SBSC in Flexible Manufacturing Environments--Details

Simulation-based Scheduling and Control in Flexible Manufacturing Environments is an on-going research project of the Texas A&M Computer Aided Manufacturing Laboratory (TAMCAM). A simulation-based control system developed by TAMCAM is used as a research tool to study the advantages and disadvantages of on-line simulation for scheduling and control of flexible manufacturing systems.

This web page and its associated links describe the methodology for the design and implementation of a simulation-based control system. Detailed information describing the structure of the TAMCAM Simulation-based Control System (TSCS), the associated software tools used in the design, development, and implementation of the control system, and a laboratory implementation of the system is presented. This control system was presented as a State of the Art Review at the 1996 Winter Simulation Conference in Coronado, California. Excerpts from this presentation are also presented below. The material may be viewed on-line or may be downloaded for viewing. Two on-line versions are available, one in HTML format and the other a Microsoft Word document. Two downloadable versions are available, one as a Microsoft Word document and the other in postscript format.

If you would like further information regarding simulation-based scheduling and control beyond what is provided on this web page and its associated links, we refer you to the TAMCAM Contact Persons section of this page.

Simulation-based Scheduling and Control in Flexible Manufacturing Environments is an on-going research project of the Texas A&M Computer Aided Manufacturing Laboratory (TAMCAM). Simulation-based scheduling and control integrates simulation technology, information system technology, and a manufacturing system’s execution level controllers for automated and manual equipment. A simulation-based control system developed by TAMCAM is used as a research tool to study the advantages and disadvantages of on-line simulation for scheduling and control of flexible manufacturing systems.

This web page and its associated links describes the methodology for the design and implementation of a simulation-based control system, the structure of the TAMCAM Simulation-based Control System (TSCS), the associated software tools used in the design, development, and implementation of the control system, and a laboratory implementation of the system. The control system described in this web page is designed to work with mostly automated systems but also facilitates the use of human operators.

Simulation is based on a logical model of how a series of processes interact combined with statistical and deterministic information about the individual processes. Simulation is commonly used to gain insight into the performance of a flexible manufacturing system. Simulation can often capture and describe the complex interactions within a particular flexible manufacturing system where analytical methods fail (Stecke and Solberg 1981; Erickson et al. 1987; Wu and Wysk 1989).

There is a growing interest in manufacturing, specifically in the area of real-time, "intelligent" shop floor control, to use simulation to predict the future impact of short-term decisions on the performance of the manufacturing system. Traditionally, simulation has been used to examine long-term system performance for planning and design purposes. These models are usually "throw-away" models because they are seldom used after the initial plans or designs of the project have been finalized (Thompson 1994). More significantly, the simulation is developed and performed off-line using custom software packages/languages with limited direct access to the data generated by the production system (Drake and Smith 1996). Traditional simulation is limited in its ability to use current system information to predict the behavior of the manufacturing system. A primary reason for this inflexibility is that the input data of the simulation are gathered and analyzed outside the simulation environment. More importantly, the simulation system cannot communicate automatically with the Manufacturing Execution System (MES), which is responsible for gathering shop floor status information. In addition, the simulation system cannot communicate directly with the manufacturing system's execution level controllers. Because of these limitations, traditional simulation is not necessarily the best means by which to address the impact of short-term decisions in operational planning, scheduling, and control of a manufacturing system. In response to these problems, there has been a move from the traditional simulation approach to an on-line simulation framework. With on-line simulation the most current system information can be used to accurately predict system performance and to develop future planning, scheduling, and control alternatives. The on-line simulation framework is a potentially powerful tool in the development and implementation of a simulation-based shop floor control system. A simulation-based control system integrates simulation technology with information system technology and a manufacturing system's execution level controllers for automated and manual equipment.

Flexible manufacturing environments are well suited to the study of simulation-based scheduling and control. The flexible manufacturing environment is characterized by small-to-medium production volume, high-variety of products, and dynamic production mix. Examples include the aerospace industry, custom electronics (ASIC) manufacturing, and specialty machining (Caterpillar Tractors, for example).

