Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, 2006: 1032-1050 Sakarya University, Department of Industrial Engineering

A REVIEW OF SIMULATION TOOLS FOR ORGANIZATION AND PRODUCTION SYSTEMS

Halil Ibrahim Koruca *, Gültekin Özdemir **, Erdal Aydemir ***

* Süleyman Demirel Üniversitesi Mühendislik-Mimarlik Fak. Makina Müh. Böl. 32260 Isparta / Turkey Email : koruca@mmf.sdu.edu.tr Tel : ++90-246-211 1245 Fax :++90-246-237 0859 ** Süleyman Demirel Üniversitesi Mühendislik-Mimarlik Fak. Endüstri Müh. Böl. 32260 Isparta / Turkey *** Süleyman Demirel Üniversitesi Mühendislik-Mimarlik Fak. Makina Müh. Böl. 32260 Isparta / Turkey

* Assist. Prof. Dr.-Ing. Halil Ibrahim Koruca

** Assist. Prof. Dr. Gültekin Özdemir

*** Ind.-Eng. Erdal Aydemir

_____

* Corresponding author. Fax : ++90-246-2370859 E-mail: koruca@mmf.sdu.edu.tr

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A REVIEW OF SIMULATION TOOLS FOR ORGANIZATION AND PRODUCTION SYSTEMS

Abstract In this paper, we reviewed simulation tools in organization and production systems.

Simulation is an analysis tool for many applications and processes in these systems.

Then, we explained some examples of simulation applications in organization and

production systems in a literature review with recommendations. Finally, the most

commercial simulation tools (software) are compared based on features, modules,

performance measurement, data classification and type of simulation.

Keywords: Review, Simulation, Tools, Organizational Structures in Production

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1. Introduction

Today, companies must be more productive, flexible and produce high quality goods for customer and market requirements in the world market’s conditions. Therefore, every stage in organization & production systems can be used for continuous improvement. For this purpose, many tools, techniques, subsystems and systems can be used.

Simulation is a tool widely used in application, analysis, and development for organizations and production systems. Simulation can be defined in many ways. Some of them are given below:

“A Simulation is the imitation of the operation of a real-world process or system over time” [4]. “Simulation is a technique for using computer to imitate or simulate the operations of various kinds of real-world facilities or processes. In a simulation we use a computer to evaluate a model numerically, and data are gathered in order to estimate the desired true characteristics of the model” [6]. Simulation can be classified as static or dynamic, deterministic or stochastic, and discrete or continuous in general terms. Static simulation is also called Monte Carlo Simulation and is used to analyze systems by generating random variables that describe certain system characteristics, which are not changing over time. Dynamic simulation models, on the other hand, can be used to represent systems that change over time. If a model does not include random variables, it is called a deterministic model; and if it includes one and more random variables, it is called a stochastic model. If a model simulates various changes in a system at discrete event times, it is called discrete-event simulation, while if it simulates the changes over a continuous time interval, it is called continuous simulation model [4].

Simulation has also advantages, benefits and disadvantages. Summary of advantages and benefits of simulation are as follows:

- Perform experiments on models of present production systems [3], - Evaluation of the performance of expensive equipment, before purchase [3], - Prediction of the individual behavior of new production facilities [3], - Analysis of alternative organizational structures [1].

Main disadvantages of simulation are constrains and restricted abilities due to assumptions needed in modeling and analysis of real world systems. Some of the restrictions of simulation depend on vendors and output analyzers [3]. In addition, simulation is one of the most widely used tools in research as presented in various journals, and national and international symposiums or conferences all over the world. Several areas of simulation applications as seen in journals, conferences or symposiums are as follows [83]:

- Manufacturing Applications, - Human Systems, - Construction Engineering , - Business Processes, - Production Systems, - Forecasting, - Military Applications, - Project Management, - Logistics and Transportations, - Distribution Applications, - Medical Applications, - Mechanical Systems, - Chemical Systems, - Electrical & Electronic Systems, - Communication Systems , - Service Systems, - Internet Applications, - Reliability and Maintainability. All production systems have their own different parameters. The time and cost

must be reviewed for increasing efficiency and improvement of process in manufacturing systems. Therefore, analyzing system facilities becomes more important for

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manufacturers before a new investment [1]. In the next section, a literature review of simulation applications in processes of organizations and production systems is presented. 2. Organization and Production Systems Applications with Simulation Tools: A Literature Review A detailed literature review of simulation tools according to different system characteristics are given in this section. Table 1 shows several application areas with simulation while Table 2 shows new simulation software packages which are used as modeling and analysis tools in the literature.

