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To provide an example of the use of system dynamics within the context of a discrete‐event simulation study.

A discrete‐event simulation study of a production‐planning facility in a gas cylinder‐manufacturing plant is presented. The case study evidence incorporates questionnaire responses from sales managers involved in the order‐scheduling process.

As the project progressed it became clear that, although the discrete‐event simulation would meet the objectives of the study in a technical sense, the organizational problem of “delivery performance” would not be solved by the discrete‐event simulation study alone. The case shows how the qualitative outcomes of the discrete‐event simulation study led to an analysis using the system dynamics technique. The system dynamics technique was able to model the decision‐makers in the sales and production process and provide a deeper understanding of the performance of the system.

The case study describes a traditional discrete‐event simulation study which incorporated an unplanned investigation using system dynamics. Further, case studies using a planned approach to showing consideration of organizational issues in discrete‐event simulation studies are required. Then the role of both qualitative data in a discrete‐event simulation study and the use of supplementary tools which incorporate organizational aspects may help generate a methodology for discrete‐event simulation that incorporates human aspects and so improve its relevance for decision making.

It is argued that system dynamics can provide a useful addition to the toolkit of the discrete‐event simulation practitioner in helping them incorporate a human aspect in their analysis.

Helps decision makers gain a broader perspective on the tools available to them by showing the use of system dynamics to supplement the use of discrete‐event simulation.

Construction‐oriented discrete‐event simulation often faces the problem of defining uncertain information input, such as subjectivity in selecting probability distributions that result from insufficient or lack of site productivity data. This paper proposes incorporation of fuzzy set theory with discrete‐event simulation to handle the vagueness, imprecision and subjectivity in the estimation of activity duration, especially when insufficient or no sample data are available. Based upon an improved activity scanning simulation algorithm, a fuzzy distance ranking measure is adopted in fuzzy simulation time advancement and event selection for simulation experimentation. The uses of the fuzzy activity duration and the probability distribution‐modeled duration are compared through a series of simulation experiments. It is observed that the fuzzy simulation outputs are arrived at through only one cycle of fuzzy discrete‐event simulation, still they contain all the statistical information that are produced through multiple cycles of simulation experiments when the probability distribution approach is adopted.

This paper aims to address a central concern in modeling and simulating electric grids and the information infrastructure that monitors and controls them. The paper discusses the need for and methods to construct simulation models that include important interactions between the physical and computational elements of a large power system.

The paper offers a particular approach to modeling and simulation of hybrid systems as an enabling technology for analysis (via simulation) of modern electric power grids. The approach, based on the discrete event system specification, integrates existing simulation tools into a unified simulation scheme. The paper demonstrates this approach with an integrated information and electric grid model of a distributed, automatic frequency maintenance activity.

Power grid modernization efforts need powerful modeling and simulation tools for hybrid systems.

The main limitation of this approach is a lack of advanced simulation tools that support it. Existing commercial offerings are not designed to support integration with other simulation software products. The approach to integrating continuous and discrete event simulation models can overcome this problem by allowing specific tools to focus on continuous or discrete event dynamics. This will require, however, adjustments to the underlying simulation technology.

This paper demonstrates an approach to simulating complex hybrid systems that can, in principle, be supported by existing simulation tools. It also indicates how existing tools must be modified to support our approach.

To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties.

The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead‐time determination. The developed methodology employs bi‐directional feedback between the non‐stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined.

An example of determining realistic production lead‐time data in the ERP system is presented to illustrate how such a marriage can be achieved.

The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations.

The organizations who have been using the discrete event simulation in their planning and decision‐making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models.

The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

The purpose of this study is to improve the utilization of walk-in tutors at the University of Dayton.

This paper discusses the application of mixed methods participatory action research with the use of the lean six sigma (LSS) define-measure-analyze-improve-control (DMAIC) methodology combined with discrete event simulation to improve the tutoring processes at the University of Dayton.

The impact of these improvements provided a great balance between tutor utilization and student wait time, and as a result, an increase in pay was negotiated for tutors. Additionally, two schools within the University, Engineering and Business, have seen the value of the tutoring for their students and have provided additional financial support for tutoring services. Specifically, these schools now have dedicated by-appointment tutors for their students.

Incorporating simulation within the LSS methodology provides a “prototype” simulation of the potential solutions prior to changing the process in the real world. This approach can minimize the risk of implementing solutions that are costly or ineffective. The improvement of increased tutor pay helped to demonstrate the university’s commitment to tutors and tutoring. Through continuous improvement efforts, the tutoring department has tripled the number of courses that they support through these enhanced relationships, improved utilization and the overall culture and work environment. The LSS methodology and tools combined with discrete event simulation can be used as a guide for improving other repetitive processes within the university.

A discrete‐event simulation of a utility trouser manufacturing plant is described. The simulation model, written in the GPSS/PC language, was validated with operating data from a large plant with a nominal production capacity of 40,000 pairs of men's denim trousers per week. Specifically, the simulation results closely agreed with key plant operating figures, such as production rate, number of work stations, work‐in‐process inventory and residence time in production.

Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.

This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.

The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.

This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.

This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.

This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.

This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions.

A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients.

Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds.

This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

Implementation of lean manufacturing is currently performed in the production industry; however, for the airline maintenance service industry, it is still in its infancy. Indicators such as work in process, cycle time, on-time performance and inventory are useful indicators to measure lean implementation; however, a financial economic perspective taking fixed assets into consideration is still missing. Hence, the purpose of this paper is to propose a method to measure lean implementation from a fixed asset perspective for this type of industry. With the indicators, continuous improvement scenarios can be explored by value stream discrete event simulation.

From literature, indicators regarding asset specificity to measure lean implementation are found. These indicators are analysed by a linear least square method to know if variables are interrelated to form a preliminary model. The indicators are tested by value stream-based discrete event simulation regarding continuous improvement scenarios.

With the new found lean transaction cost efficiency indicators, namely, turnover, gross margin and inventory pre-fixed asset (T/FA, GM/FA and I/FA, respectively), it is possible to measure operation performance from an asset specificity perspective under the influence of lean implementation. Secondly, the results of implementing continuous improvement scenarios are measured with the new indicators by a discrete event simulation.

This research is limited to the airline maintenance, repair and overhaul (MRO) service industry regarding component repair. Further research is necessary to test the indicators regarding other airline MRO service companies and other sectors of complex service industries like health care.

The lean transaction cost efficiency model provides the capability for a maintenance service company to simulate the effects of process improvements on operation performance for service-based companies prior to implementation.

Simulation of a Greenfield process can involve employees with possible changes in processes. This approach supports the adoption of anticipated changes.

The found indicators form a preliminary model, which contributes to the usage and linkage of theories on lean manufacturing and transaction cost theory – asset specificity.

Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.

This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.

Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.

Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.