Figure 1 shows the example flexible manufacturing system (FMS) that is used to describe the TAMCAM Simulation-based Control System (TSCS). This system is located within the TAMCAM Computer Aided Manufacturing (TAMCAM) lab. The system consists of three CNC milling machines, one CNC turning center, two industrial robots, and an automated cart based conveyor system. In addition to the automated equipment, human operators are used to load and unload some machines and perform assembly and inspection tasks.

The TAMCAM shop floor control system is a two-level control system consisting of a decision making module and an execution module. A well-defined interface between the two modules allows the independent development of each module. The TAMCAM shop floor control system is illustrated in Figure 2. The shop floor control system in this environment is responsible for converting the production requirements into specific instructions for the individual pieces of equipment and interacting with the equipment to implement the instructions (Wysk and Smith 1995).

The decision making module is responsible for routing parts through the system and setting priorities between individual parts at the processing stations. The TAMCAM shop floor control system uses a discrete event simulation, developed using System Modeling's Siman/Arena software, as the decision making function. The execution module is a two-level hierarchical control system. Each piece of equipment has an associated equipment level controller. These controllers provide the interface between the shop floor control system and the device-specific controller provided by the equipment vendor. Equipment instructions are in the form of numerical (NC) instructions or the equivalent instructions that can be interpreted by the vendor-supplied machine controllers. The objective of the shop floor control system is to identify a "good" sequence of equipment instructions in terms of the system performance. Evaluation criteria include typical performance measures such as batch makespan, cycle time, and due date-based measures.

The production requirements are provided by the higher level planning system (e.g., the MRP system) and are in the form of part process plans. In the TAMCAM shop floor control system a typical MRP system is emulated using a Microsoft Access database as the manufacturing information system. Production orders can be input to the shop floor control system, specifically the decision making module, at any time by specifying a part type (identifying the process plan), quantity, and due date for the order. Open Database Connectivity (ODBC) provides the means for the decision making module to directly access the manufacturing information system. TCP/IP sockets are used for communication between the decision making and execution modules as well as between the two levels of the execution system. In addition to part process plans, the database includes information about part operations, part routings, resources, and works

A key concept for development of flexible control systems is the explicit separation of decision making functions from execution functions in the control system (Jones and Saleh, 1990; Smith et al., 1994). Under this paradigm, the execution functions provide the device-level control of the equipment, whereas the decision making functions schedule the tasks of the execution functions such that the production requirements are met. As a result, the execution functions are dependent only on the physical configuration of the equipment and not on the specific parts and production volumes currently required. Similarly, the decision making functions are not affected by the details required to exercise low-level control over the physical equipment. The goal is to provide a well defined interface between the decision making and execution systems so that they can be developed independently and "plugged" together to meet current needs.

In the TAMCAM control system, the explicit separation is provided by a message queue mechanism between the simulation and the execution system. This message queue mechanism is illustrated in Figure 3. The simulation-based decision maker sends task request messages to the execution system through the message queue. Once the execution system has completed the task, it returns a completion message to the decision maker through the message queue. Under this paradigm, neither system knows how the other works (or even whether it is manual or automatic). That is, the decision maker does not know or care how execution tasks are performed, and the execution system does not know or care how decisions are made as to task sequencing. As a result, either system can be modified or even replaced without affecting the other.

The task initiation message contains the operation and the part ID. All tasks are sent only to free resources that have the parts required to complete a task in their part queue or buffers. This restriction avoids including queuing times of parts waiting for resources or resources waiting for parts in the processing time. Queuing times are excluded from the model because queuing time arises primarily from the scheduling choices developed by the decision maker.

