Production process based on CIMOSA modeling approach and software agents Pawel Pawlewski 1, Arkadiusz Kawa 2 1 Poznan University of Technology, Strzelecka 11, 60-695 Poznań, Poland pawel.pawlewski@put.poznan.pl; 2 Poznań University of Economics, al. Niepodleglości 10, 60-965 Poznań, Poland arkadiusz.kawa@ue.poznan.pl; Abstract: The paper presents the results of the authors studies which consisted in modeling the processes taking place in an enterprise characterized by manufacturing of complex products (machine building) of a very long production cycle. The production environment of a diesel ship engine is described. The idea of the so-called domains of the CIMOSA concept which has been used for the modeling was explained. The article introduces the multi-agent system idea on the basis of which a model will be built and used in the simulation experiment. Finally the algorithm for planning process is presented. Keywords: distributed manufacturing system, multi-agent systems, supply chain, distributed process planning and scheduling. 1 Introduction In order to maintain their competitive advantage in the changing world economy enterprises must have the ability of both developing new products quickly and adjusting their production capabilities and their functionality to the current market needs. Enterprises which make highly complex products (machine-building, automotive, aerospace industries) make use of the possibilities offered by the conception of Product Lifecycle Management (PLM) in order to produce them competitively. The PLM approach offers methods of product management and development, information related to the product, including its development, production, marketing, order and delivery processes, throughout the whole product lifecycle from the initiation (product idea) to its end [1]. It must be stated that although the PLM conception helps to manage product-related information, its numerous variants and modifications to a large extent, its application in critical production phases is insufficiently defined/developed. The main reason is the fact that additional levels related to the complexity of production processes are not taken into consideration. Plenty of instances (copies) of the product must be made in the required amount, which means that production processes are, in fact, composed of many individual processes (one for each item produced) carried out simultaneously but in different temporal sequences. Each of them may also contain additional elements (variants) due to customization.
Product portfolio Volume needed Process Resources design change 1 4 7 8 n 1 4 7 8 m Production customization 1 2 3 4 n Volume 1 2 7 9 k 1 2 3 4 n Time Fig. 1: Complexity of production process and its management [2]. That is why there is an obvious need to expand the basic PLM conception which would allow to take the process perspective product lifecycle management and the PPLM process (Product and Process Lifecycle Management) into account fully. With regard to the need of developing an alternative for the PLM conception the consideration of the process perspective the authors have mainly focused on two important phases of the PLM conception the process planning phase and the production phase. The studies related to these phases published in the world have integrated them into the so-called Enterprise Architecture (EA). It is being predicted that almost 70% of the European Union enterprises will introduce the EA projects in the nearest future [3]. The previous research of the authors has already proven that two approaches are dominating the range of EA-related studies at the moment: one, dominated by computer science, which aims at enterprise integration around information systems and assumes information service of business processes as its staring point, and another one which aims at enterprise integration around productive processes. The worldwide research related to the second approach is mainly based on the CIMOSA and GRAI conceptions [4]. The aim of the paper is to present the results of the authors studies which consisted in modeling the processes taking place in an enterprise characterized by manufacturing of complex products (machine building) of a very long production cycle. It is unrhythmical production, also characterized by the highest degree of complexity of planning and production controlling problems due to the amount and changeability of labor. The subject of the studies is the production process of ship engines at HCP a company which is a co-operator in the research conducted by the authors. The article consists of 4 sections, the second of which presents the production environment of a ship engine at HCP. The third one describes the idea of the socalled Domains of the CIMOSA conception which has been used for the modeling. The fourth section depicts the multi-agent system idea on the basis of
which a model will be built and used in the simulation experiment. The fifth section focuses on modeling of long cycle process and the proposals to planning process. 2 The factory characteristics HCP is a factory established in 1857 in Poznań (Poland), and named by its founder Hipolit Cegielski. Nowadays it is the biggest ship engines producer in Europe. HCP produces slow speed-rotation engines for transport ships. The ship engines are built to special orders of customers. Even two engines of same type may have some differences depending on a customer s wishes. HCP builds about 25 35 engines per year. The engines are built under the license of Sulzer Brothers (Wartsila) and Burmeister & Wain. The dimensions of such engines are impressive: over 4 meters wide, over 20 meter long, almost 16 meters high. A ship engine is a product of a very high capital intensity, and that is why financing its production must be supported by guarantees and bank loans. Therefore, in the company there are two sale schedules: customer sales schedule and optional sales schedule. The customer sales schedule takes into account those engines which are secured by bank guarantees. The optional sales schedule includes the engines which do not have the guarantees yet. Due to the length of the engine production cycle, the customer sales schedule is prepared two years before it is introduced, and the optional sales schedules even three years before the introduction. Obviously, in the meantime the schedules are modified, since owing to the instability of the shipyard industry frequent modifications are necessary. At the moment, HCP company produces approximately 10 types of engines. An average length of the production process runs at the level of nine months. The manufacturing process in the enterprise takes place in four divisions: welding shop, processing, assembly and packing department. 