Discrete Event Simulation Approach Research Articles

Assessing operational resilience in food supply chains: a discrete-event simulation approach based on a framework for integrated decision-making

ABSTRACT Food supply chains (FSCs) play a critical role in ensuring food security but face increasing vulnerabilities to disruptions. With the objective to mitigate these challenges, this study focuses on assessing operational resilience within the Austrian dairy supply chain during sudden demand spikes. For this purpose, we introduce a novel framework for integrated decision-making which encompasses strategic, tactical, and operational perspectives, augmented with modular building blocks for FSC modelling and simulation. Based on this framework, we develop a discrete-event simulation model to assess and quantify the impact of strategic, tactical, and operational interventions on operational resilience. Our analysis reveals the effectiveness of upstream buffer stocks, adapted reordering strategies, and operational adjustments in response to demand increases. However, no single intervention consistently outperforms others across all performance metrics, emphasising the importance of tailored intervention selection. Hence, the major contributions, i.e. (1) the framework for integrated decision-making and (2) the quantification of potential interventions to enhance operational resilience, are useful for improving the operational resilience of FSCs and increase their ability to supply essentials in the short term despite disruptions. The results assist FSC actors in making more informed decisions to enhance operational resilience.

Smoothly pass the parcel: implementing the theory of swift, even flow

This research examines the application of the Theory of Swift, Even Flow (TSEF) by a distribution company to improve the performance of its processes for parcels. TSEF was deployed by the company after experiencing lean improvement fatigue and diminishing returns from the time and effort invested. This case study combined quantitative and qualitative approaches to develop a good understanding of the operation. This approach enabled the business to utilise Discrete Event Simulation (DES), which facilitated the implementation of TSEF. From this study, the development of a novel DES application revealed the primacy of process variation and throughput time, key factors in TSEF, in driving improvements. The derived DES approach is reproducible and demonstrates its utility with production improvement frameworks. TSEF, through the visualisations and analysis provided by DES, broadened the scope of improvements to an enterprise level, therefore assisting the business managers in driving forward when lean improvement techniques stagnated. The impact of the research is not limited to the theoretical contribution, as the combination of DES and TSEF led to significant managerial insights on how to overcome obstacles and substantiate change.

Assessing Inland Waterway Transport (IWT) container logistics on the Rhine Alpine corridor: A discrete event simulation approach

Inland Waterway Transport (IWT) is pivotal for hinterland freight logistics, connecting numerous inland terminals with major deep-sea ports. Despite its significant advantages in cargo capacity and environmental sustainability, it remains underutilized due to operational inefficiencies, diminishing its competitive edge compared to road and rail transport. Addressing these challenges requires innovative solutions, yet existing studies often neglect to evaluate these innovations and their interactions within the broader IWT logistics framework and other transport modes.This study bridges these gaps by introducing a comprehensive Discrete Event Simulation (DES) model to capture the dynamic interactions within the IWT logistics system, its interplay with other transport modes, and the potential impact of innovative measures on IWT performance. The model simulates the current container logistics system along the Rhine-Alpine (RALP) corridor, incorporating key elements such as cost-time calculations, transport mode selection, network flow allocation, and the assessment of IWT innovations.The methodological framework and architecture of the DES model are presented, emphasizing verification and validation processes to ensure accuracy and reliability. The model outputs a network-wide analysis of the current IWT logistics system, examining mode split, cost, time, emissions, distance, and the interrelationships among these factors. This analysis serves as a benchmark for evaluating the effects of various innovative strategies on IWT performance.By employing the DES methodology, this research advances the understanding of container IWT logistics, providing critical insights for stakeholders and policymakers. It evaluates the current performance of container IWT in the RALP corridor and identifies opportunities to improve the efficiency and competitiveness of container IWT.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

Changing the Patient Flow Process for the Green Area of Emergency Department: A Discrete-Event Simulation Approach