The task completion message contains the outcome of the task and the active processing time required for the task. The unique identifier for the task completion message is the part number and workstation. The basic active processing time includes set up time, down time, and transportation time within the workstation. For particular resources where a more detailed description of the system is required, the active processing time is split into subgroups including down time, reaction time, setup time and activity time for particular sub-tasks (Table 1). These additional attributes are only required if needed by the simulation.

	Initiation Sequence	Completion Messages
Require Information	Part Number, Workstation	Part Number, Workstation, Active Processing Time
Optional Information	Due Date, Special Instructions, Priority	Setup Time, Reaction Time, Sub-Operation, Processing Times, Success of Operation, Additional Routing Based on the Operation.

The decision maker is responsible for routing parts through the system and setting priorities between individual parts at the processing stations. In the example system, these decisions translate to prioritizing the loading of parts from the load/unload station onto the Shuttleworth conveyor system and setting the queue priorities for each of the ProLights, the Hercus, the assembler, and the inspector. The Shuttleworth conveyor itself is responsible for tracking empty pallets, avoiding congestion, and determining the specific routes that parts take from a source port to a destination port. The structure of the material transport controller is described by Edlabdakar (1995). As such, the decision maker is does not make decisions regarding specific part routing on the conveyor. Instead, it simply instructs the Shuttleworth controller to move parts from a source port to a destination port, and the conveyor controller decides on the path and sets priorities between carts already on the system.

An alternative design would be to have the decision maker explicitly track all pallet movement between ports on the conveyor system. However, this would also mandate that the decision maker direct the flow of empty pallets. In the example system, this presents an unnecessary burden on the decision maker. However, in a more complex system, this would be a natural application for hierarchical simulation. Using hierarchical simulation, the equipment level material transport controller is also based on simulation, and can be used to predict specific transport times.

The decision making module is implemented using a modified version of the SIMAN simulation language. The modification allows delay-type blocks to function either in simulation mode or in real-time control mode. In simulation mode, entities coming into the delay block are held for the specified delay time. In real-time control mode, the task message associated with the delay block is sent to the execution function and the entity waits at the delay block until a completion message is received from the execution function. The ability to run in either simulation model or real-time mode allows the same simulation to be used as a predictive analysis tool and a real-time controller (Smith et al., 1994). The SIMAN modifications for implementing real-time control have now been included in the commercial versions of the Arena simulation package.

Simulation-based control uses simulation but not in the traditional sense. It requires additional detail to describe the system. This detail describes the part flow through the system, resource acquisition, part locations, and parameters for the execution system. Simulation-based control is data-driven through external data sources. Traditional simulation model are concerned primary with material processing and sometimes material transport. A simulation acting as a decision maker must be concerned with material processing, transport, and material handling.

The execution system is based on the two-level hierarchical control architecture described by Smith et al. (1994). Each piece of equipment (MP, MH, and MT) has an associated equipment level controller. These controllers provide the interface between the shop floor control system and the device-specific controller provided by the equipment vendor. The second level, or workstation controller, is responsible for coordinating the activities between the equipment level controllers. For example, the decision maker in the example system might request that the Eshed robot move a part from the Shuttleworth conveyor port 10 to the ProLight Mill (represented by the arc between nodes 10 and 4 in the graph shown in Figure 2). The workstation level controller would receive the request and synchronize the actions of the Shuttleworth, Eshed, and ProLight required to implement this task.

The equipment and workstation level controllers are based on the message-based part state graph (MPSG) model described by Smith and Joshi (1993). The MPSG model provides a formal language with which to describe the processing protocol between distributed controllers in a shop floor control system. The processing protocol is essentially the sequence of messages that are passed between controllers and the tasks that individual pieces of equipment perform to implement higher level tasks. A significant advantage of using the MPSG model is that a significant portion of the execution module for a controller can be automatically generated from a textual representation of the MPSG.

