Arena Simulation Software Research Articles

Simulation Experiment Research on the Production of Large Box Girders

This paper introduces system simulation technology into large-scale beam field production and uses the simulation software Arena (14.0) to construct a simulation model of the beam field production system considering the randomness of the actual beam field production process operation time. The relationship between the production efficiency of the beam yard and the working time system was studied. In this paper, the improvement in beam-making efficiency in the existing beam field that is achieved by the commonly used reinforcement pre-binding method in the existing beam field is analyzed and calculated, and the improvements in the production efficiency in the ordinary beam field and the intelligent beam field are quantitatively calculated and compared. The results show that (1) when the working time system is increased from 8 h/d to 12 h/d, the average occupancy time of the traditional beam-making pedestal is shortened by 11.5 h when the working time is extended by 1 h per day; (2) with the extension of the working time system, the advantages of the pre-binding method of reinforcement gradually decrease; and (3) the application of intelligent technology not only improves the production efficiency of the beam yard but also makes the beam yard’s production more flexible and more resistant to risks.

Optimizing Restaurant Queue Management: A Simulation-Based Approach Using Arena Software

Examining the effectiveness of models in terms of utilization and waiting time is one of the anticipated benefits of analyzing queuing systems. This study uses Arena simulation software to examine the queue system of a particular restaurant. The primary objective is to assess and enhance the restaurant’s service efficiency through the modelling of customer arrivals, service procedures, and queue dynamics. Data on customer arrivals and service times were collected for the restaurant’s current system. The necessary expressions were developed from the observed data using the input analyzer. A conceptual model of the original queuing system was created, and two alternative Arena models were developed to reduce customer waiting times in the restaurant. The developed models were run to evaluate their performance. During a 24-hour simulation study, the average waiting time in the overall model was found to be 24.49 minutes, with a maximum of 48.69 minutes. The waiting time for server 1 was 25.30 minutes, for server 2 was 23.27 minutes, for server 3 was 25.11 minutes, and for server 4 was 22.30 minutes. The number of customers in line at server 1 was 12.65, at server 2 was 6.98, at server 3 was 8.29, and at server 4 was 8.25. Resource utilization was 57% for server 1, 35% for server 2, 27% for server 3, and 15% for server 4. By decreasing the queuing length and average waiting time in the restaurant, customer satisfaction can be increased, leading to a reduction in time wasted. The findings indicate significant potential for improving overall service quality and reducing client wait times. This research provides valuable insights for restaurant managers seeking to enhance customer satisfaction and operational efficiency.

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options, sometimes illegally. The root cause is corruption, which leads to traffic jams, stops and packs vehicles beyond their safe carrying capacity, and violates passengers' human rights and freedoms. This study focused on optimizing the time passengers had to wait in public vehicle stations. This applied research employed both data-gathering sources and mixed approaches. Then, 166 samples of key informants of transport stations were taken using the Slovin sampling formula. The time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The study also focused on determining the minimal response time required for passengers and vehicles queuing to reach their ultimate destinations within the transportation stations in Tepi, Mizan, and Bonga. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4 hours to 1.5 hours, quality, safety and designed load performance calculations employed. Stakeholders are asked to implement the model and monitor the results obtained.

Productivity improvement through assembly line balancing by using simulation modeling in case of Abay garment industry Gondar

This study aims to balance the garment line of the polo shirt operation by utilizing line balancing techniques. These techniques are employed to enhance the efficiency and productivity of the Abay garment industry. Observational methods and stopwatch timing were employed to collect data on the processing times for each operation. Subsequently, the standard allowed minute was determined based on work measurement principles. To assess statistical significance and identify appropriate expressions for the existing simulation modeling, all collected data underwent statistical analysis using the Arena input analyzer. Moreover, Arena simulation software was employed in this research to simulate and evaluate the effectiveness of both the current and proposed polo shirt sewing line models. Building upon the existing model, alternative improved models were proposed. The alternative models yielded significant improvements in multiple performance metrics, including output, capacity utilization, waiting time in queues, and number of products waiting. These improvements indicate an increase in productivity resulting from the implementation of balanced production lines. The outcomes of the improved scenario included an increase in output from 288 to 381, a rise in line efficiency from 39.06 % to 55.64 %, and an enhancement in labor productivity from 54.25 % to 66 %. Additionally, the cost of labor was reduced by 15.63 %, while revenue experienced a notable increase of 30 %.

