AUTOMATIC CREATION OF SIMULATION MODELS FOR FLOW ASSEMBLY LINES

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1 AUTOMATIC CREATION OF SIMULATION MODELS FOR FLOW ASSEMBLY LINES João Weinholtz, Rui Loureiro, Carlos Cardeira, João M. Sousa Instituto Superior Técnico, Technical University of Lisbon Dept. of Mechanical Engineering, GCAR - IDMEC Av. Rovisco Pais, Lisboa, Portugal [email protected], {carlos.cardeira, jmsousa}@ist.utl.pt Bombardier Transportation Portugal, SA Rua Vice-Almirante Azevedo Coutinho, nº 1, Amadora, Portugal [email protected] Abstract: This paper presents a new software tool for automatic creation of simulation models for flow lines. The tool was developed in a general way. The tool was applied to the production flow line at the rolling stock Company Bombardier Transportation Portugal, SA, in Amadora, in order to develop its simulation model. Information regarding manufacturing philosophies, flow assembly lines, production scheduling, and software tools for the model creation are required to perform this task. The paper mainly focuses on the model creation process and the productivity gains generated by this work. Keywords: Process simulators, production systems, scheduling, simulation. 1. INTRODUCTION Recent manufacturing philosophies have been centered in marketing, logistics, production or materials. New manufacturing philosophies points to production as the central area of the manufacturing process. Note that production is supported by many other service functions in order to timely deliver high-quality, low-cost, functional products to customers, see Pinedo (2002). The major concerns of the order/project management are then process organization, resource optimization and throughput time reduction according to vision and objectives of the company. The dynamic strategies of corporations, especially the ones that have to deal with long lead-time products, are dealing with a strong increase of management complexity, since processes and information must be adjusted quickly and consistently. Currently, these adjustments to changes have not been done in an efficient way; the costs of changes are usually high. Therefore, the low adaptation of enterprises to the constraints of the market resources is not allowing the optimization of processes and is increasing production costs. Several software solutions are commercially available to this optimization, but they normally are focused on the sequence generation and/or line balancing, disregarding the optimization of production processes and the dynamism of the companies. However, to the best of our knowledge, software tools to create models to simulate flow assembly lines are not readily available. Due to the high costs associated with the revision of a manufacturing process (a methods team would spend weeks or months to redesign a running process and the double of the time to issue all required documentation) process optimization is only verified once in the initial phase of layout design and aggregate documentation. Thus, the optimization process is done by local subprocess improvement with the goal of reducing the execution time of individual tasks, ignoring completely the dynamic philosophies linked with company main lines. In order to achieve optimized solutions at reduced cost, it is imperative to develop algorithms to be implemented in some standard software. This paper presents the development of software tools to simulate and optimize flow assembly lines. The presented tool allows the inclusion of methods staff expertise. This know-how allows a fast dynamic reconfiguration of the assembly scheduling, a fast response to shortages in assembly line or equipment

2 malfunctions and an efficient adaptation to changes in the company objectives. The study of flow production lines is a very complex task due mainly to the huge number of variables to handle (Pinedo and Chao, 1999). The developed work started by analyzing an existing production line. A deep and continuous communication with the methods team was required for acquiring data and learning in detail the implementation of the production line. The key point in a flow assembly line is the production schedule. The scheduling solutions for production were optimized based on standard techniques, namely the Shifting Bottleneck Heuristic (Pinedo, 2002). These solutions were developed recurring to the software MatLab. The obtained scheduling is stored in a database and becomes the input necessary for the model creation. These data are translated to Arena using Visual Basic, to simulate, validate and test the obtained production simulation models. Note that the work is general enough, such that the developed models can simulate different types of large-scale products, such as rolling stock material, planes or armored vehicles. Classically, simulations models are built to represent the reality. As so, the ideal model should allow the simulation of unforeseen difficulties and adapt itself to new environments. A simulation, beyond representing the reality, can also be used to study the model responses to chosen sequences of events. The main goals of the developed work were then to obtain a solution for the scheduling problem and to define the risk of the solutions found. To achieve these objectives the following steps were performed: 1. Analysis of flow assembly lines and identification of their constraints. 2. Definition of a data structure to store the production schedule for simulation purposes. 3. Development of a software tool for automatic creation of simulation models of a given production schedule. 4. Application to the flow assembly line of the train manufacturing process implemented at Bombardier Transportation SA Amadora. 