Figure ?? shows a flowchart describing the creating of MPSG-based controllers. The input file input.m is the textual representation of the MPSG. This file is generated based on the shop floor controller class described by Smith and Joshi (1995) and the machine type. The input file is processed by the MPSG Builder which crates the controller-specific C++ code to implement the processing protocol (input.cpp and input.h). The task action file (tasks.cpp) is the machine-specific code to implement the machine actions (e.g., downloading and executing an NC file, moving a robot arm, opening and closing a robot’s gripper, etc.). The majority of this code must be supplied by the equipment vendor or developed based on vendor-supplied specifications. These source files are compiled to create a dynamic link library (input.dll) with the processing protocol interpreter. The dynamic link library is used with the generic controller (controller.exe) to form an operational controller. The code required for data communications between controllers is part of the base controller class and is implemented in the generic controller. This class includes code for serial communication with the machine and TCP/IP-based communication with the other controllers in the control system.

The information system is spilt into a description of the manufacturing system and a historic record of the on-line simulation that contains data about the actual production process.

The description of the manufacturing system contains a list of parts (i.e., part types), a list of specific parts to be produced with due dates, a list of resources for the production process with feasible messages for each resource, routings for the parts, alternative routings for the parts, and any additional information required to develop the simulation. Many of these inputs including the current production schedule, due dates, part routings, and resource relationships are deterministic and can be directly accessed from a relational database using SQL as suggested by Drake (1996). However, several key inputs to the proposed simulation model, including distribution of the process times, fallout rates, and shift schedules, are stochastic and vary for different part types and equipment stations and cannot be directly accessed from a static database.

This information system can access the simulation to run the simulation in look-ahead mode. The look-ahead simulation tool can predict whether a given production schedule is feasible by using the simulation as a query tool. The feasibility can be expressed as either the probability of completing a set of tasks within a given period of time or the entire estimated cumulative density function for completing the task as a function of time. Using this ability, multiple simulations can be used to improve the scheduling of resources by changing the operating rules (Tautuo and Pierreval 1995). The instruction for on-line simulation tools for manufacturing systems should resemble commands commonly used in database systems. A method for achieving this goal is to have the commands as an extension of SQL (Balasubramanian and Tuzhilin 1996). Even though our model does not follow the command set proposed by Balasubramanian and Tuzhilin, the TSCS model tries where possible to use a command structure that is easily understood to database and industrial/manufacturing engineering professionals.

The results of the real-time simulation execution are stored in a database. The real-time results are the data on how the system actually operates. The combination of a MES system with a database system is extremely common with most vendors using Open Database Connectivity (ODBC) software or a similar methodologies to connect controllers to database systems. These systems warehouse this information for many purposes including statistical quality control.

The TSCS database system stores the results of operations including process times and fallout rate (Figure ?). The user can exclude data points from the estimation of processing times for statistical quality control or look-ahead simulation. The primary reasons for excluding data are that the observations are influenced by a problem where the underlying cause is resolved and will not reoccur or that the data is in error.

From the database system, a data analysis tool estimates the distribution for process times and fallout rates at each workstation for each part type. These estimates are stored and used in the simulation model. The default distribution for process times is the gamma distribution. If the sample is not sufficiently large, then the estimation of the process times is based on a grouping of parts with similar characteristics. The estimation procedure identifies extreme values and allows the user to delete these values from the estimation.

The results of the simulated system in look-ahead mode can also be compared to the actual flow time for the system in real time. A difference between the simulated system in real time and the estimated performance in look-ahead mode could reveal problems in the simulation or the actual system that occur in a series of stations.

An important element to make on-line simulation effective is the ability to do retrospective studies. To model the past, the TAMCAM Simulation Control System (TSCS) writes the transitions between system states to a temporal database for later analysis. Queries on the database provide estimates of process times, rework, and fallout rates. These estimates for the distribution of the time required to do a task drive the simulation model as inputs in look-ahead mode.

All messages sent within the system about task initiation and completion are also sent to the information system. Only control messages sent between Arena and a workstation are recorded in the database. Messages sent within the workstation for equipment coordination by the BigExec are not recorded in the database. The message list contains the message, the time when the message was sent, the message source and message destination. These messages are a complete description of the task that occurred in the system.