Zülch and Grabel [7] defined pre-productive functions and organizational structure determined by functions and products. O’Kane et all [8] developed models in engine manufacturing process with materials flows, utilization, bottlenecks, man skills and usages, types and times by using WITNESS. Table 1. Simulation application in various production and manufacturing areas

Application areas of simulation* Source 1. An overview of simulation knowledge [1,9,47,48,77] 2. Applications with Genetic Algorithms [17,28,76] 3. Applications with Fuzzy [29,40] 4. Assembly Line Processes [18,25,28,50,79,80] 5. Automated Systems [27,36,77] 6. Business Processes and Management Systems [14,19,34,43,53,54,76] 7. Complex Production Systems [30,43] 8. Computer-aided Systems (CAD/CAM/CIM/CAE/CAPE) [33,49,77] 9. Crisis Management [73] 10. Data Modeling and Constraint Checking of Instances

(DAMOCCI) [16] 11. Finite Element Method (FEM) [35] 12. Flexible Manufacturing [20,31,49,58,71,97] 13. Human Systems and Organizational Structures [7,38,49,58,59,66,72,73,

74,75,80] 14. Hybrid Systems [22,37] 15. Intelligent Manufacturing Systems [52,77] 16. Just in Time Manufacturing and its Subsystems [26,64,65,67,68,69,70, 91,94,96,98] 17. Machine Reliabilities [10] 18. Material Handling Systems and Material Flows [8,36,42] 19. Mechatronic Systems [77] 20. Multiple-Machine Systems [69] 21. Network and Petri Nets Systems [24,78] 22. Predictive and Maintenance Control [13,45,85,86,89,95,99] 23. Production & Manufacturing Systems and Analysis [11,21,27,28,30,31,32,39,

40,41,44,46,48,55,56,57, 60,61,62,63, 88,90,92, 93,100,102,103]

24. Raw Materials [12] 25. Sensitivity Analysis [47] 26. Sequential Function Chart [15] 27. Supply Chain Systems [23,29,51,53,54] 28. Facility Layout and Location Analysis [87,101]

* In alphabetical order

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Law and McComas [9] explained how to use simulation in manufacturing systems for improving performance measurements and software, designing and analyzing experiments, statistical issues within simulation structures.

Kamrani et all [10] modified changes in the processing rate of machine tools, increases/decreases in machine reliability with a simulation model.

Khan et all [11] developed a generic software, called TEXSIM, for the textile production systems with simulation model analyze stochastic behavior of system performances and make a solution the real-life weaving problems. They used WITNESS with coding FORTRAN’77.

Moussa et all [12] offered ways to efficiently model case of a production line operations for raw material handling from stock to production lines.

Gharbia and Kenne [13] presented a two-level hierarchical control model for production and preventive maintenance control problem in multiple-machine systems. They used simulation-based statistical tools, experimental design and response surface methodology for optimization control policies and computed cost function and control parameters. Simulation modeling has been applied in various other production related areas, such as in designing and evaluating a flexible manufacturing cell (FMC) system by Savsar [97], in designing and evaluating a facility layout by Savsar [ 87,101], in maintenance modeling and analysis by Savsar [85,89,95,99], in just-in-time production control modeling and analysis by Savsar [91,94,96,98], and in manufacturing flow control and analysis by Savsar [88,90,92,93,100,102,103].

Tzafestas et all [14] used simulation of model-based predictive control (MBPC) as a decision-making tool for complex integrated production planning problems in stochastic environment and provided MBPC is a valuable tool between technical and economic process for testing and implementing productive strategic enterprise plans.