3 CIMOSA domain concept Enterprise processes of the HCP company were modeled on the basis of the CIMOSA (Computer Integrated Manufacturing Open System Architecture) paradigm. CIMOSA is an open systems architecture for enterprise integration [5]. Its aim is to develop an Open System Architecture for Computer Integrated Manufacturing (CIM) and describe ideas and rules to facilitate the design and construction of future IT systems. This architecture uses the system life cycle concept together with a modeling language and definitions of methodology and supporting technology to cover the function, information, resource and organization aspects [6]. CIMOSA gives the possibilities to create a model which can be used for process simulation and analysis. It places the business process concept at the heart of the approach to model the various sequences of steps and the numerous flows
occurring in enterprises[7]. It is very important, especially for complex production processes. As mentioned before (cf. Section 1), the whole process of marine diesel engine manufacturing is multipronged and composed of a lot of operations. According to the CIMOSA paradigm, processes can be logically organized into functional clusters which are called domains. They are a modular way to deal with the overall system complexity. Such a domain is a functional scope which embraces complete processes (e.g. management planning domain, procurement domain, loads planning domain, control domain) [7]. Figure 2 shows ten domains (D1, D2,, D10) which are defined for the engine production process in HCP. Fig. 3. Domains in marine diesel engine manufacturing process (source: HCP). 4 Multi-agent approach Traditional approaches to production planning and scheduling in MRP based logic do not consider real-time machine workloads and shop floor dynamics. Therefore, there is a need for the integration of manufacturing process planning and control systems for generating more realistic and effective plans. The overview of approaches to production planning and scheduling can be found in [8]. Additionally specific, very long cycle production processes are not the main subject which is taken into consideration by this concept. Traditional approaches are to production planning and scheduling are based on: Centralized Optimization Algorithms [9], Close Loop Optimization [10] and Distributed Process-Planning (DPP) Approaches [11]. Agent-based approaches provide a distributed intelligent solution by multiagent negotiation, coordination, and cooperation. The following researches refer to application of multi-agent systems for production planning purpose [8]:
- bidding based approach - the process routes and schedules of a part are accomplished through the contract net bids; - a multi-agent architecture based on separation of the rough processplanning task as a centralized shop floor planner from the detailed process planning conducted through agent negotiations. - based on cascading auction protocol provides a framework for integrating process planning and hierarchical shop floor control.. The application of multi-agent can be extended to whole long cycle process due to following potential advantages of distributed manufacturing scheduling [12] logic : - usage of parallel computation through a large number of processors, which may provide scheduling systems with high efficiency and robustness. - Ability to integrate manufacturing process planning and scheduling. - possibility for individual resources to trade off local performance to improve global performance, leading to cooperative scheduling. - possibility of connection directly to physical devices and execution of realtime dynamic rescheduling with respect to system stability. - it provides the manufacturing system with higher reliability and device fault tolerance. - the manufacturing capabilities of manufacturers can be directly connected to each other and optimization is possible at the supply chain level, in addition to the shop floor level and the enterprise level. - possibility of application of other techniques may be adopted at certain levels for decision-making, for example: simulated annealing, genetic algorithm etc. Agent-based approaches provide a natural way to integrate planning and control activates and makes possible simultaneously the optimization of these functions. Proposed by authors conceptual framework for the multi-agent approach method involves the hybrid solutions combining the advantages of MRP simple logic and theory of constrains (TOC) ability to synchronize all production and material flow in very long cycle processes. The applications of TOC as synchronization mechanism allows to reduce a number of parameters to be control so it allows to simplify the complexity of integration problem. 5 Multi-agent approach for long cycle process Agent-based system is defined in following paper as a multi-agent system that s acts as a support tool and utilized the databases of main system (ERP system). Multi-agent system is a collection of heterogeneous, encapsulated applications (agents) that participate in the decision making process [13]. The architecture of proposed tool (VLPRO-GRAPH Very Long Process Graph) is based on the assumption that system will support the MPS creation in ERP system and will be plug in to ERP system database by for example java connector.
Fig. 4. Model of very long cycle production process of diesel ship engine. Fig. 5. VLPRO-GRAPH agent model. Figure 4 shows the model of very long cycle process and figure 5 illustrates structure and the amount of agents which can be found in each layer. The planning problem in following paper is described at three layers reflecting to [15]: 1) Long process perspective so called long process planning. 2) The entity level where long process plan is divided to sub-plans which are executed by each subprocess and being transform for individual production schedule at domain level and where local re-planning activities takes place.