Discrete-Event Simulation (DES) and Banker, Chames and Cooper Data Envelopment Analysis (BCC-DEA) Models in Improving the Allocation Officers in Outpatient Department

This study aims to propose an improvement model for the queuing system and determine the best and most appropriate allocation suggestion for officers at the outpatient department of a public clinic in Johor. In this study, Arena Simulation Software and Lingo Software were used. Discrete-Event Simulation (DES) and Banker, Chames, and Cooper Data Envelopment Analysis (BCC-DEA) models were used to determine the best improvement model across various alternatives. The Min-Max of officers was suggested as an improvement model. The mathematical formulation has been programmed and tested in the Lingo 19.0 software, and Decision-Making Units (DMU) would be suggested. After that, each DMUs were run in Arena Simulation Software. Then, the input and output of each DMUs were determined. The researcher used BCC Model Input-Oriented to reduce the input required to produce the optimal output. The mathematical formulation has been programmed again and tested in the Lingo 19.0 software. Based on the results, DMU is considered an efficient choice if the value θ_0 is one (θ_0= 1). DMU is considered an inefficient alternative if the value θ_0 is not one (θ_0 ≠ 1). The input-oriented BCC model needs to identify the most effective and efficient DMU because they have multiple DMUs that are rated as efficient. So, the Super Efficiency model was used to identify the most efficient and suitable DMU. The mathematical formulation has been programmed again and tested in the Lingo 19.0 software to identify the Super Efficiency model

Identification and fixing bottlenecks of a food manufacturing system using a simulation approach

In manufacturing systems, simulation modeling plays an important role in creating some changes instead of working on real systems. Manipulation in a real system is more costly than manipulation in a simulated model. In this research, we tried to use a simulation approach to recognize and minimize bottlenecks of a production line, which will decrease costs and improve productivity. To achieve our objectives, we chose a case and analyzed its production line. By using our case study strategy, we tried to collect our data and adapt a conceptual model of production processes drawing on an operation process chart (OPC). After that, we created a simulation model of the production processes by using the popular Arena simulation software 13.5. By running the Arena model, some bottlenecks were found. Ultimately, we proposed some solutions to obviate bottlenecks and reduce the total costs of production.

Using inventory related performance measures in the analysis of a production/inventory flexible manufacturing system

To survive in the ever-increasing competition in the market, manufacturing firms must adopt the latest technologies available. Flexible manufacturing systems (FMS) provide an effective solution to the problem, but its performance should be continually enhanced to remain competitive in the market. The aim of this work was to identify the effect of routing flexibility and system load conditions on the performance of FMS using both production and inventory related performance measures. ARENA simulation software was used to model and study the system. (R, r) inventory control policy was applied to the production/inventory system. Routing flexibility was found to have a significant effect on all the performance parameters, with maximum improvement of performance observed at increase of level from 0 to 1. It was further observed that the system load condition does not have a significant effect on any of the performance measure. The FMS showed slightly improved performance when the system was fully balanced. It can be concluded that the inventory related performance measures give comparable results to that of production related performance measures.

The Evaluation and Improvement of the Production Processes of an Automotive Industry Company via Simulation and Optimization

Production delays are significant problems for the loss of goodwill of the customers and the loss of profits associated with them. The delays may accrue as a result of insufficient resource planning and poorly designed unsatisfactory procedures. In this study, a new mathematical model is proposed to optimize the production processes by minimizing production delays, and a simulation model is developed to test the alternative facility designs. The purpose is to increase customer satisfaction by ensuring that the products are delivered timely and preventing lost sales in an automotive company that manufactures garbage collectors by using real data. The mixed-integer programming problem related to the minimization of production delays is solved by the GAMS CPLEX 24.1.3 software. In this way, the total delay in the production area is minimized by the mathematical model to prevent labor and time loss. Accordingly, the alternative designs are investigated for the improvement of the production processes by using discrete system simulation. A system analysis is performed to determine the bottlenecks in the production processes by developing a simulation model via the ARENA simulation software. With the proposed facility layout alternatives, the delays are eliminated, the total production time is reduced, and an increase in production efficiency is observed.

Modeling and simulation of the furniture manufacturing and assembly process in the arena simulation software

The article describes issues related to creating discrete simulation models for the implementation of the furniture manufacturing and assembly process in a furniture company. The methodology of the manufacturing system analysis was presented, which is aimed to support the appropriate approach to the construction of simulation models. The scope of the work includes the technological identification of the furniture manufacturing and assembly process in real production conditions, on the basis of which the appropriate simulation model was built. The course of the process was analyzed in the Arena software on the basis of a computer simulation based on reports. As a result of the simulation of the manufacturing process with the use of information from report of usage, an area for improvement was located. The re-analysis of the material flow made it possible to propose a change in the input parameters for the simulation model in the indicated area. The results of the second simulation show significant changes in the effective use of workstations and increase in the efficiency of the production line. In practice, it can be the basis for introducing simulated changes in the production system.