Music and Matko [15] represented the sequential control logic by SFC (Sequential Function Chart) in combined systems and used MATLAB-SIMULINK results for supporting control design in process control and manufacturing. They linked SIMULINK and SCADA packages in various graphic display formats.

Giannasi et all [16] used data modeling and constraint checking of instances (DAMOCCI) technique in a manufacturing environment and described discrete event simulation models in DAMOCCI’s approach. Verification of the process can be fully automated of error messages.

Ding et all [17] proposed for supplier selection for using a genetic search method and developed a multi-objective genetic algorithms is called MOGA. They selected purchasing costs, transportation costs, inventory costs and total backlogged demands as performance indicators for supplier selection in simulator with MOGA by searching the Pareto-front of multi-criteria solution area.

Jun et all [18] provided virtual assembly process planning (VAPP) as a productive and suitable tool for planning assembly process and used interactively in simulation environment similar to each process in real-life for assembly process planning.

Chatha and Weston [19] generated simulation models for supporting design and optimization of business processes and described art enterprise modeling techniques.

Madan et all [20] proposed a framework that is based on O-O modeling of five different simulation models fidelity for FMS and determined the most productive model fidelity for FMSs according to experimental results.

Duvivier et all [21] developed a generic simulation and optimization framework about reusability and modularity leads with discrete-continuous problems of planning and scheduling and are still improving this framework for multi-criteria scheduling methods. Rabelo et all [22] analyzed system dynamics (SD) simulation models with discrete-event simulation (DES) for hybrid systems and results are provided holistic analysis of the future stability of the enterprise.

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Mertins et all [23] generated analytical models for a behavior supply chain system. Their model is a DES by adequate technology.

Xu et all [24] developed comprehensive performance modeling for network-based decision systems. Their future work is to analyze and reconfigure the network for a specific algorithm, and then to predict the performance of an algorithm and design with a convenient algorithm for any network configuration.

Kim et all [25] involved simulation-based design (SBD) in shipbuilding system is called virtual assembly simulation system for shipbuilding (VASSS) and suggested for ship manufacturing. VASS can simulate operation of winch and erection of lock in a virtual dock.

Smet and Gelders [26] developed a simulation model which is based on “real production data” for Kanban Systems. Wiendahl and Breithaupt [27] presented a new dynamic production model for planning level which is based on techniques of control theory and results are confirmed with an automatic production control about flexibility and range of capacity. Fontanili et all [28] used flow simulation and GA to optimize management parameters in production, especially their study is about cost of an assembly line management with bypass workstations and produced only one type of product or some types of products are produced simultaneously. Petrovic [29] developed a supply chain (SC) by SCSIM is a special purpose simulation tool. The model can be two types which are fuzzy analytical model for optimal inventories for all levels and simulation model for testing performance of SC. This study mainly is a simulation of SCs behavior and evaluates performance in an environment of uncertain customer demands. Zulch et all [30] established a new simulation tool, called Osim, and this tool determined the quality and reliability of simulation results used detailed data so combined of variously detailed models from complex production systems. This study is supported an example of aluminum production application which will be demonstrated by evaluating logistical key data. Jing-Wen Li [31] used simulation with a model testing and improving performance for reducing setup/process time variability on the production of a job shop environment with demand-pull production control and offered to reduce setup/process time variability to obtain more efficiency in a cellular layout. Nazzal et all [32] presented a case study to reduce cost of cycle time in Agere Systems for wafer fabrication facilities by a simulation-based modeling and their economic investment analysis was for cycle time and operation parameters. Adamidis et all [33] used simulation for computer-aided engineering (CAE) in steel strip production and solved on grid-based model in fluid flow and metal forming process with the GRISSLi Coupling interface. Bley et all [34] presented a simulation model for model management system (MMS) with SimBASE tested in material flow simulation and suggested that efficiently use of the MMS is necessary for enterprise organizations. Gantar et all [35] developed a process on finite element method (FEM) by numerical simulation in sheet metal forming process and cost, benefit and time parameters are preformed in numerical simulation. Beschorner et all [36] simulated performance of an automated material handling system (AHMS) in MaxFlow theory and summed up realistic results from model. Mujber and the others [37] presented a simulation model for a new hybrid modeling to improve efficiency and reduce costs. Zulch et all [38] simulated different variants of human factors in production systems and related to objectives of enterprise and workers. Results show that reduction of mental and physical work will also reduce human errors.