5. Analysis of obtained results. This article is organized as follows. Section 2 presents some software simulation tools and the reasons to choose a special one. Section 3 describes the characteristics of the developed software tool, presenting both the objects of the tool and the flow assembly lines specifications and special characteristics. The detailed description of the developed software tool and the justification for some options that were made are presented in Section 4. A case study and its production scheduling are described in section 5, and some conclusions are drawn in section SOFTWARE SIMULATION TOOLS The simulation of industrial assembly lines has been done by different methodologies, especially by those that are computer aided. Different software tools can be found for discrete or continuous event systems exist. Due to the industrial focus of simulation software houses, some difficulties arise in finding information about the features of commercial available tools. Hssain (2000) presents some solutions to flux simulation. Moreover, there are different software simulation and scheduling tools available like Arena (Kelton et al., 2003), Witness, Automod, Asprova, or Jobboss, for which their features were summarily studied. Russo and Vicente (2003) performed a comparative study of those software tools based on learning easiness, process adaptation, graphic features and cost aspects, using a decreasing A to D classification, as presented in Table 1. Table 1: Software tools comparison learning easiness process adaptation continuos process graphic features cost ARENA D A C A C AUTOMOD C A C A D EXTEND B B A B A PROMODEL B B D B D SIMPLE ++ C C D C E TAYLOR B C D B B WITNESS C A C B D The characteristics that are more relevant for the presented work are process adaptation and the visualization facility translated in the graphic features. By observing Table 1, it is easily concluded that both Arena and Automod are suitable to the simulation purposes of this paper. As Arena is less expensive, and moreover a license under special conditions for research and development was possible to be bought, it was the software chosen for simulation of the production line. 3. CHARACTERISTICS OF THE SOFTWARE TOOL This section presents the description of the basic features, in terms of functional characteristics to allow the development of simulation models.

3 3.1 Objects of the software tool The objects of the software development tool are the following. Entities are the dynamic objects for simulation. They can be created, used through the system and deleted when they no longer necessary for the simulation. They are abstract representations of the objects to be processed in the system. Attributes are each characteristic of an entity. An entity can contain several different characteristics at same time. Variables are defined as global for a specific system, and have read/write access by all the entities. Resources represent any need, such as a worker, a machine, etc. of a task. The resources have limited capacity, can be used in any section of the model and can be used by any entity. Queues are entities that request a resource in use by other entity. The entities must wait in the queue until the resource is released. Arena Macros Visual Basic Automatic creation of simulation model Macros Fig. 1: Creation of the simulation model. In order to build a simulation model automatically it is necessary to make the connection between a production schedule using the Shifting Bottleneck Heuristic (stored in Excel) and Arena. The communication is established using a Visual Basic program, which receives the necessary information and creates the simulation of the production line in Arena. 3.2 Specifications and special characteristics During the process analysis important constraints were taken into account: Working stations, which are the physical places, were a sequence of tasks is executed, may share resources. Tasks may have predecessors. Assembly lines have the ability to produce vehicles of different types. Different tasks can be executed simultaneously in the same working station, but at different locations. Task assembly times follow a probabilistic distribution. Task times can have large variations. These specifications were all implemented in the software tool to create simulation tools. It should be stressed that he cooperation of workers and production experts was fundamental for the determination of the characteristics of the assembly line and their respective constraints. 4. SOFTWARE TOOL FOR MODEL CREATION The software tool developed for the simulation of production lines is at the moment implemented for Flexible Flow Shop configurations (Pinedo, 2002). Any production schedule can be modeled and simulated by Arena using the developed tool. The model manages all the existing resources and tasks, creating sequences and attributes characteristics to them. Each task can be described as independent, i.e., it does not need to follow a sequence, and can be part of different sequences. Several variables must be created, namely: vehicle type, vehicle type comparison, station occupation control and task execution identification. The software tool also creates the processing models described next. 4.1 Entity creation Each entity represents a vehicle, which receives all tasks and resources required, and transport them until the end of the manufacturing process. The entity must follow the tasks defined in the production schedule, which are accomplished in the given station. At any instant the entity must contain the work in progress of the process. This module determines also the first stations of the routing to be followed by the entity, holding it in case the destination station is not free. An example of the Arena aspect of an entity is given in Fig. 2. Car Selection Car Attributes Fig. 2: Entity creation and routing indication.

