Different Types of Simulation - Literature review Example

Types of simulation University Student Id Course Date Introduction Simulation is the imitation of a given operation which is a real-world system over a certain period. The process of simulating something it first needs a specified model to be introduced which should represent the major features or even behaviors of the selected abstract system (Brette, et al. 2007). Moreover, the model usually represents the real-world system itself, while on the other hand, the aspect of simulation represents the system operation over a particular period. However, the aspect of simulation is also applied in many contexts such as in the; safety engineering, training and in the context of testing. The literature review is addressing the various aspects of simulation and types of simulations while relating with the dynamic size problem. Background Simulations are important in mathematical modeling in many systems that are mostly used in gaining the necessary knowledge concerning a new technology. Organizations make use of simulations in making estimations of performance of the complex systems with the aim of establishing the analytical solutions. The mathematical models have been considered to be used as the standard subjects on the management of organizational operations. The major operation management problems areas can include scheduling, inventory control, forecasting, capacity planning, equipment maintenance, and quality control. For instance, McDonald chain of hotels has been making use of simulations in the process controlling its inventories (Vargas, 2009). Therefore, the mathematical modeling approach is considered important in controlling the inventory (Sedeño, et al. 2004). Inventory is critical in logical behavior in almost all manufacturing systems and operation management in organizations. The modern techniques that are important include just in time, material requirement planning and the time-based competition techniques. The techniques are crucial in the process of determining the sizes of the production lots at the moments the demand can be determined. Many organizations embrace simulation, as it has proved to be important in addressing the lot sizing problem as they have been helping in the lot size programming. For example, Toyota Company makes use of simulation in the process of controlling the production process of the company to meet the market demand (Robinson, Narayanan, and Sahin, 2009). Digital computerized simulations are used in the economic planning of different lot sizes, inventories, and workforce. A linear programming, dynamic and deterministic models are used in the process of obtaining the appropriate solutions for the lot sizing problem using both stochastic and dynamic (Buschkühl, et al. 2010). The common procedure involves a single item inventory control used to compare with the tests from linear programming models. The linear programming approach is considered to be the promising method for the economic planning of operation in the production process. However, simulation application is always restricted by various bounds, and the problems are usually known to be capacitated (Robinson, Narayanan, and Sahin, 2009). Moreover, the inventory levels are bounded variables hence they are referred to as the inventory model which is bounded. However, even though these problems are somehow related, the principles that characterize their optimal plans are usually different. However, the benefit which is accrued from the simulations is partially frustrated by the development of the simulation code which is somehow complicated. For instance, if an individual is willing to move to the modern architecture, the simulation of the event-driven increasingly becomes difficult to code because of the complications in the synchronization protocols. Therefore, it is always necessary to ensure that there is a balance between the merits and the demerits of the event-driven simulation (Vargas, 2009). There are various types of simulation that have been applicable in the process of addressing the lot sizing problem, which can include event-based simulation, continuous simulation, and time-based simulation. Event-based simulation The discrete events which are based on the simulation in most cases model the operations of a given system to form a sequence of discrete events within a period. However, every discrete event takes place at a specific instant of time, and it is a sign of change in the system. Moreover, between two consecutive events, it is always assumed that there is no single change which takes place and therefore the aspect simulation can directly take place from one event to another (Kleijnen, 2005). On the side of continuous simulation, the aspect of simulation proceeds continuously hence tracking the dynamic system over a given period, thus instead of being referred to as event-based it is referred as the activity based. During the continuous simulation, time is usually split into small slices of time, and the system state is also updated in agreement with the activities which are set to take place in each time slice. The current method for the simulation of the discrete events is the approach of the three phases, whereby the first phase is moving to the chronological event, while the second phase is executing various events which take place unconditionally at the same time. The last phase of the three-phase approach is the execution of all the events which conditionally take place at that time. Importantly, this approach is usually a refinement for the approach of event-based, whereby the simultaneous events are then ordered to utilize the computer resources more efficiently (Tarim and Kingsman, 2004). Furthermore, this approach is mostly used by the software packages for the commercial simulation. A common exercise of understanding how to come up with simulations for the discrete events is by introducing the queue model, such as the customers in a given bank being served by a teller