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Pan et all [39] developed an object-oriented and Java-based generic plant growth simulator, called OWSimu, and its interface run on the Internet. OWSimu can be used mainly study of plant growth management systems. Cochran and Chen [40] used C++, SIMAN, and SIMPLE++ with discrete event simulation (DES) and fuzzy multi-criteria selection for triangular fuzzy numbers (TFN) for production system analysis.

Sandikci and Sabuncuoglu [41] presented analysis of transient period in non-terminating simulations like serial production lines and job-shop production systems. Process times, system size, reliability, buffer size and system load level are parameters for this study and they suggested a new convenient technique for these performance measures. Schweiker et all [42] used simulation as tool of material flow for optimizing and analyzing of automated laboratory equipment design and control planning reliability of process development. Fowler [43] presented a simulation model to manage complex and dynamic process of strategy by system modeling and simulation and developed strategy processes in business operations managements.

Zhou and Li [44] developed simulation of 3-D mold heat transfer of the TV panel pressing process and determined placement and dimensions of cooling system.

Rezg et all [45] simulated preventive maintenance and inventory control of a production line by using commercial software ProModel and combined with GAs optimization parameters to improve effectiveness of the system.

Couretas et all [46] developed a model with discrete event simulation (DES) based on manufacturing capacity analysis.

Kleijnen et all [47] presented an overview about sensitivity analysis with simulation experiments.

Tisza [48] presented an overview about numerical modeling and simulation in sheet metal forming process and its examples of industrial area.

Schlick et all [49] developed two-task networks for human-centered design and simulation of work processes in flexible manufacturing systems. The first one is for processes in Autonomous Production Cells (APC) and the other one is for processes in Computer Numerically Controlled (CNC) in manufacturing systems. Results show that APCs with a single-operator is 30% more productive in total task finished on time.

Selen and Ashayeri [50] defined a problem of “underbody front-subsassembly” in the automated welding unit in an automotive industry and developed a simulation model with experimental design for this problem. Cycle times, buffer size, mean times to failure and repair are used as input parameters for the simulation model.

Persson [51] used three different models for impact of different levels of detail in manufacturing systems. The first model was developed with high level of detail for all elements. The second model was developed with combination of some processes and the last model was only developed for main processes of all models which were simulated with experiments. This study is carried out in the supply chain of a mobile communications industry.

Acaccia et all [52] presented simulation-aids of intelligent manufacturing of textile industries. They modeled the set-ups and adaptive-schedules for clothing and used three layers of relational, generative and information in simulation. They used LCK-SIFIP code and object-oriented MODSIM III language.

Jain et all [53] modeled business processes and inventory control parameters within an animated simulation model for logistics in supply chain management.

Angerhofer and Angelides [54] reviewed systems dynamics modeling for design, re-engineering, decision making, problem solving and inventory of supply-chain management.

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Sadowski and Grabau [55] defined elements of success in simulation. They used simulation as a tool with gathering true data in experiments for successful practice.

Silva et all [56] presented a simulation study with limitations and problems in manufacturing processes. System usage rate and queue length were used as performance parameters.

Huda and Chung [57] simulated production scheduling with ARENA for food manufacturing systems and system time and resource utilization were used as performance measures. Alternative means were combined with ANOVA and Duncan multiple range test.

Savolainen [58] developed two simulation games in Finnish PRODEAL / Rough Modeling Project in computer integrated manufacturing systems. The first one was “Project management simulation game (PMSG)” for flexible manufacturing systems and the second one was “Business process simulation game (BPSG)” for mass production systems. As a result, simulation games were defined as productive and practical tool for improving human integrated manufacturing (HIM).

Zülch and Bogus [59] presented simulation models of planning for flexible working time. For this purpose, new simulation models were developed in working time models. They used FEMOS as a simulation tool in various working times and “downstream planning” of break configurations.