The message structure is constructed such that operations only occur on one part where all resources employed in the operation work on the part for the duration of the operation. This restriction avoids estimating process times and fall-out rates from processes that are mixed together. Avoiding multiple processes based on a single message facilitates direct estimation of process times from the messages being sent in the system.

An example of a mixed process to be avoided is a worker loading a machine and then the worker being released after the completion of the loading task before the completion of the machining process. This process is modeled as two tasks since the number of resources required by the part changes during the operation. If the worker is seized for the entire period of time then the operation would be modeled as one task in the simulation.

Based on the messages, processing times and fallout rates can be estimated for different operations. This information is the input data to the simulation model in emulation mode. Also, this information is used for Statistical Quality Control of the processes within the lab.

The TAMCAM Scheduling and Control System consists a simulation-based controller developed in Arena, a message router and client controllers developed in Microsoft Visual C++, and an external database system developed in Microsoft Access. The simulation-based controller is built in Arena using the real-time options. The Arena simulation model also uses a user-coded dynamic link library (DLL) written in Microsoft Visual C++ to provide the implementation-specific communication functions required by the router. All connections within the real-time system are implemented using the TCP/IP protocols. The connections within the forecast system are implemented with Database Access Objects (DAO).

The ability to estimate statistical distribution directly from the information system is critical to this model. For implementing real-time simulation in an actual manufacturing environment, the key information challenge is insuring semantic correctness in a distributed database system. For information systems that are loosely federated together, the accessing of data may include changes to the data structure, attribute naming, and data schema. An example of these differences is extracting processing times from a database system that stores the processing time as an attribute in a table and a system that stores processing times as the difference between start time and completion time in a series of records. The major obstacle to this integration is not the ability to communicate between different data sources that is standardized with Open Database Connectivity (ODBC), Data Access Objects (DAO), and embedded SQL; the challenge is identifying the ways data are stored and having a clear understanding of what the data implies about the manufacturing process. For data quality in on-line simulation, the information must be accurate and represent what the model thinks it represents.

For the TAMCAM Simulation-Based Control System, these information system design problems were solved by making the information system meet the needs of the simulation model. For a commercial implementation of these concepts, substantial commitment for standardization of system states would be required. Based on the amount of resources spent on the simulation of manufacturing processes compared to relational database systems and MRP systems, the majority of the flexibility must come from the simulation package. This flexibility includes the following:

With the release of Systems Modeling's Arena 3.0 with Microsoft Visual Basic for Applications, the power and flexibility of performing simulation-based scheduling and control is greatly enhanced. Visual Basic for Applications allows direct access to data stored in external databases using Data Access Objects (DAOs) and Open Database Connectivity (ODBC). Using Arena 3.0 with Visual Basic for Applications is expected to reduce the simulation development time and the cost of implementing the concept of simulation-based scheduling and control.

Another feature in Arena 3.0 is object linking and embedding (OLE). It provides the flexibility to call an Arena simulation from an external program such as a manufacturing execution system or material requirement planning system using the Visual Basic for Applications function. OLE automation allows applications to exchange data, control each other, and automate actions in themselves

The concept of simulation-based scheduling and control is further enhanced by the ability to generation the simulation model directly from an external data source. The simulation model can be automatically built and changed based on the data stored in the external data source. By reducing the difficulty in generating simulation models, a simulation is more likely to be reused for optimizing the system under study and for identifying current and potential problems. This flexibility in developing simulation models directly from corporate information systems reduces the cost of generating simulations and should increase the use of simulation for short term planning and scheduling decision making.

Arena RT provides a set of extensions to the Arena simulation system for communicating with external clients, initiating and reacting to external actions, and synchronizing with external clocks. Arena RT is designed such that the same model can be used for simulation as well as control.

