The term Arena as used in software and technology science represents an object-oriented, hierarchized, rapid, and broad advancement in simulation technology that enables the use of enterprise-wide simulation. It is extendible and provides a complete simulation environment that supports all the base steps in a study [1]. The Arena simulation branch is an inclusive and relatively new yet used system that includes all phases of simulation projects, from the input data analysis to its simulated output data [2]. Simulation, like science itself, is the most accurate imitation of a real-world process or system by modeling an experience derived from the characteristics or behaviors of a selected physical or abstract system [3]. The model is used to give or create simulations that act out its “original” form. Simulation is used widely today in sectors like entertainment (Virtual Reality), education, safety engineering, and training (e.g., pilots that use the software to learn), among others [3]. Individuals use simulation to gain knowledge and insight into the functioning and operation of human-driven systems and the manufacturing of artificially intelligent devices. Simulation can also be used as a backup for a natural system when it is indisposed or not available. Furthermore, simulation is used by people to produce alternatives and changes to a system.
This case study was obtained from the IEEE database and modeled by Majeed A. Ghaleb, Umar S. Suryahatmaja, and Ibrahim Alharkan from the King Saud University, Riyadh, KSA
The case study chosen included the making of a simulation model for a university’s main restaurant. It is to be a queuing system to help better arrange the food cafeteria system’s chaotic and disorderly operation in the case study’s place of learning. The scholars proposed the simulation as a result of observance, experience, and calculations in performance issues. The appropriate variables to be chosen were the average waiting time of the system and the average number of students in the queues. Individuals realized that during rush hour mealtimes, especially lunch in the afternoon, there was more pressure on the system as expected, with the weight of the pressure being on the first hour of serving lunch. The authors used arena Simulation Software to build a simulation model, analyze the output, and produce a list of alternatives and scenarios from the given data [2]. The scholars then chose the best option from rank to improve efficiency in their school restaurant during rush hour mealtimes from the given scenarios. An Arena Process analyzer, a known software simulation tool, was used by the researcher besides other methods to select and rank the best alternative.
The problem to be solved was the impossibly long queues that occurred during the peak hours of lunch 6/7. In hindsight, days a week proved to be an aggravating task for the staff and the students. The objective was to use Arena Simulation Software to develop a queuing system to reduce the waiting time and provide a group of alternatives to help reduce and possibly eliminate the human traffic and lag. The Main KSU restaurant (school restaurant) consists of two floors, each with two sitting areas. The first-floor plan consisted mainly of four identical lines that consisted of single queues, a kitchen, and toilets. The second floor has another small bar serving fast food. The bar has four parts whereby; the first part is a self-service station consisting of various food, meaning the students get to pick the food themselves.
The second part consists of a food service station with two servers to attend to the students. Note: the food in this second station is changed almost daily to avoid monotony. In addition to that, one server seems to take the less bulk of food serving (less labor) in comparison to another (e.g., one serves only one dish and the other takes the rest). The third part is self-service with mostly condiments like drinks and sauces; here, one can skip ahead if they take too much time to choose what they would prefer. The fourth and last part is the cashier; the majority of the human traffic occurs in the second and the first section (the self-service stations). The cashier attributes this to the abundance of primary food in the fourth and last part; the lag of the servers whether allocated or self and the inability to skip over a person still making decisions. A student ID is needed at the cashier’s section for payment. The cashier counts the items on the plate to determine whether a school discount is applicable or not (it is not applied on more than six items purchased). Lines 1 and 2 share the same outlet and cashier (depending on the availability of either cashier), identical to lines 3 and 4. This queuing system was characterized primarily by three components, the arrival process, the service mechanism, and the queue discipline. Queuing provides an orderly and efficient way to look at the problem. Queuing also taps into the hierarchal nature of system simulation software. The arrival process describes how the students enter or arrive at and out of the system.