Application of Simulation Technique for Bus Stops Arrangement

This study uses simulation to improve efficiency of The Chiang Mai Municipality bus no. 2 (Arcade - Chiang Mai airport route). The target is to achieve the travel time from the starting station to the destination station within 1 hour and 45 minutes. The content of the research will present guidelines for the improvement and design of the Chiang Mai Municipality bus service by using simulation software Arena. In order to use the data obtained from the simulation to analyze and improve the service process of the Chiang Mai Municipality bus to be more efficient. From the simulation by reduce the number of stations. Chiang Mai Municipality bus will be able to provide the service as targeted for only 1 hour 45 minutes and reduce travel time up to 13:46 minutes or accounting for 11.01%. In conclusion, Chiang Mai Municipality bus service has a long traveling time causing passengers to wait a long time. The results of simulation found that Chiang Mai Municipality should reduce the number of stations.

Design and Development of Best Class Discrete Production Model for Distributed Manufacturing under Industry 4.0.

Global competitiveness creates a challenge for manufacturing companies to maintain their market share with dynamic customer requirements. Capital investment in machinery does not allow facility expansion to accommodate large orders from customers but to reconfigure the manufacturing enterprise. Distributed manufacturing (DM) is embraced in order to increase facility utilization by decentralizing production. An enterprise in charge of a DM network allows customers to choose the best manufacturers available for their order based on their track record, which is available through historical and online performance data. Furthermore, manufacturers as members of this network may receive orders based on their past performance. Industry 4.0 with all necessary Industrial Internet of Things (IIoT) enables the online monitoring of production key parameters of manufacturers subscribed to a DM network. We develop a new network model of manufacturers teamed under specific terms and conditions to support a group of customers who have specific needs. The proposed model, known as the continuous supervised model, is created with the ARENA simulation software. We demonstrate the effectiveness of our model by contrasting it with the standard practice approach. To ensure the best possible performance, we continuously monitor the cost, quality, delivery time, and production rate indicators of the various manufacturers and update their performance ranking for current and future orders. Furthermore, using the analytic hierarchy process (AHP) approach, a single performance measure based on the four indicators is developed. Implementing the proposed model showed an improvement in the average performance by 51.3%.

Using discrete event simulation to optimize nucleic acid testing process for coronavirus disease 2019 (COVID-19).

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus-2 (SARS-CoV-2) has placed enormous diagnostic burden on hospitals and testing laboratories. It is thus critical for such facilities to optimize the diagnostic process to enable maximum testing on minimum resources. The current standard of diagnosis is the detection of the viral nucleic acid in clinical specimens.MethodsIn order to optimize the laboratory’s nucleic acid testing system for COVID-19, we performed a Discrete-Event-Simulation using the Arena Simulation Software to model the detection process based on the data obtained from the First Affiliated Hospital of Guangzhou Medical University (FAHGMU). The maximum of total time that specimens spent and the equipment consumption was compared under different scenarios in the model.ResultsSeven scenarios were performed to simulate actual situation and improved situations. We analyzed conditions that adding a new nucleic acid extraction system (NAES), shifting a member from night duty to morning duty, using specimen tubes containing guanidine isothiocyanate (GITC), then tested the maximum testing capacity in the current number of technicians. In addition, the costs including personal protective equipment (PPE) and testing kits was calculated.ConclusionsA work schedule based on specimen-load improves efficiency without incurring additional costs, while using the specimen tubes containing GITC could reduce testing time by 30 min. In contrast, adding new NAESs or polymerase chain reaction (PCR) instruments has minimal impact on testing efficiency.

Capacity Optimization in Dynamically Routing Computer Network Systems

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®.

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

As aging populations increase worldwide, many governments have introduced the concept of paratransit services to assist individuals with limited mobility with transportation. A successful paratransit service must be able to satisfy most requests to the system; this success is typically related to the allocation of vehicles to dispatch stations. A suitable configuration can reduce unnecessary travel time and thus serve more people. This resembles the classic Dial-a-Ride problem, which previous studies have solved using heuristic algorithms. Most of these algorithms, however, incur heavy computational costs and, therefore, cannot be operated online, especially when there are many conditions to consider, many configuration requirements, or many vehicles requested. Therefore, this paper proposes an approach based on the generative adversary network (GAN), which can reduce computation significantly. In online environments, this approach can be implemented in just a few seconds. Furthermore, the amount of computation is not affected by the number of conditions, configuration requirements, or vehicles requested. This approach is based on three important concepts: (1) designing a GAN to solve the target problem; (2) using an improved Voronoi diagram to divide the overall service area to generate the input of the GAN generator; (3) using well-known system simulation software Arena to swiftly generate many conditions for the target problem and their corresponding best solutions to train the GAN. The efficiency of the proposed approach was verified using a case study of paratransit services in Yunlin, Taiwan.