3) Domain sub-layer where production control activities are executed and information about disturbances are gathered and passed to upper levels. The graphical user interface agent creates a graphical user interface (GUI) for the interaction of the MAS (Multi-Agent System) to production manager (direct users). The GUI-Agent is able to initialize and sent behavior parameters and messages to the Super-Visor Agent (SV-Agent). The SV-Agent is exactly one in the system because the data from all the domain agents (D-Agent) is fused at this agent to generate re-planning schedules for the production. The SV-Agent is responsible for control of the logic of all agents and creates the plans for the D- Agent. The planning process is presented in figure 6. Definition of chain goal and set of performance indicators STEP 2 Generate an initial MPS for domains taking in consideration customer orders assign to planning horizon plan and capacities constrains Negotiate the initial plan with supply and distribution side and find a plan with lower number of constrains among them (so called feasible MPS for long cycle process) Decompose the feasible MPS for sub-plans for domains Insert synchronization among sub-plans based on TOC concept for time buffers and Drum-Buffer-Line concept where manufacturer sub-mps is giving pace for supplies and distribution planning activities Allocate sub-plans to agents using task-passing mechanism, if failure come back to previous step or generate new global MPS (step 2) Initiate plan monitoring when plans are executed in TOC green buffer no additional re-planning needed when plans are executed in yellow TOC buffer the re-planning at local level, if plans are executed in TOC red buffer go to step 2. Fig. 6. Planning algorithm for VLPRO-GRAPH agent model. D-Agents are initialized by the SV-Agent and they are responsible for translation of the long process plan into detail schedules. The agent is allowed to prepare the number of alternative (contingency) local plans as long as there are not conflicting with long process MPS. The local re-planning activities are allowed as long as they don t influence the long process MPS. When re-planning activity affects the long process MPS it has to be passed to SV-Agent. The A-Agent is responsible for
control of plans execution within sub-process based on given performance indicators. It reports to D-Agent in upper layer whether production plans are executed according to given MPS. References 1. Saaksvuori A, Immonen A (2008) Product Lifecycle Management, 3rd ed.,springer, Berlin Heidelberg New York 2. Pasek Z.J., Trujillo J., Pawlewski P., (2009) Process Flow Logic Approach to Manufacturing Process Definition, Configuration and Control [in:] The proceedings 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2009) 3. Vernadat F.B. (2007) Potentials and benefits of Enterprise Architectures: a practitioner s experience. [in]: Proceedings IESM 2007. Beijing. 4. Chen D., Doumeingts G., Vernadat F.B., 2008, Architectures for enterprise integration and interoperability. Past, present and future. Computers in Industry 59 p.647-659 5. AMICE, 1993, CIMOSA Open System Architecture for CIM, 2nd edition, Springer-Verlag, Berlin 6. Vernadat F., Enterprise modeling and integration: principles and applications. Chapman and Hall, 1996 7. Berio G., Vernadat F. (2001): Enterprise modelling with CIMOSA: functional and organizational aspects. Production Planning & Control, Vol. 12, No. 2, s. 128-136. 8. Shen, W., Wang L., Hao Q. (2006), Agent-Based Distributed Manufacturing Process Planning and Scheduling: A state-of-art survey, IEEE Transactions on Systems, Man and Cybernetics-Part C:Applications and Reviews, Vo. 36, No.4 9. Zijm W.H.M, (1995), The integration of process planning and shop floor scheduling in small batch part manufacturing, Ann CIRP, vol. 44, no.1., pp.429-432, 1995. 10. Saygin C., Kilic S.., Integrating flexible process plans with scheduling in flexible manufacturing systems, Int. J. Adv. Manufacturing Tech.,vol.15, no.4,pp268-280. 11. Chang F.T.S., Zhang J., Li P., (2001), Modelling of integrated, distributed and cooperative process planning system using an agent-based approach, Proc. Inst. Mech. Eng., Part B-J. Eng. Manuf., vol.215, no. B10, pp.1437-1451. 12. Shen W. (2002), Distributed manufacturing scheduling using intelligent agent, IEEE Expert /Intell.Syst., vol. 17, no 1, pp. 88-94 13. Pechoucek M., Říha A., Vokrínek J., Marík V. and Prazma V. (2003), ExPlanTech : Applying Multi-agent Systems in Production Planning Production Planning and Control vol.3, no 3, pp. 116-125 14. Jennings N.R., Wooldridge M.J., (1998), Applications of Intelligent Agents. Agent Technology: Foundations, Applications, and Markets, Springer, pp. 3-28 15. Pawlewski P., Golińska P., Fertsch M., Tujillo J., Pasek Z., Multiagent approach for supply chain integration by distributed production planning, scheduling and control system. International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008) SPRINGER Advances in Soft Computing ISBN 978-3-540-85862-1