4 4.2 Start of an assembly station Assembly stations are independent amongst them. These stations transform the arriving entity and process it. The entity type is verified and routed to the task sequence defined for the station. The number of teams and its location in the station are defined and included into the entity. Also task works are attributed to the entity. The assembly station can receive different kinds of entities and processes them sequentially. An example of an assembly station is given in Fig. 3. Car Selection Human resources and task definitions This module is used to aggregate all assembled parts of the product in just one entity that represents all work in process incorporated in the tasks processed before. This last entity is then routed to the end of the module, where is retained until the next station is available to receive it. Transport of entities between stations is made by a single resource. If the transportation means are not available, the entity remains at the end of the station waiting for its availability. Each closing assembly station can concatenate up to 25 teams (processes) into one single entity. This number is considered enough to aggregate all cars in the formation of a tram, e.g. the Eurostar is a ten car formation tram. Is also in this module that some statistical data can be stored for later analysis. An example is given in Fig. 5. Car Tasks A WIP aggregation Car Tasks B Fig. 3: Example of an assembly station with different types of entities to process. 4.3 Tasks The objective of a task is to use the respective resources taking a certain time according to its requirements. Some tasks may be dependent of others (precedents) to be initialized. Only the precedent tasks processed in the same station are verified before the initialization of a task, because the tasks on the preceding stations are already concluded. When an entity arrives at a certain task, the availability of resources must be verified. If the resources are not available, the entity is retained until they are at disposal. The resources are retained by that task during a certain time, and are released when this time elapses. After the task conclusion, the entity is routed to the next task or to the end of the station. The task formation in Arena is shown in Fig. 4. Fig. 5: Example of a closing assembly station module concatenating 2 teams. 4.5 End module After an entity had passed through all assembly stations it is routed to the end module (were its throughput time is computed) and disposed, see Fig. 6. Fig. 6: Process ending module. Fig. 4: Task formation. 4.4 Closing of the assembly station When an assembly station is closing, the leaving entities represent an assembled product or part of it. 5. CASE STUDY In this paper, the case study for software validation is the production line of rolling stock vehicles of the CP2000 type, manufactured at Bombardier Transportation Portugal SA, Amadora. The flow assembly line is producing a train of four different vehicles, as shown in Fig. 8.