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company’s call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.

A multi-product and multi-period aggregate production plan: a case of automobile component manufacturing firm

PurposeThe study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.Design/methodology/approachThis research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.FindingsThe findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.Research limitations/implicationsThe proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.Originality/valueAn integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

Investigating the effect of input variables on the performance of FMS followed by multi-response optimization: A simulation study

Flexible manufacturing systems (FMSs) help in producing reliable and good-quality products. The performance of FMSs depends on the various input variables and therefore, it is important to investigate their effect on the performance of the system. This work explores the effect of three input variables i.e., newly developed sequencing rules (SR), the number of machines, and AGV selection rules to determine their optimal setting that yields optimal results of three performance measures viz. makespan time, AGV utilization, and machine utilization simultaneously. ARENA simulation software was used to develop a replicate model of an FMS problem taken from literature. Nine models for a different combination of variables as input as per Taguchi’s L9 orthogonal array were developed and data for three performance measures were collected. Further, multi-response optimization using the integrated MEREC-MARCOS method was performed. The results of the study revealed that considering 3 machines, with GMPD as SR and shortest distance as AGV selection rule yields the optimal performance of the FMS. Further, among the three input variables, the number of machines was found to have a significant effect on the multi-performance measures. Finally, the optimal solution obtained in the study was validated by performing a confirmation test.

Utilization of the Arena simulation software and Lean improvements in the management of metal surface treatment processes

For companies to stand out in increasingly competitive, dynamic and global markets, they must have customer satisfaction goals, create value through their processes, products and services and also aim for innovation. In this context, computer sciences combined with engineering processes constitutes a powerful way for companies to be able to improve process management, to interact with such markets in an efficient and effective way. The main objective of this article is to use Arena simulation software, to quantitatively predict the impact of improvements applied in metal surface treatment processes, based on tools to support Lean thinking. A case study in a Portuguese company in the metalworking sector is presented, in which it is verified that the proposed improvements in terms of the factory layout and resource management, suggested by the comparison between simulations of the current state of the company and the improved one, streamline the processes of finishing in metals, namely zinc coating and lacquering which prevent the occurrence of oxidation and the consequent corrosion of the base metals, by adding other metals and materials to their surface, which adhere and protect it. Through the results obtained, it is concluded that the reduction of waiting times and transport of stocks without production and of work-in-progress, as well as the increase of the productive capacity, make the company more able to guarantee the satisfaction of the requirements of its customers and improve its positioning in the market compared to its competitors.

Discrete Event Simulation to Evaluate Different Treatments of Diabetic Retinopathy Disease

Modeling approach using discrete event simulation has been proven to work well in modeling in health care. The aim of our paper is to propose a simulation approach which shows realistic models presenting different possible treatments in different stages of diabetic retinopathy. We have presented three models in order to choose the best treatment for diabetic retinopathy patients. The first model describes the flow of a patient through stages without any medical treatments. It takes 13 years to reach blindness. The second model which includes the laser photocoagulation treatments leads to blindness after 46 years. Then, the third model illustrates the involvement of vitrectomy operation and delays blindness by 23 years. To construct the models, data were taken from experienced doctors and professors of the ophthalmology department in the University hospital Habib Bourguiba and the endocrinology department in the University hospital Hedi Chaker in Sfax, Tunisia. Our objective is to delay reaching the blindness stage as late as possible. Three models were developed, verified and validated through many iterative implementations with ARENA simulation software.

A roadmap to improving productivity in a fastener manufacturing unit by process optimization using ARENA

In an increasingly competitive market, the need for resource maximization is ever-present. Efficient and maximum optimization is desired throughout the organization. This paper focuses on methods and techniques that can achieve said effectivity in production through mainly heuristic means. Hence these techniques are ideal for engineers in management. They are based on both experience of personnel and scientific analysis of academicians, thus creating an excellent and executable plan that is straightforward, flexible and practical. A detailed study and analysis of the manufacturing unit was carried out. Personal interactions with the operators and employees in their workplace, during work hours, greatly helped in problem identification. Each problem was identified was then categorized and observed to gather solutions for improvement in quality and productivity of the process from which the problem originated. An intensive time-study analysis was also done to understand the inefficiencies in process flow and the results were simulate on Arena Simulation Software and various models for improved models were proposed. These improved models were found by either adjusting resources or eliminating processes, thus improving output. Thus, this research will be beneficial for practitioners and researchers alike, for tacking various inefficiencies in an organization.