A combined discrete-continuous simulation model for analyzing train-pedestrian interactions

Network centric operations (NCO) has been dubbed the most significant revolution in military affairs (RMA) in the past 200 years. The promise of NCO is based on the notion that information sharing and connectivity is fundamental to the effectiveness of a combat force. This is due to the ability of a properly networked force to self-synchronise itself as it engages enemy forces. The information age combat model (IACM) is a popular representation of NCO. Previous research at modelling the IACM has so far focused solely at using agent-based modelling. That work produced significant contributions to the IACM literature. However, it has proven to be computationally expensive. IACM simulations must be sustainable as research into IACM evolves and more NCO complexities are introduced. This research will propose a discrete-event simulation (DES) approach to model the IACM. The paper will present the basis upon which DES was selected for this purpose and will illustrate how the DES approach can provide significant improvements in terms of the time and computational power needed to run IACM simulations.

The standard clock (SC) method is an efficient approach for discrete event simulation. Its basic ideas are quite different from traditional approaches. SC has neither an event list nor event lifetimes. However, its applicability is limited to exponential distributions and a class of non-exponential distributions. In this paper we provide an effective approach to extend SC to all general distributions, while preserving the efficiency advantage of SC. Numerical testing shows that our approach can effectively accomplish this goal.

Custom Hiring Centers (CHCs) for agricultural equipment and services are being promoted by the Government of India and various state governments. CHCs are important part of the complex agricultural system. They are beneficial to the community as small and marginal farmers, instead of having to purchase costly machines, can rent the machines as needed. Key factors that affect profitability are the type and number of equipment hosted by the CHC. Each piece of equipment entails a sunk cost as well as maintenance cost. However, since most agricultural operations are time sensitive, the demand is concentrated in time, and having too few pieces of equipment would entail inability to serve the demand and lost revenue. This paper presents a model to simulate the operations of a CHC, under varying scenarios, in order to estimate its profitability. The model uses a combination of Agent Based Modeling (ABM) and Discrete Event Simulation (DES) approaches. Farmers and equipment are modelled as the agents in the system while the CHC operations are modelled using DES approach. The use of the model is also demonstrated with the help of an example. The results showed that even for one combination of equipment, the profitability varies a lot. This significant variation in profitability arises from variation in the percentages of areas under different types of crop, as well as the inherent variation in crop growth rates, readiness of individual fields for particular operations within the seasonal window, as well as propensity of individual farmers to wait for CHC equipment if not immediately available. Results also demonstrate that simulation is useful to model CHC operations in a complex agricultural system and take informed decision about number and types of equipment to buy.

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).

The aim of this research is to create a simulation model for a gas station in Changlun, Kedah. This model is intended to identify bottlenecks and propose alternative solutions to enhance system performance and improve customer satisfaction. The study uses the discrete event simulation (DES) approach to develop and analyse the operational system of a specific gas station in Changlun. Performance metrics considered in this model include average and maximum queue waiting times, average and maximum total system times, average and maximum queue lengths, and utilisation rates. The simulation model is developed using Arena software, and the resulting data is analysed. Based on the findings, three potential solutions are proposed: designating pumps exclusively for motorcycles, expanding parking space, and combining both approaches.

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.

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

Models to understand the impact of management practices on retail performance are often simplistic and assume low levels of noise and linearity. Of course, in real life, retail operations are dynamic, nonlinear and complex. To overcome these limitations, we investigate discrete-event and agent-based modelling and simulation approaches. The joint application of both approaches allows us to develop simulation models that are heterogeneous and more life-like, though poses a new research question: When developing such simulation models one still has to abstract from the real world, however, ideally in such a way that the ‘essence’ of the system is still captured. The question is how much detail is needed to capture this essence, as simulation models can be developed at different levels of abstraction. In the literature the appropriate level of abstraction for a particular case study is often more of an art than a science. In this paper, we aim to study this question more systematically by using a retail branch simulation model to investigate which level of model accuracy obtains meaningful results for practitioners. Our results show the effects of adding different levels of detail and we conclude that this type of study is very valuable to gain insight into what is really important in a model.

We show how a basic discrete event simulation language can be enabled for business process modeling and simulation by adding an activity construct. While activities are often not considered at all or not treated in a conceptually satisfactory way in the discrete event simulation literature, the great majority of business process modeling languages are based on an activity construct. However, unlike a simulation language, the predominant business process modeling languages, including UML Activity Diagrams and the Business Process Modeling Notation (BPMN), are not executable. So, the challenge for business process modeling is to define an executable semantics for activities, while the challenge for discrete event simulation is to find a way how to introduce an activity construct on top of the basic discrete event simulation concepts of objects and events. The main idea is to define an activity as a complex event having a start event and an end event. This idea is well-known from the business process modeling literature, e.g. from the Business Process Definition Metamodel (Bock 2008), but it has also been used in a rudimentary way in some discrete event simulation approaches, e.g. in (Fishman 2001). Our research contribution consists of two achievements: 1) we define a conceptual model of activities for discrete event simulation and implement it in our ER/AOR simulation language; 2) we show how to use BPMN for the purpose of simulation modeling.