machine. However, the aspect of simulation in most cases it maintains only a single list for the simulation events (Sedeño, et al. 2004). Also, some of the frameworks for the simulation usually provide time for each event to be specified regarding interval hence creating start time as well as the end time for every event. Importantly, simulation engines which are single-threaded mostly have a single current event, whereas the simulation engines that are multi-threaded in most cases they have current events which are multiple. However, in both simulations, there are significant problems related to the synchronization of these current events. Time-based simulation The time-based simulation is primarily aimed at developing the necessary stream of data from the personal detectors that provide the necessary data based on certain time frame. The introduction of time-based simulation took place during the digitization of data in organizations (Karimi, Ghomi and Wilson, 2003). However, the operations of the time-based simulations are usually triggered through its functions, where the standard method of doing analysis concerning the time-based simulation is always event-based. The simulation of the continuous data needs a combination of data from different time. The time based simulation is usually achieved with assistance from the individual handlers of the organizational data. It has been observed that in a case where the time taken by the succeeding events is small, time based simulation can be utilized. Continuous Simulation Continuous simulation is defined as the use of mathematical formulae with the aim of explaining the response of the simulated elements to certain conditions. It involves the behaviors for all the elements recognized, where they are governed by various equations (Beaubien and Baker, 2004). However, these equations are usually applied by the continuous simulator to come up with a graph that accurately reflects the reality of the given. The graphs in most cases reflect the various changes which take place in the system state respectively of the period, even though the same graph can also illustrate other kinds of relationships. Discussions The approach that was used helped in assessing the different types of simulations. The approach involved reviewing different literatures that gave enough insights concerning simulations. Ensuring a thorough review of the simulation related literatures is among the primary pros of the approach used. Also, the references collected provided the needed review that helped in understanding simulation. The references used in the literature review are enough to provide the necessary information concerning the simulations. However, there are some gaps in the literature concerning simulations that can include the limitations associated with the failure to identify the practical application of simulations in industries. Researchers have not given the necessary attention to the applicability of the simulations in the process of improving the performance in production. Simulation tests are done using data that is taken from the actual factory. However, the version of the simulations which is capacitated has been researched in deep details by a large number of authors. The authors while studying the above version have put into consideration the distinct assumptions concerning the functions, boundaries and also the cost of the production quantities (Jans and Degraeve, 2007). In contrary to this, the inventory model which is bounded is found in a small number of references to the literature. Therefore, new properties have been introduced to facilitate the determination of the optimal plans for the lot size problem concerning the storage capacity. Conclusion While developing the simulation model, individuals should make sure that this model is properly implemented and also should ensure that it represents the real system. After the completion of developing the model, the next step is usually to deal with the issues of the transient removal as well as the issues of the stopping criterion. However, since the simulations of the discrete events do not simulate each time slice, they are more likely to run faster as compared to the continuous simulation. In the future, there is the need for research to be conducted to assess the application of stimulations in production. References Beaubien, J. M., & Baker, D. P. (2004). The use of simulation for training teamwork skills in health care: how low can you go?. Quality and safety in health care, 13(suppl 1), i51-i56. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., ... & Zirpe, M. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience, 23(3), 349-398. Buschkühl, L., Sahling, F., Helber, S., & Tempelmeier, H. (2010). Dynamic capacitated lot-sizing problems: a classification and review of solution approaches. Or Spectrum, 32(2), 231-261. Jans, R., & Degraeve, Z. (2007). Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches. European Journal of Operational Research, 177(3), 1855-1875. Karimi, B., Ghomi, S. F., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378. Kleijnen, J. P. (2005). Supply chain simulation tools and techniques: a survey. International Journal of Simulation and Process Modelling, 1(1-2), 82-89. Robinson, P., Narayanan, A., & Sahin, F. (2009). Coordinated deterministic dynamic demand lot-sizing problem: A review of models and algorithms. Omega, 37(1), 3-15. Sedeño-Noda, A., Gutiérrez, J., Abdul-Jalbar, B., & Sicilia, J. (2004). An O (T log T) algorithm for the dynamic lot size problem with limited storage and linear costs. Computational Optimization and Applications, 28(3), 311-323. Tarim, S. A., & Kingsman, B. G. (2004). The stochastic dynamic production/inventory lot-sizing problem with service-level constraints. International Journal of Production Economics, 88(1), 105-119. Vargas, V. (2009). An optimal solution for the stochastic version of the Wagner–Whitin dynamic lot-size model. European Journal of Operational Research, 198(2), 447-451. Read More