This web page and its associated links has presented the methodology for the design and implementation of a simulation-based control system. Detailed information describing the structure of the TAMCAM Simulation-based Control System (TSCS), the associated software tools used in the design, development, and implementation of the control system, and a laboratory implementation of the system was presented.

There is a growing interest in manufacturing, specifically in the area of real-time, "intelligent" shop floor control, to use simulation to predict the future impact of short-term decisions on the performance of the manufacturing system. As flexible and agile manufacturing become prevalent, the ability to describe the short-term future performance of a manufacturing system will become critical. Traditional simulation is not necessarily the best means by which to address the impact of short-term decisions in operational planning, scheduling, and control of these systems. There has been a move to use an on-line simulation framework. This framework is a potentially powerful tool in the development and implementation of a simulation-based scheduling and control system. A simulation-based scheduling and control system integrates simulation technology, information system technology, and execution level controllers for automated and manual equipment. With on-line simulation the most current system information can be used to predict system performance and to develop short-term planning, scheduling, and control alternatives.

Simulation-based scheduling and control simplifies and shortens the development cycle for shop floor control systems. It can be used to define consistent requirements between different modeling levels in a hierarchical modeling environment and to develop a framework to maintain consistency between different levels. Through our research, we have been able to illustrate the ability to change the control strategy in response to changes in the environment. We have also demonstrated the technical feasibility and utility of using on-line simulation for scheduling and control in flexible manufacturing environments. On-line simulation for scheduling and control of flexible manufacturing systems is a promising framework for the study and optimization of the dynamic characteristics of a flexible manufacturing system.Advances in simulation technology and information system technology will make simulation-based scheduling and control available for wide-spread use.

Simulation-Based Scheduling and Control in Flexible Manufacturing Environments is a State of the Art Review. It was presented at the 1996 Winter Simulation Conference in Coronado, California. The Microsoft PowerPoint presentation may be viewed on-line or may be downloaded for viewing.

Two on-line versions are available. The first version uses a timed, animated format which does not require the user to advance the slides. The second version uses a user-controlled format which requires the user to advance the slides.

This version requires Windows and the PowerPoint Animation Player, which can be downloaded directly from Microsoft from a link on our Web site when you select the animated format for viewing. This plug-in is distributed royalty-free for viewing PowerPoint presentations on the Internet. It takes approximately 1 minute to download and install the player.

When viewing the presentation, the user has the option of using the left mouse button to advance the slides faster than the pre-set timing. The user may also use the right mouse button to navigate to previous slides and to view the presentation full screen or full window. To return to Internet Explorer from full screen or window viewing, press the ESC key.

This version can be viewed through any Web browser on any platform. A completely graphical version of the presentation as well as a text-only HTML version are available for viewing. Control buttons in the graphical version are used to navigate between slides and to switch between the graphical version and the text-only version. In the text-only version, links are provided to navigate between slides and to switch between the two versions.

The downloaded presentation file may be viewed through Microsoft PowerPoint for Windows 95, Version 7.0 or higher.

The article appearing in The Proceedings of the 1996 Winter Simulation Conference may be viewed on-line or may be downloaded for viewing. Two on-line versions are available, one in HTML format and the other a Microsoft Word document. Two downloadable versions are available, one as a Microsoft Word document and the other in postscript format.

If you would like additional information regarding simulation-based scheduling and control beyond what is provided through this web page and its associated links, please contact either of the following individuals.

Jeffrey S. Smith
Assistant Professor
Department of Industrial Engineering
Texas A&M University
239A Zachry Engineering
College Station, TX 77845-3131
Phone : (409) 845-4335
Fax :(409) 845-7079
Email: jsmith@tamu.edu

Brett A. Peters
Assistant Professor
Department of Industrial Engineering
Texas A&M University
239B Zachry Engineering
College Station, TX 77845-3131
Phone: (409) 845-3574
Fax: (409) 847-9005
Email: bpeters@tamu.edu

This page maintained by Cynthia LaJimodiere.
The contents of this page were last modified on Monday, 21 April 1997.