Mclean and Leong [60] explained benefits of simulation applications in manufacturing systems as a decision-making tool in strategic manufacturing.

Kiran et all [61] developed a model for a new international terminal at airport in Istanbul, Turkey. They used simulation tool of ProModel. The models performed to evaluate the passenger and aircraft flow in system capacities and to improve many operations, such as parking, check-in, packaging, serving and arriving.

Bapat and Swets [62] presented benefits and features of ARENA simulation packages for enterprise and flexible modeling and suggested several solutions.

Sivakumar and Chong [63] provided analysis of release control, set-up time, machine up time, cycle time and cycle time spread on variables of selected process with a simulation model. Their model is built on AutoModTM simulation tool and it helped for cycle time distribution, reworks, units per hour, batch process time, reliability and set-up time.

Garg et all [64] presented a simulation study of performance measures for the implementation of JIT manufacturing systems. The study involved Monte Carlo Simulation of case studies within one, two and multi-product production systems.

Rodrigues and Mackness [65] defined some outcomes from the simulation models: total production time, average waiting time and lead time, bottleneck utilization and input/output (I/O) control in JIT production systems.

Soares et all [66] presented technology support in social-technical evaluation and developed modeling of integrated manufacturing systems by Organizational Behavior simulation system (OBSim) and object-oriented simulation tool SIMPLE++.

Falay [67] analyzed MRP and JIT Production Systems with a SIMAN simulation software. This study compares supply times, system utilizations, inventories, lead times between MRP and JIT.

Ozkalaycioglu [68] presented a simulation study with FOXPRO for job-shop control and material flow in clothing manufacturing industry. He suggested a Kanban system for optimizing balance of production lines.

Baykoc [69] viewed performance of multi-products, multi-lines and multi-stages of a JIT production system in stochastic environment. SLAM II simulation language was used to model parameters of system utilization, waiting time and usage rate.

Ipek [70] presented Just-in-time system in push/pull production systems and developed two products with three workstations simulation scenarios in SIMAN.

Yigit [71] developed a simulation model for Flexible manufacturing systems with SIMAN to optimize the shortest process time scheduling.

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Zülch et all [72] generated a model with organizational modeling system FORM and a simulation tool FEMOS. They analyzed dynamic behavior and performance of the different structural organizations.

Koruca et all [73] discussed a simulation model for production and organization parameters with several crisis scenarios in crisis environment with alternative organizational structures.

Koruca et all [74] presented efficient structures in organizational structures for planning and flow charts of manufacturing with personnel characteristics in a bicycle manufacturing factory by using FEMOS simulation tool.

Koruca et all [75] presented a research of alternative organizational structures in textile industry by using ARENATM simulation software and developed some strategic, tactical and operational levels for a sewing unit.

Drstvensek et all [76] developed a simulation model based on technological database (TDB) with GAs for predicting and optimizing of production processes and performed into database management systems (DBMS).

Betram et all [77] presented an overview of vehicle modeling and simulation with mechatronic design in current automotive systems by using FASIM_C++ simulation environment.

Carullo et all [78] defined a Java-based simulation tool and timed Petri Nets (TPN) Designer for DES and performance analysis of TPN.

Mujber et all [79] presented an overview about virtual reality (VR) applications in manufacturing process and explained design, operations management, machining, manufacturing process and assembly and inspection.

Lasila et all [80] used simulation for human-centered operations in an assembly line of automotive manufacturer and defined human operations and behaviors with control measures for performances of assembly line.

Abdelmalek et al. [85] developed a simulation model to evaluate tool change policies in a flexible manufacturing system.

Savsar [86-103] has done extensive applications and modeling in simulation analysis related to production systems, just-in-time production control, maintenance modeling, and facility layout. Following is a list of related applications.

Savsar [86] discussed the effects of maintenance policies on the productivity of flexible manufacturing cells using simulation model for the analysis of FMC operations.

Savsar [87] analyzed the effects of random material flow volumes on layout flexibility using a simulation model for facility layout.