5 A bottleneck is found in workstation 630 due to the tightness tests. 5.3 Working locations Fig. 8: The CP2000 train formation. 5.1 The CP2000 assembly line The assembly line is unique and processes all vehicles simultaneously at the same bit-rate. Each vehicle has its own assembly tasks but the assembly line responds well to the different solicitations. Eight workstations, with four locations each, form the flow assembly line to built the train, see Fig. 9. The team working locations vary depending on the workstation. Teams can have 1, 2 or 4 workers each. Working locations by station are determined according the following rules: Vehicle interior o Salon Max 2 teams of 2 workers o Cab (EA, EB) Max 1 team of 1 worker Vehicle exterior o Lateral Max 2 teams of 2 workers o Roof Max 2 teams of 2 workers o Under floor Max 2 teams of 2 workers The majority of working stations have three up to four teams (six up to eight workers) at a time. On Cab only one worker is allowed. Pré-Montagem Bogies Estação 620 Estação 625 Pré-Montagem Entrada 5.4 Production schedule Saída Fluxo de Trabalho Estação 650 Estação 630 Ensaio estanqueidade EA IA IB EB Estação 640 Fig. 9: Assembly line implemented. The development of the production schedule for this assembly line is a time consuming and difficult task since it is necessary to conciliate constraints and minimize resources, while a balance of works is required to reduce the idle time and cost of the process. Experts make this task manually with the know-how acquired in past projects and constant revision of the tasks. 5.2 Sequence of workstations The assembly line is composed by five workstations (numbered 620, 625, 630, 640 and 650) and the station 640 is divided into four independent stations. The workstation 650 does the concatenation of the four vehicles for train formation. The sequence of workstations is presented in Fig. 10. An Excel file containing the sheets: tasks; resources and scheduling stores the production schedule to which the simulation model is intended. The first sheet relates to the list of executable tasks, the second to the list of existing resources and the third to the list of the schedule planning to simulate. The database must follow a fixed format and must have the configuration shown in Fig. 11. Station 620 All vehicles Bit Rate 3 days Station 625 All vehicles Bit Rate 3 days Station 630 All vehicles Assembly tasks Duration 2 days Tightness test Duration 1 days Bit Rate 3 days Station 640 (1) Vehicles EB Station 640 (2) Vehicles IB Station 640 (3) Vehicles IA Station 640 (4) Vehicles EA Station 650 Vehicle concatenation EA Fig. 10: Sequence of working stations. Fig. 11: Production scheduling data sheets example.

6 5.5 Simulation The database is read by Arena, using the Visual Basic program to communicate between software. The created model is then simulated by Arena. First, possible scheduling errors are checked. If no errors are detected, Arena runs the simulation model. Utilizacao do(s) Recurso(s) ao longo do tempo Mec 16.rdat Mec 18.rdat Tempo (em Unidades de Tempo) Fig. 15: Comparison between the usages of two different resources. Idle time and tasks performed by the resources are shown. Fig. 12: Probability distribution for the through put time of the process studied The simulation produces a report that stores important information about the process. A MatLab program called Analyzer was developed to analyze the data stored during model creation, to identify delays due to predecessors and shared resources, to identify the critical path, to determine the confidence intervals and to identify the minimum resources needed. Figures 13, 14 and 15 present some results produced by Analyzer. The methodology proposed in this paper was compared to the methodology actually used in the factory. The software tool was able to reduce the production of a new schedule from 45 days using a manual solution to 8 hours. Also some scheduling errors in the implemented solution at Bombardier were found. The study shown that the major constraint of the process, i.e. the limitation to 5% of extra time to fulfill all tasks within the bit rate of 3 days (24 hours), can easily be achieved by small adjustments in the production schedule. 6. CONCLUSION This paper presents the development of a software tool to create simulation models of flow assembly lines. One example of a very complex model, the production line of rolling stock vehicles, showed the powerful capabilities of the tool, which is able to simulate a wide variety of assembly lines. Fig. 13: Statistical analysis for the throughput time of the studied process. The intention of this paper was to briefly discuss the simulation model creation process. The development process was not shown in detail, as it can be consulted in (Russo and Vicente, 2003). Future work will deal with other scheduling methods based on soft computing methods. REFERENCES Fig. 14: Confidence interval for the throughput time of the process studied. Hssain, A., (2000). Optimisation des Flux de Production. Methodes et Simulation, Éditions Dunod, Paris. Russo, J., Vicente R., (2003). Análise e Simulação de uma Linha de Montagem, Technical Report GCAR Lisboa, Portugal. Kelton, W., R. Sadowski, D. Sturrock (2003). Simulation with Arena, McGraw-Hill. Pinedo, M. (2002). Scheduling: Theory, Algorithms, and Systems, 2 nd Edition, Prentice Hall. Pinedo, M. and X. Chao (1999). Operations Scheduling with Applications in Manufacturing and Services, Irwin/McGraw Hill.