFAC Symposium - INCOM 218, Bergamo, Italy. June 11-13, 218 ACKNOWLEDGEMENTS This work was funded by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) under reference number /215-6 and by the German Research Foundation (DFG) under reference number FR 3658/1-1, in the scope of the BRAGECRIM program. REFERENCES Asadzadeh, L. (215). A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Computers & Industrial Engineering, 85, Azzouz, A., Ennigrou, M. & Said, L.B. (217). A hybrid algorithm for flexible job-shop scheduling problem with setup times. International Journal of ion Management and Engineering, 5(1), Borshchev, A. (213). The big book of simulation modeling: multimethod modeling with AnyLogic 6. AnyLogic North America. Boulekrouche, B., Jabeur, N. & Alimazighi, Z. (216). Toward integrating grid and cloud-based concepts for an enhanced deployment of spatial data warehouses in cyber-physical system applications. Journal of Ambient Intelligence and Humanized Computing, 7(4), Cao, W., Jiang, P.Y., Lu, P., Liu, B. & Jiang, K.Y. (217). Real-time data-driven monitoring in job-shop floor based on radio frequency identification. International Journal of Advanced Manufacturing Technology, 92(5-8), Frazzon, E.M., Kück, M. & Freitag, M. (218). Data-driven production control for complex and dynamic manufacturing systems. CIRP Annals - Manufacturing Technology, 67(1), in print. Grüninger, M., Shapiro, S., Fox, M.S. & Weppner, H. (21). Combining RFID with ontologies to create smart objects. International Journal of ion Research, 48(9), Kouider, A. & Bouzouia, B. (212). Multi-agent job shop scheduling system based on co-operative approach of idle time minimisation. International Journal of ion Research, 5(2), Lee, E.A. & Seshia, S.A. (216). Introduction to embedded systems: A cyber-physical systems approach. MIT Press. Leitão, P. (29). -based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22(7), Leitão, P., Colombo, A.W. & Karnouskos, S. (216). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, Leitão, P., Rodrigues, N., Barbosa, J., Turrin, C. & Pagani, A. (215). Intelligent products: The grace experience. Control Engineering Practice, 42, Lou, P., Ong, S.K. & Nee, A.Y.C. (21). -based distributed scheduling for virtual job shops. International Journal of ion Research, 48(13), Merdan, M., Moser, T., Sunindyo, W., Biffl, S. & Vrba, P. (213). Workflow scheduling using multi-agent systems in a dynamically changing environment. Journal of simulation, 7(3), Monostori, L., Váncza, J. & Kumara, S.R.T. (26). based systems for manufacturing. CIRP Annals - Manufacturing Technology, 55(2), Owliya, M., Saadat, M., Jules, G.G., Goharian, M. & Anane, R. (213). -Based Interaction Protocols and Topologies for Manufacturing Task Allocation. Ieee Transactions on Systems Man Cybernetics-Systems, 43(1), Pinedo, M. Scheduling. New York: Springer, 212. Potts, C.N. & Strusevich, V.A. (29). Fifty years of scheduling: a survey of milestones. Journal of the Operational Research Society, 6(1), S41-S68. Reaidy, J., Massotte, P. & Diep, D. (26). Comparison of negotiation protocols in dynamic agent-based manufacturing systems. International Journal of ion Economics, 99(1), Russell, S.J. & Norvig, P. (22). Artificial intelligence: a modern approach (International Edition). Scholz-Reiter, B. & Freitag, M. (27). Autonomous processes in assembly systems. CIRP Annals Manufacturing Technology, 56(2), Shaw, M.J. (1988). Dynamic scheduling in cellular manufacturing systems: a framework for networked decision making. Journal of Manufacturing Systems, 7(2), Shen, W. (22). Distributed manufacturing scheduling using intelligent agents. IEEE intelligent systems, 17(1), Shen, W., Hao, Q., Yoon, H.J. & Norrie, D.H. (26). Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, 2(4), Veigt, M., Ganji, F., Morales Kluge, E. & Scholz-Reiter, B. (211). Autonomous Control in ion Planning and Control: How to Integrate Autonomous Control into Existing ion Planning and Control Structures. In: Autonomous Cooperation and Control in Logistics. Springer, Berlin, Vogel-Heuser, B., Lee, J. & Leitão, P. (215). s enabling cyber-physical production systems. At- Automatisierungstechnik, 63(1), Wang, S.Y., Wan, J.F., Zhang, D.Q., Li, D. & Zhang, C.H. (216). Towards smart factory for industry 4.: a selforganized multi-agent system with big data based feedback and coordination. Computer Networks, 11, Wooldridge, M. (29). An introduction to multiagent systems. John Wiley & Sons.