Almutawa et al. [88] presented a model and a simulation analysis procedure for optimum machine selection in multistage manufacturing systems.

Savsar [89] Evaluated and presented performance analysis of an FMC operating under different failure rates and maintenance policies.

Savsar and Youssef [90] developed an integrated simulation-neural network meta model for the designing and analysis of production flow lines.

Savsar and Choueiki [91] presented a neural network procedure incorporated into a simulation model for kanban allocation in JIT production control systems. Savsar [92] utilized simulation modeling procedure for dynamic resource allocation policies in transfer lines with serial duplicate stations. Savsar [93] analyzed the effects of scheduling policies on the performance of transfer lines with duplicate stations. Savsar [94] presented simulation analysis of a pull-push system for an electronic assembly line. Savsar [95] presented simulation analysis of maintenance policies in just-in-time production control systems. Savsar [96] analyzed the effects of kanban withdrawal policies and other factors on the performance of JIT systems using simulation modeling approach.

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Cogun and Savsar [97] presented performance and evaluation of a flexible manufacturing cell (FMC) by computer simulation. Savsar and Al-Jawini [98] presented simulation analysis of JIT systems and presented a simulation modeling procedure based on SIMAN simulation package. Savsar [99] presented a simulation model and the results for maintenance crew size determination by computer simulation. Savsar and Kilic [100] prsented a simulation model for multi-stage manufacturing systems to evaluate different tool changing policies. Savsar [101] presented a FORTRAN-based simulation model for flexible facility layout analysis. Savsar [102] presented a production line simulator, which evaluates performance of system design alternatives. Savsar and Biles [103] presented a model and results for simulation analysis of automated production flow lines.

Several ready packages have been developed to be utilized in simulation modeling and analysis of systems. They are listed in table 2. Table 2. Important Simulation tools in literature

Simulation Tools * Source 1. ARENATM [57, 62, 75] 2. AUTOMODTM [63] 3. C++TM [40] 4. Experimental Design [13, 47, 50, 51, 54, 55, 79] 5. GRISSLiTM [33] 6. FASIMTM _C++TM [77] 7. FEMOS [59, 72, 73, 74] 8. FORTRANTM [11] 9. FOXPROTM [68] 10. JAVATM [39, 78] 11. MATLABTM-SIMULINKTM – SCADA [15] 12. MODSIM IIITM and LCK-SIFIPTM [52] 13. OBSim [66] 14. OSim [30] 15. OWSimu [39] 16. PROMODELTM [45, 61] 17. SCSIM [29] 18. SIMANTM [40, 67, 70, 71] 19. SimBASE TM [34] 20. SIMPLE++TM [40, 66] 21. SLAM IITM [69] 22. Statistical Tools (ANOVA-Duncan-Monte Carlo, Num. ... ) [17, 23, 24, 31, 32, 35,

42, 48, 56, 57, 64, 65] 23. TEXSIM [11] 24. WITNESSTM [8, 11]

3. Simulation Software and Their Features In this section most commercial simulation tools (software) and their features are defined and compared based on modules, data classifications, performance measurement, type of simulation (discrete, continuous, geometric, etc. …), programming language and one of the most important manufacturing or application areas. A system is a collection of many entities as sub-systems. Systems simulation is modeled from real-life characteristics. A process simulation in organization & production

* In alphabetical order

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systems starts with development of a model which has time, cost, machines, man and work environment, management, and constrains. Table 3 shows development history of simulation languages [4] and packages until 1987. Animation modeling of real systems started in simulation after 1987. 2-dimentional modeling is improved from 1990s and 3-dimentional modeling started at the end of 1990s. In this study, three areas of simulation software are reviewed: Discrete-event simulation, continuous simulation and geometric (robotics) simulation software. A Discrete-event Simulation (DES) Software system simulates various changes only at a discrete event set of time scale. A Continuous Simulation (CS) Software system simulates various changes continuously set of time scale [4]. A Geometric (robotics) Simulation (GS) Software system simulates various changes in Computer-aided systems design and automated complex simulation set of time scale [1].Table 4 shows an overview of simulation software in applications of DES, CS and GS. Table 3. History of simulation languages and packages 1955 – 1960 FORTRAN 1961 – 1965 ALGOL, GPSS, GASP, SIMSCRIPT, SIMULA 1966 – 1970 GPSS/360, SIMSCRIPT II 1972 GPSS/NORDEN 1974 GASP IV 1977 GPSS/H 1979 SLAM II, SIMAN 1987 – …. 2D - 3D Modeling