However, simulation application is always restricted by various bounds, and the problems are usually known to be capacitated (Robinson, Narayanan, and Sahin, 2009). Moreover, the inventory levels are bounded variables hence they are referred to as the inventory model which is bounded. However, even though these problems are somehow related, the principles that characterize their optimal plans are usually different. However, the benefit which is accrued from the simulations is partially frustrated by the development of the simulation code which is somehow complicated.

For instance, if an individual is willing to move to the modern architecture, the simulation of the event-driven increasingly becomes difficult to code because of the complications in the synchronization protocols. Therefore, it is always necessary to ensure that there is a balance between the merits and the demerits of the event-driven simulation (Vargas, 2009). There are various types of simulation that have been applicable in the process of addressing the lot sizing problem, which can include event-based simulation, continuous simulation, and time-based simulation.

Event-based simulation The discrete events which are based on the simulation in most cases model the operations of a given system to form a sequence of discrete events within a period. However, every discrete event takes place at a specific instant of time, and it is a sign of change in the system. Moreover, between two consecutive events, it is always assumed that there is no single change which takes place and therefore the aspect simulation can directly take place from one event to another (Kleijnen, 2005).

On the side of continuous simulation, the aspect of simulation proceeds continuously hence tracking the dynamic system over a given period, thus instead of being referred to as event-based it is referred as the activity based. During the continuous simulation, time is usually split into small slices of time, and the system state is also updated in agreement with the activities which are set to take place in each time slice. The current method for the simulation of the discrete events is the approach of the three phases, whereby the first phase is moving to the chronological event, while the second phase is executing various events which take place unconditionally at the same time.

The last phase of the three-phase approach is the execution of all the events which conditionally take place at that time. Importantly, this approach is usually a refinement for the approach of event-based, whereby the simultaneous events are then ordered to utilize the computer resources more efficiently (Tarim and Kingsman, 2004). Furthermore, this approach is mostly used by the software packages for the commercial simulation. A common exercise of understanding how to come up with simulations for the discrete events is by introducing the queue model, such as the customers in a given bank being served by a teller machine.

However, the aspect of simulation in most cases it maintains only a single list for the simulation events (Sedeño, et al. 2004). Also, some of the frameworks for the simulation usually provide time for each event to be specified regarding interval hence creating start time as well as the end time for every event. Importantly, simulation engines which are single-threaded mostly have a single current event, whereas the simulation engines that are multi-threaded in most cases they have current events which are multiple.

However, in both simulations, there are significant problems related to the synchronization of these current events. Time-based simulation The time-based simulation is primarily aimed at developing the necessary stream of data from the personal detectors that provide the necessary data based on certain time frame. The introduction of time-based simulation took place during the digitization of data in organizations (Karimi, Ghomi and Wilson, 2003). However, the operations of the time-based simulations are usually triggered through its functions, where the standard method of doing analysis concerning the time-based simulation is always event-based.

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