Introducing a Hybrid Simulation Modeling Formalism to Specify Complex Adaptive Systems on the Conceptual Level

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.

Hybrid simulation modelling in operational research: A state-of-the-art review

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.

Agent-based simulation (ABS) is a relatively recent modeling technique that is being widely used to model complex adaptive systems by many disciplines. Few full length courses exist on agent-based modeling and a standard curriculum has not yet been established, but there is considerable demand to include ABS into simulation courses. Modelers often come to agent-based simulation by way of self-study or attendance at tutorials and short courses. Although there is substantial overlap, there are many aspects of ABS that differ from discrete-event simulation (DES) and System Dynamics (SD), including applicable problem domains, disciplines and backgrounds of students, and the underpinnings of its computational implementation. These factors make ABS difficult to include as an incremental add-on to existing simulation courses. This paper reports on some approaches to teaching the modeling of complex systems and agent-based simulation that the authors have used in a range of classes and workshops.

The aim of this paper is to discuss the implications of a complex adaptive systems approach to the management of logistics operations. The research is a result of the need for managers of logistics operation to be able to adapt to the ever-changing demands of their environment and customer demands. The identified emphasis of mechanistic assumptions in traditional modeling of logistics is discussed and it is suggested that a change towards adaptive models and tools is needed. This means that the models have to be able to consider more complex behavior such as self-organization and emergent phenomena. A complex adaptive system (CAS) approach is applied by the use of agent-based modeling (ABM). Two cases are provided in which the CAS approach has been applied through ABM simulations. It is concluded the CAS approach implemented by the use of ABM can provide valuable insights for both researchers and practitioners within the field of logistics management.

The last 10 years have witnessed a marked upsurge of attention on Hybrid Simulation (HS). The majority of authors define HS as a joint modelling approach which includes two or more simulation approaches (mainly Discrete Event Simulation, System Dynamics and Agent Based Simulation). Whilst some may argue that HS has been in existence for more than 5 decades, the recent rise tended to be more problem driven rather than technical experimentation. Winter Simulation Conference (WSC) 2015, 2016, 2017 have witnessed 3 panels on the purpose, history and definition of HS, respectively. This paper reports on a comprehensive review conducted by the panelists on HS and its applications. The aim of the paper is to move the debate forward by exploring potential platforms for developing concrete avenues for research on HS in conjunction with the modelling life cycle.

Construction systems and projects comprise complex combinations of subsystems, processes, operations, and activities. Discrete Event Simulation (DES) has been used extensively for modeling construction systems, addressing system complexity, and analyzing system behavior. However, while DES is a powerful tool for capturing operations as they occur in reality, DES does have difficulty modeling context and its mutual effects on the operational components of a system. System Dynamics (SD), on the other hand, captures feedback loops that are derived from the context level of a system and that can anticipate system behavior; nevertheless, SD cannot effectively model the operational parts of a system. Hybrid SD and DES modeling provide a set of tools that use the capabilities, while improving upon the disadvantages, of these two approaches. Although initial efforts to develop hybrid SD-DES modeling dates back to the late 1990s, in the construction industry, there are relatively few studies in this area, and there is still no robust architecture for hybrid system developers. This paper addresses these issues by proposing a comprehensive hybrid simulation architecture based on the High Level Architecture (HLA) infrastructure, which can be used by hybrid simulation developers in the construction industry. A typical steel fabrication shop has been modeled based on the proposed architecture, and it has been compared with the ideally developed hybrid simulation architecture.

The complexity of systems and their multi-faceted relationships mean that the combined application of simulation methods, or hybrid simulation, will enable synergies across techniques and will provide greater insights to problem solving. In this paper we present a hybrid simulation case study in which two models have been developed; an Agent-Based Simulation (ABS) and a Discrete-Event Simulation (DES). The models simulate the operations of offshore wind turbines, with the ABS modelling turbine failure (through, among other things, simulating a degradation model of turbine components) and the DES modelling Maintenance, Repair and Operations (MRO) strategies. The models have been implemented in NetLogo and Simul8 respectively. In this paper we review strategies for the interoperation of our hybrid ABS-DES model and discuss the advantages of using the IEEE 1516 HLA standard for distributed simulation for dynamic information exchange and synchronized model execution.