Table 3. Main Simulation Software Systems in application DES Software CS Software GS Software ARENATM APROS TM eM-WORKPLACETM AUTOMOD TM MATLAB / SIMULINK TM GRASP2000 TM CORPORATE MODELLER TM PROSIM TM IGRIP TM e-FACTORY TM VISSIM TM MATLAB / SIMULINK TM eM-PLANT TM PROENGINEER TM ENTERPRISE DYNAMICS TM EXTEND TM FLEXSIM TM GOLDSIM TM IGRAFX TM PROCESS TM PROMODEL TM QUEST TM SHOWFLOW TM SIMUL8 TM WITNESS TM

APROS TM is a full-scale continuous and dynamic simulation tool for industrial processes, combustion power plants (APROS Combustion TM), nuclear power plants (APROS Nuclear TM), pulp and paper mills (APROS Paper TM). It is a data application as TCP/IP-based communication mechanism which is called APROS TM Communication Library (ACL). APROS TM has also a graphical design interface that is called Grades. APROS TM supports automation systems, design, testing, re-design, maintenance etc.

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applications. Users can develop models by using FORTRAN or C programming languages [104].

ARENATM is based on modules and modeling structures which are flowchart data objects. The ARENATM interface is a separate window in which users can edit, design and debug with Visual Basic Code and forms. Users can define procedures and functions in blocks with Visual Basic for Applications (VBA) [105]. An AUTOMOD TM model consists of queues, conveyors, stations, resources and performs flow of entities between separate operations which creates a logic file in the graphical window. AUTOMOD TM uses AutoStat TM for evaluating data output analyzer [106]. CORPORATE MODELLER TM is a set of logically chosen object types. Users can define business process, organization, location, data, application, technology. Models can be created in Casewise Repository [107]. e-FACTORY TM provides simulation of factory layout planning for efficiency and designs to improve layout models. e-FACTORY TM (VisFactory) works simultaneously with AutoCAD TM that is an architectural or mechanical design software and involves Factory CAD TM which has SDX (simulation data exchange) parameters: cycle time, scrap time, load/unload time, set-up time, breaktime etc. Factory Flow TM is a graphical material handlings system. Factory Mock-upTM, a variant of Factory Flow TM, is a 3-D visualizer for factory layout [108].

eM-PLANT TM is an object-oriented simulation language and has own simulation library. The model structure is easy and fast to develop applications. eM-PLANT TM uses 3-D solid modeling, CAD drawing and ActiveX TM based on internet applications. Furthermore, users can perform SQL TM databases ODBC (Open database Connectivity), Windows-Office TM applications and C TM programming language [109].

eM-WORKPLACETM (RoboCAD) designs simulation, optimization analysis in off-line production systems with complex, robotic and automated manufacturing cells. eM-WORKPLACETM is a 3-D graphical workstation. Users can integrate many of process types and also human operations. It uses especially 3-D interfaces of CATIA TM , Unigraphics TM , Pro/Engineer TM , and IDEAS TM formats [110].

ENTERPRISE DYNAMICS TM is an object-oriented software for modeling, simulation and control of dynamic processes and has product, source, sink, server and queue as elements which are called atoms in standard library. ENTERPRISE DYNAMICS

TM integrates with Excel TM grid interface [111]. EXTEND TM creates dynamic models with interactive and graphical architecture.

EXTEND TM integrates with Excel TM , ODBC, ActiveX TM, OLE (Object Linking And Embedding) and DLL (Dynamic-Link Library) [112].

FLEXSIM TM is an object-oriented simulation tool with graphical modeling environments and integrates with Excel TM , Word TM, C++ TM programming language and 3-D Studio TM, DXF, STL formats [113].