The emergency department is important as a fundamental element of the healthcare system. Patient flow processes in emergency departments are a critical factor that determines effective and rapid medical intervention as well as the overall performance of the healthcare system.&#x0D; Aim: This study aimed to analyze patient waiting times and hospital stay times by developing a different approach to the current patient flow processes in the green area of a hospital emergency department.&#x0D; Methods: Numerical and statistical results were obtained using a discrete event simulation model for the current and new situation of patient flow processes.&#x0D; Results: The waiting time decreased from 12.91 minutes in the current simulation model to 12.81 minutes in the proposed simulation model. The length of stay time decreased from 54.72 minutes in the current simulation model to 53.90 minutes in the proposed simulation model. In this case, it is seen that the proposed model provides a reduction in the length of stay and patient waiting time. Conclusion: The result shows that the proposed model provides some improvement in both waiting and length of stay time. This study offers a different approach to patient flow, allowing emergency department personnel to use their resources effectively and minimizing the time spent on patients' treatment or waiting periods. Thus, this study concluded that the effective management of recommended patient flow processes increases the capacity of emergency departments to deal with emergencies and accelerates access to emergency medical services.

Fleet management for earthmoving operation in the phase of detail estimation using mixed integer nonlinear programming

ABSTRACT Earthmoving operations generally account for about a quarter of the construction budget in high-way constructions. Feasible planning is thus required for delivering the successful performance of such projects. Selecting the best fleet for the operation is one of the crucial factors affecting both duration and cost. However, owing to changing operating circumstances and the uncertainty of construction projects, this task is yet conducted based on the experience of site managers. The existing literature has witnessed that discrete event simulation (DES) and references such as the construction standard production rate (CSPR) are used to assist the decision-making. Those methods have shown limitations for practical application due to the need for expert knowledge of simulation modeling and time-consuming efforts for data collection. Accordingly, this study proposes a more efficient method of using mixed integer nonlinear programming (MINLP) in multi-objective problems, enabling optimal fleet information derivation based on given circumstances. The proposed method uses the CSPR-based mathematical equations for the consideration of productivity generated by the types and sizes of equipment. A comparative study of the proposed method and a DES approach was given for further discussions.

Analisis kinerja sistem produksi pada industri produsen tahu bandung dengan pendekatan simulasi event diskrit: Studi kasus pada Tahu Bandung ALN

Background: This study aims to analyze the performance of the production system currently implemented by Tahu Bandung ALN and propose alternative improvements that can make the performance of the production system more efficient. Methods: This study uses a discrete-event simulation approach, and the parameters of performance used are total production time and total production costs. Finding: The results of the base case scenario simulation found that there are several production processes that have waiting times which indicate the bottlenecks. Therefore, two alternative of improvements are proposed, namely the first scenario (adding more resources in the production processes that have waiting times) and the second scenario (combination of adding more resources in the production processes that have waiting times and implementing the inventory policy of certain raw material). Conclusion: The first alternative scenario is the better one as it can provide an improved performance, in which the total production time is reduced by 22,40% and the total production cost is reduced by 40,57%.

Enhancing Accessibility in Academic Buildings: A Discrete Event Simulation Approach for Robotic Assistance

Enhancing Accessibility in Academic Buildings: A Discrete Event Simulation Approach for Robotic Assistance

Simulation-based comparison of push- and pull-based planning in panelized construction

Offsite construction involves the prefabrication of construction components in a factory environment, followed by transportation to the construction site for assembly. In planning the sequences for construction tasks, a push approach can maximize the offsite manufacturing capacity, while a pull approach can achieve just-in-time delivery. Both approaches need to consider the logical constraints and resources of offsite construction processes. This complexity fuels a debate of the effectiveness of push- and pull-based planning, highlighting the necessity to study the impacts of these approaches on planning. This study utilizes Discrete-Event Simulation (DES) and Agent-Based Modeling (ABM) approaches to compare planning outcomes for offsite construction. The results demonstrate that the pull-based planning approach resulted in an overall 12.6% reduction in project completion time and an 80.3% reduction in product waiting time, demonstrating pull-based planning as a more efficient solution for offsite construction planning.

Methodology for construction-standard-production-rate-based simulation modeling and production-rate data generation

ABSTRACT While implementing a construction project, production-rate assessment needs to be conducted throughout the different stages of the project because this assessment can determine the success or failure of the project. In South Korea, the construction-standard-production-rate (CSPR) is used for determining the production-rate of individual construction equipment. However, CSPR cannot provide the production-rate of activities considering a combination of more than two different types of equipment (eg, excavator and dump truck used for excavation-load-haul activities). Having ready-made data on such production-rates of different equipment combinations would be beneficial for determining and optimal fleet management plan. Furthermore, such data can be beneficial for implementing optimized scheduling methods using supervised-learning methods. Accordingly, simple discrete event simulation approaches were carried out for data generation and regression analysis for testing the feasibility of the usage of such data. The time inputs used in this study were calculated based on the equations and information given in the CSPR.