We define an operational (transition system) semantics for the two most basic forms of Discrete Event Simulation (DES): event-based simulation (without objects) and object-event simulation. We show that under our operational semantics, DES models correspond to a certain form of abstract state machines (ASMs) such that the Future Event List (FEL) is part of the transition system state and the transition function is based on event routines. Unlike other formalisms proposed for DES (such as Petri Nets or DEVS), our ASM semantics takes all basic DES concepts (like event types and the FEL) into consideration and allows for expressive transition system states representing the objects, properties, relations and functions of the evolving possible worlds of a simulation run. As a direct formal semantics of DES, it provides a basis for comparing, and explaining design choices in, different DES approaches.

An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department

Keith Beven was amongst the first to propose and demonstrate a combination of conceptual rainfall–runoff modelling and stochastically generated rainfall data in what is known as the ‘continuous simulation’ approach for flood frequency analysis. The motivations included the potential to establish better links with physical processes and to avoid restrictive assumptions inherent in existing methods applied in design flood studies. Subsequently, attempts have been made to establish continuous simulation as a routine method for flood frequency analysis, particularly in the UK. The approach has not been adopted universally, but numerous studies have benefitted from applications of continuous simulation methods. This paper asks whether industry has yet realized the vision of the pioneering research by Beven and others. It reviews the generic methodology and illustrates applications of the original vision for a more physically realistic approach to flood frequency analysis through a set of practical case studies, highlighting why continuous simulation was useful and appropriate in each case. The case studies illustrate how continuous simulation has helped to offer users of flood frequency analysis more confidence about model results by avoiding (or exposing) bad assumptions relating to catchment heterogeneity, inappropriateness of assumptions made in (UK) industry‐standard design event flood estimation methods, and the representation of engineered or natural dynamic controls on flood flows. By implementing the vision for physically realistic analysis of flood frequency through continuous simulation, each of these examples illustrates how more relevant and improved information was provided for flood risk decision‐making than would have been possible using standard methods. They further demonstrate that integrating engineered infrastructure into flood frequency analysis and assessment of environmental change are also significant motivations for adopting the continuous simulation approach in practice. Copyright © 2016 John Wiley &amp; Sons, Ltd.

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather. SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model. DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints. Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term. The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements. An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

Discrete Event Simulation (DEVS) is a widely used approach for modelling and simulation of dynamic discrete systems. The modern object-oriented DEVS world view regards active objects (entities) passing passive objects (stations) along given path. An event mechanism updates movement of entities, being capable also of collisions and other state-dependent phenomena. Consequently; DEVS can be used for simulation of pedestrian and evacuation dynamics. Classical straightforward modelling approaches, based on abstract classical queuing systems, do not take into account the spatial distributions primarily — only for animation a spatial component is used. But modern object-oriented tools allow modelling of a spatial distribution primarily by means of topological attributes as well in active and passive objects at the modelling level. This contribution discusses the “spatial” features of DEVS simulation systems The chosen simulation tool, Enterprise Dynamics, offers a high flexibility due to its programming language 4d script that allows adding further functionality to the provided elements as well as creating completely new ones.

Background and Purpose: Agent-based modelling and simulation (ABS) is growing in many areas like, e.g., management, social and computer sciences. However, the similar trend does not seem to occur within the field of business process management (BPM), even though simulation approaches like discrete event simulation or system dynamics are well established and widely used. Thus, in our paper we investigate the advantages and disadvantages of agent-based modelling and simulation in the field of BPM in simulation experiments. Design/Methodology/Approach: In our research, we investigate if there is a necessity for ABS in the field of BPM with our own simulation experiments to compare traditional and ABS models. For this purpose, we use simulation framework MAREA, which is a simulation environment with integrated ERP system. Our model is a complex system of a trading company selling computer cables. For the verification of our model, we use automated process discovery techniques. Results: In our simulations, we investigated the impact of changes in resources’ behavior on the outcome of company’s order to cash process (O2C). Simulations experiments demonstrated that even small changes might have statistically significant effect on outcomes of the processes and decisions based on such outcomes. Simulation experiments also demonstrated that the impact of randomly distributed fluctuations of well-being have a diminishing tendency with the increasing number of sales representatives involved in the process. Conclusions: Our research revealed several advantages and disadvantages of using ABS in business process modelling. However, as we show, many of them were at least partially addressed in the recent years. Thus, we believe that ABS will get more attention in the field of BPM similarly to other fields like, e.g., social sciences. We suggested areas in BPM simulations, e.g., modelling of resources, be it human or technological resources, where there is a need for ABS.