GOLDSIM TM is a software which is highly graphical and extensible, and able to quantitatively represent the inherent uncertainty in all complex systems such as, environmental, engineering, business and economical [114].

GRASP2000 TM has Windows TM style interface and menu system with icon toolbars, high level 3-D graphics with interactive control and built interactive 3-D solid modeler. Output of simulation models is in one of avi, VRML, Runtime and Grasp2000 Preview formats [115].

IGRAFX TM PROCESS TM offers to business processes with behaviors. Modeling techniques are included spreadsheet, user-friendly diagrams and drawn connecting activities. IGRAFX TM PROCESS TM integrates VBA TM programming and Visio TM

diagramming. The output publishing is chosen by HTML and JAVA TM [116].

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IGRIP TM is a physics-based robotic simulation software to model work cells quickly and graphically. It integrates 3-D interface with CATIA TM , Unigraphics TM , Pro/Engineer

TM , and IDEAS TM formats [117]. MATLAB TM is based on mathematical model of variety in control systems design

techniques for dynamic processes and robotics. SIMULINKTM develops detailed bloc diagrams of systems and builds more than 150 blocks. They integrates programming with CTM, C++TM, FortranTM and outputs with JavaTM, COMTM and ExcelTM [118].

PROENGINEER TM defines mechanical design and components then integrates them in a model. PROENGINEER TM generates digital model as CAD model with functional simulation to test and to optimize. It helps to deliver better products in less time. User can transfer data between PROENGINEER TM and MATLAB TM [119].

PROMODEL TM provides data and builds functions for large and complex system models in smaller sections/segments from Excel TM interface. It merges models and sub models in two functions. Hierarchical models can not be built by PROMODEL TM. C, C++ and Pascal languages can be used to construct models in PROMODEL TM [120].

QUEST TM requires level of high detail to improve and simulate the processes. It has the capability of 2-D or 3-D graphical modeling and planning by using Microsoft spreadsheet to obtain ergonomic workplace designs. The QUESTTM library includes geometric objects such as; buffers, machines, docks, materials, conveyors and material handling systems and elements such as, classes, groups, sub models, logical and geometrical environments. QUESTTM works with MES, ERP, MRP, PLC and analysis of production and planning systems by QUEST Express TM [121].

SHOWFLOW TM is a general purpose simulation tool. It can be used to model flow of materials, goods, people or information in business environments. SHOWFLOW TM can model general transporting, warehousing, automatically guided vehicles and retrieval systems. Data output reports are generated by Excel spreadsheet TM [122].

SIMUL8 TM is a drag and drop simulation tool on screen. Flow chart structure is supported by Visio TM, data access and coding macros is supported by VBA TM with Excel TM, programming is supported by VBA TM, C++ TM, Delphi TM and ActiveX TM. SDX TM exports objects and XML TM edits or imports data in SIMUL8 TM. SQL TM is used to data query and finally graphical flowcharts in process are built by İGrafx TM [123].

VISSIM TM is a simulation software for modeling and simulating of complex dynamic systems. VISSIM TM combines an intuitive drag & drop block diagram interface with a powerful simulation engine. VISSIM TM provides fast and accurate solutions for linear, nonlinear, continuous time, discrete time, varying time and hybrid system designs. VisSim/C-Code automatically generates ANSI C code for the model/controller/algorithm [124].

WITNESS TM provides easier modeling and uses blocks of icons of machines, labour, prts, conveyors, tanks, pipes etc. Photos, clips and solid or CAD drawings are 3-D images possible to use in model environment. WITNESS TM has control codes for input or output transfer with the other simulation tools. WITNESS TM links directly or indirectly with OLE TM, ODBC TM , spreadsheet Excel TM , ARENA TM , APROS TM , Factory CAD TM, MATLAB TM, IGRAFX TM PROCESS TM , PROENGINEER TM and SIMUL8 TM applications [125]. 4. Conclusions The answer to the question of: “How can we simulate human behaviors and work load in organizations to obtain more realistic results from simulation software?” is not easy. None of the simulation software in the market can give reasonable answer to this question.

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