Perbandingan Waktu Tunggu Dan Waktu Pelayanan Sistem Pembayaran Cash Dan Cashless Di Parkiran Menggunakan Discrete Event Simulation

Congestion can take a lot of time for people. Congestion does not only occur on roads but also occurs in public facilities such as parking facilities. where one of the causes is the queue in and out of parking in the business area, which has an impact on discomfort. The purpose of this study was to assess the effectiveness of the cash and non-cash payment service queuing system in terms of waiting time and service time. The stages of the research are observation, data collection on service time, data sampling, data processing and analysis using the discrete event simulation (DES) approach, validation, and suggestions for improvement. The results of the analysis show that the payment system with the cashless method is more effective than the cash payment method. Therefore, the cashless method can be used as a reference for parking service providers in the modern market to be implemented in a parking payment system, with an average service time of the Cashless method of 15.2365 seconds compared to an average service time of the cash method of 42.6812 seconds. seconds or an increase in server time from cash to cashless by 2.81% and a decrease in waiting time using cashless compared to cash by 9.31%. Keywords: Discrete Event Simulation, payment method cashless, Queue System

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

DES and IIoT fusion approach towards real-time synchronization of physical and digital components in manufacturing processes

Today's manufacturing systems offer more products to meet specific needs. Complex production systems in rapidly changing environments result from product variation, shorter product life cycles, and supply chain expansion. A cyber-physical production system (CPPS) can use manufacturing and logistics data to plan, monitor, and control production. Discrete event simulation (DES) and digital twin (DT) technology can model and evaluate manufacturing and logistics processes using high-level decision support and process monitoring. The cost of collecting input data from different enterprise data sources and mapping it into models and the lack of qualified experts prevent the widespread use of these methods in industry, especially in small and medium-sized enterprises and larger multinational companies. This research aims to create a modular digital twin framework for manufacturing process optimization and real-time monitoring in an industrial environment with few components. The system can identify and track the product through the manufacturing cycle while updating the DT in real-time and can be used independently to collect input parameters for discrete event-driven simulations and even for automatic simulation building in the future. The framework's operation will be shown through an example. With the proposed IIoT (industrial internet of things) system integration, it can detect faults and warn of deviations from normal operation, and DT can drastically reduce data collection and model building and support model reusability, increasing sustainability.

Special Issue “From COP 26 to COP 27: Contributions of Systems Approaches to Address the Challenges Ahead”: An Editorial Commentary

This Special Issue focuses on proposing and analyzing systemic interdisciplinary approaches to support collaborative strategies and agreed-upon global sustainability policies toward addressing the challenges that lie ahead for our planet’s future. The contributions target applications in system dynamics, systems thinking, discrete event simulation, agent-based modelling, and hybrid approaches and provide valuable qualitative and quantitative insights to guide the collaborative efforts of governments, institutions, organizations in general, and even the financial sector toward the next Conference of Parties (COP28).

Multi-criteria scheduling of realistic flexible job shop: a novel approach for integrating simulation modelling and multi-criteria decision making

Increased flexibility in job shops leads to more complexity in decision-making for shop floor engineers. Partial Flexible Job Shop Scheduling (PFJSS) is a subset of Job shop problems and has substantial application in the real world. Priority Dispatching Rules (PDRs) are simple and easy to implement for making quick decisions in real-time. The present study proposes a novel method of integrating Multi-Criteria Decision Making (MCDM) methods and the Discrete Event Simulation (DES) Model to define job priorities in large-scale problems involving multiple criteria. DES approach is employed to model the PFJSS to evaluate Makespan, Flow Time, and Tardiness-based measures considering static and dynamic job arrivals. The proposed approach is implemented in a benchmark problem and large-scale PFJSS. The integration of MCDM methods and simulation models offers the flexibility to choose the parameters that need to govern the ranking of jobs. The solution given by the proposed methods is tested with the best-performing Composite Dispatching Rules (CDR), combining several PDR, which are available in the literature. Proposed MCDM approaches perform well for Makespan, Flow Time, and Tardiness-based measures for large-scale real-world problems. The proposed methodology integrated with the DES model is easy to implement in a real-time shop floor environment.