Individuals express the service mechanism as the number of servers available and the probability distributions of the student service times both self and served. The Queue discipline follows the same rules of First in First Out, a well-balanced algorithm [4]. The data was collected over five weeks but only during the six peak days. It was composed during peak hours from around 12 pm to 1 pm. The researcher’s proposed input data were as follows; the arrival time for the students, the time between arrivals of different students (intervals), and the percentages of students that choose the second floor over the first. In addition, the percentage of students choosing particular areas on the first floor, and time of service for the self-service section (both the first and the second), were part of the researcher’s proposed input data. In addition, the time of service for the second self-service in the third section, and the time for assistance for the cashier in each line were included. On top of that, the time of service for each of the two available servers is part of the researcher’s proposed input data. The data was collected, tested, and put together in a concise and meaningful way that would allow the determination of associated distributions of arrival and service times; these are the inputs of the simulation model.
The researchers tested the data using various tests and modules that individuals commonly apply in mathematical computations of data. The various tests and modules used by the researchers include; a correlation plot and Chi-squared, to get a better detailed and accurate summary of the bundle of data they had gathered. The researchers designed a flowchart to describe the model in a better view, “put it into perspective,” and provide a diagrammatical representation of the method of serving in use. The Entities to be modeled after in the system were students falling in the time of the rush hour lunchtime, which was midnight to 1 pm. The scholars validated the model via the use of pilot runs which occurred ten times, i.e., the model underwent ten replications. The verification and validation results were as follows: the natural system and the model have a similar process step. There was no error during program execution hence validating the correctness of the compiler. The output between the model and the actual system was compared using expert opinion and statistics.
After a set of data and operation research computations that are often used to ensure the most accurate answers and output are given, the base use of simulation building was achieved. As a result, several alternatives to better systems based on the fundamental model analysis used were realized by the researchers to pick the best ranking solutions to implement [2]. The alternatives listed were: Removing the second self-service, which included moving the cold drinks to the first self-service and adding the cutlery after the cashier. Not only that but also, the combination of lines one and two and three and four result in major lines. The combination was in place of the initial primary model and the first alternatives and operation of lines three and four based on two hours. Arena process analyzer and Dudewicz and Dalal (D&D) were used to obtain the results for each alternative and compare them. Upon further replications and research using the methods listed above, the researchers determined that the best choice was to remove the second self-service in the third section. The operation of lines three and four on a schedule of two hours only was to be also removed to reduce the customer waiting time and the long queues.
Some of the scholars’ limitations were: that the time taken to prepare the different foods may vary considerably. The authors previously mentioned that they often change foods to reduce monotony. Hence, the model may sometimes prove too rigid to operate on a two-hour schedule only; the lines did not provide much technique to the researchers. The possible limitations they could face include: Failure to meet operating times of allocated lines, meaning student schedules vary: some classes may end late. The kitchen could delay potentially causing disorder and shutting people out to work on a fixed timeline, having not so accurate or not inclusive dates due to using only one real-time data collection technique. There is a possibility that the researchers had the avenue to conduct interviews with both the kitchen staff. Additionally, conducting the interviews could help the students to be in the position of gaining extra information they may have missed or cannot calculate using the mathematical tools. Also, the model might not be conducive to the operational staff (Kitchen staff) as it was based mainly on research only from the students and school without including the team.
The case study then reached the following conclusions; the food system is cramped and poorly organized to run efficiently. Out of the five alternatives, two alternatives proved the best. Removing the second self-service reduces the human lag time significantly. It enables the lines to move faster and the operation of two lines only for two hours, making somewhat an “express” line and meeting the final goal, which was to improve efficiency [5]. Although the researcher noted that removing self-second service could be applied with minimum effort and cost to make the lines attain efficiency. In terms of deployment, if all parties partaking agree, they could either choose to run it concurrently with the current system, slowly phasing it out, or change or abruptly start anew. The best would be phasing out to give all parties to acclimatize to the change without chaos. Overall, the system met the requirements of adhering to and matching the features and functions of a simulation system. The system provides multiple alternatives; out of that, a natural and working solution for the problem has been derived. The system has also been used to gain insight into the existing system software. For instance, researchers could electrify the cause of a load of human traffic and the inefficient system during rush hour times through modeling and calculations. It is also essential to recognize the number of work researchers put into proper and correct validation and verification by passing the work through the various iterations [3]. Verification and validation are detrimental to a project as no particular algorithm exists for guidance on the techniques and procedures to be used. Every simulation presents itself with unique challenges that need efficient solving.
The specifications to be met have also been well outlined and followed. The system model is not complicated; it is clear, concise, and efficient. There has been straightforward use of diagrammatic and textual representation of both the problem at hand and the solution reasoning processes like documentation and making them easy to follow as they are well put together. The Formulas used for both testing and comparison, e.g., the Correlation plot and the Arena Process Analyzer, are up to date and approximately accurate within their able ranges [4].
Another issue regarding implementation and a possible limitation is the successful use of the software in the institution hence bringing the model to life. Successful use of the software in the institution is dependent on a couple of issues. For example, the availability of simulation expertise in machinery, labor, and technical knowledge are among the issues impacting software utilization. Another issue is the ability of the availed software to be merged and combined with the new software concerning the surrounding environment, cost, and other factors [3]. Additionally, the area in which the software is being applied fits and meets the demands even with changing times and demographics is another issue. For example, the population of the students and staff, changing diets and foods, possible changes in the architecture of the building, and possible evolution are among the ever-changing elements.
In conclusion, the researchers successfully implemented the Arena Operating software for this case study, and the results exceeded the expected outcome; the research was well done and adequate. The data collection and processing methods were up to date and well modeled without jargon and complicated ways. It would be easy for a non-expertise or civilian to go through the data and understand its majority with much ease. The problem itself is natural and existing; hence their solution provides real help to their institution and may be used as a model in other similar current scenarios, e.g., in other institutions. The mathematical and research computations used to calculate, correct, validate, and verify their data were also very simple but impressive. Given their nature as students, it is evident that this problem hits close to home as the impossible queues can delay both class and other activities and overall is not a pleasant experience.
The researcher looks forward to bringing the software into working without a hitch, given the circumstances of lack of technology and machinery and the rarity of expertise, experience, and knowing labor. In addition to that, researchers should feel more motivated to continue developing even more simulation software to solve problems around them. This study only shows how essential simulation software is in solving problems, even the problems that look menial. The simulation software is an inclusive science that picks everything in the technology and tries to merge it into one. From other studies such as reducing bottlenecks in traffic and even job scheduling, the simulation software is a rapidly growing yet successful and helpful subject in all matters [3]. The simulation software perfects the art of problem-solving, taking away the bulk previously used to solve such issues once. Arena software simulation provides accurate and beneficial statistical reports that provide easy, correct, and accessible information. In addition, Arena software allows for the option to use more than one method for solving, gathering, ordering, and processing. Hence the software gives developers chances to choose whatever suits them best rather than the developers feeling constrained. It is something that should be embraced openly and used more often in the solving of existing problems in society.
References
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J. Hamman and N. Markovitch, “Introduction to Arena(simulation software),” Winter Simulation Conference Proceedings, pp. 519-523, 1995.
M. A. Ghaleb, U. S. Suryahatmaja and I. M. Alharkan, “Modeling and simulation of Queuing Systems using arena software: A case study,” 2015 International Conference on Industrial Engineering and Operations Management (IEOM), pp. 1-7, 2015.
P. D. G. M. Nawara and E. W. S. Hassanein, “Solving the Job-Shop Scheduling Problem by Arena,” International Journal of Engineering Innovation & Research, vol. 2, no. 2, pp. 161-165, 2013.
K.Chaharbaghi, “Using Simulation to Solve Design and Operational Problems,” International Journal Of Operations &Production management, vol. 10, no. 9, 1990.