Stochastic modelling of multi-layer HAP-LEO systems in 6G for energy saving: An analytical approach

In the current era of communication systems, the next-generation technology known as sixth generation (6G) is anticipated to extensively utilize space-air-terrestrial networks (SAT). High altitude platforms (HAPs) and low earth orbit (LEO) satellites are essential components of SAT network. The presented study proposes an architecture which concentrates on energy saving of HAP in the SAT network. Further, a stochastic model has been proposed which considers three energy saving states of HAP; power saving, standby, and sleep states. It has been assumed that in these states, the energy consumption of HAP is very low or zero. Whenever, there is an arrival of a packet in the system, HAP will start providing service immediately and the whole system will be in active state. The analysis of the stochastic model has been carried out using Markov process (MP) and semi-Markov process (SMP). The system has been analysed in steady-state for both the MP and SMP to obtain the steady-state probabilities. Through the numerical results, the effective contribution of energy saving states in enhancing the energy saving of HAP system has been provided and a comparison for the MP and SMP case has been described in detail. To verify the accuracy of the presented model, a discrete-event simulation approach is employed.

A Petri Nets-Based Simulation Methodology for Modular Modeling and Performance Evaluation of Car-Sharing Networks

In the field of urban and green mobility, electric car-sharing networks have recently emerged as one of the promising alternatives, dealing with both environmental and transportation issues. Despite the increasing success of the shared mobility concept over the world, the design and management of such urban mobility systems implies strategic, tactical and operational challenges. To support designers and operators, all these issues constitute an emerging and challenging research topic, aiming to develop techniques and tools for modeling, analysis and optimization of such complex systems. In this contribution, based on the modeling and analysis power of stochastic timed Petri nets, a discrete event simulation approach is developed, taking into account their dynamic behavior complexities due to their self-service mode and characteristics including capacities of the stations, energy availability, rebalancing and maintenance activities. A real-life application is presented, to demonstrate the applicability and the potential of the proposed modeling and simulation analysis approach. <i>Note to Practitioners</i>&#x2014;To support designers, providers and managers of shared mobility programs, performance and optimization models and decision-making tools are needed. In this contribution, we deals with a powerful and modular stochastic timed Petri net methodology suitable for performance modeling and simulation analysis of such complex dynamical systems. The authors believe that this discrete event dynamic approach has significant promise for the future and to influence economic viability and operational efficiency of such emerging urban transportation systems.

Comparing allocation and relocation policies at a logistics service container terminal: a discrete-event simulation approach

Comparing allocation and relocation policies at a logistics service container terminal: a discrete-event simulation approach

Sustainability Aspects of Drone-Assisted Last-Mile Delivery Systems—A Discrete Event Simulation Approach

The goal of this article is to examine the advantages and disadvantages of the application of drones in last-mile delivery systems from a sustainability point of view regarding CO2 emissions and energy consumption. As commercial drones are developing rapidly, the application of such tools in the field of last-mile delivery and transportation can offer many opportunities to increase service flexibility, reduce delivery time and decrease CO2 emissions and energy consumption. In this article, a discrete event simulation is applied to examine how the assistance of drones in parcel delivery services can influence the sustainability aspects of such services regarding CO2 emissions and energy consumption in an urban environment. Based on factory parameters, a vehicle-based delivery scenario is compared to a drone-assisted scenario under ideal conditions. According to the results, within the simulation parameters with the assistance of drones, a decrease in CO2 emissions and energy consumption is possible in last-mile delivery services, but more environmental, technological and financial limitations should also be addressed and incorporated to determine whether such a development is worthwhile from a last-mile delivery company’s point of view.

Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic

ABSTRACT Adherence to wait time benchmark targets for the diagnosis and initiation of interventions for rheumatoid arthritis is crucial in altering the disease trajectory. We analysed the impact of the maximum wait time guarantee (MWTG) policy for routing referrals for the initial rheumatologist consults on the waiting and service costs. We modelled a central intake system for a rheumatology clinic as a discrete event simulation (DES) model. Using data from a central intake and rheumatology clinic as input to the model of the system, we simulated the arrival of referrals and rheumatologist visits of patients. We demonstrated the impact of the referral policy on system performance and compared the system costs in an MWTG policy and first-available-appointment policy scenarios. MWTG policy is an option for a wait time management strategy but comes with essential cost considerations. Healthcare managers and policymakers should consider the DES approach to support referral decision policy choices.