Simulation of Product-Service-Systems Piloting with Agent-Based Models (Outlined Revision)

Discrete-Event Simulation (DES) has been the mainstream in the area of construction simulation. Recently, research has shown the possibility of applying System Dynamics (SD) to the construction management discipline. Unlike DES, which is based on statistical analysis and queuing theory, SD is able to represent the complex system of construction management through capturing feedback effects, managerial actions, and soft variables such as schedule pressure and morale, which are important in determining project behavior. Though SD is capable of representing such variables and relationships, the validity of SD models has often been questioned because when modeling with SD, it is difficult to attain as much detail as when modeling with DES. Therefore, SD has not been fully adopted to simulations in the area of construction management, despite its advantages. To gain widespread use in the construction management area, SD needs to address operational details. In this paper, an SD based earthmoving process model is presented to explore ways in which operation details can be represented using SD. Initially, an SD based earthmoving model is proposed and for the purpose of comparison, and is tested against a DES counterpart model, created using STROBOSCOPE. The tests have shown that the simulation results generated by the SD model are as accurate and reliable as those of the STROBOSCOPE models. The specifics of this comparison are identified, including how the SD model is created from and compared to DES model at an operational level. These research findings support the widespread use of SD in the area of construction management; thus, aiding in the representation of the complexities inherent in construction.

Autonomous vehicles (AVs) may have a transformative influence on transportation systems. AV use is expected to reduce crash, transport costs, and land use. However, the net effects of the autonomous vehicle are unknown because there are no fully-autonomous vehicles in reality. The simulation systems are indispensable to be built up not only for cost but also for obtaining massive data. An agent-based simulation is capable of simulating the entities in a complex system, such as the AV system. Considering the potential of agent-based models to complement existing models and the difficulty to understand, to duplicate and to compare different agent-based models, this paper review existing agent-based modeling of autonomous vehicles with the help of ODD protocol, which has been applied in the review of agent-based land-use change models and agricultural policy evaluation. The significant variables that researchers have taken into consideration in sensitivity analysis and in the different scenarios are explored in the paper. Fleet size is the primary variable researchers considered, and we also discussed the probable causes of different AV replacement rates. We have assessed the quality of reviewed articles to gain more acceptance from the agent-based modeling community. Most of the reviewed papers follow the ODD protocol of agent decision making. However, in order to improve the level of modeling transparency, the executable and related data shall be available, at least the data source and the programming language/simulation platform should be provided. Several recommendations for further research are also presented.

Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control difficult and requires storage units. The goal of this research is to develop a flexible capacity planning tool, which will allow us to obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions. The tool is based on hybrid (system dynamics and agent-based models) simulation and meta-heuristic optimization. In particular, the proposed tool has been developed for scenarios, where photovoltaic generators and storage units (compressed-air-energy-storage and super-capacitors) are used to supply energy demands in a region characterized by different house-holds considering different times and seasons. The constructed tool has been used to test impact of several factors (e.g. demand growth, efficiencies in PV panel and storage techniques) on the total cost of the system. Initial results look quite promising.

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.

Background: While there is a great deal of discussion about complex adaptive systems in the field of evaluation, little has been written about the expression of complex adaptive systems in terms of agent-based models, the execution of those models as computer simulations, or the tight integration of agent-based modeling with traditional evaluation methods. These topics need to be explored if evaluation is to move beyond using complex adaptive systems in an exclusively heuristic fashion. Purpose: This paper advocates for the integration of agent based modeling into traditional evaluation activities. We advance this position in order to spark a movement toward incorporating the formal application of complex adaptive systems into the theory and methods of evaluation. To make our case, we provide a hypothetical example of how interactions between an agent-based model and evaluation can provide unique and powerful understanding about the adoption of evidence-based practices in the field of addiction treatment. We advance our argument by addressing four questions: Why combine evaluation methods with simulation methods? Why use a complex adaptive systems approach? Why add agent-based modeling to the mix of evaluation tools? What is the relationship between agent-based modeling and the theories and methods of evaluation that are being developed to deal with surprise, unintended consequences, and program evolution? Setting: Not applicable. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: Not applicable. Findings: Not applicable.

As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial and regulatory environments in which they operate. A new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation (ABMS) approach. The purpose of an ABMS model is not necessarily to predict the outcome of a system but to reveal and understand the complex and aggregate system behaviors that emerge from the interactions of the heterogeneous individual entities. Emergent behavior is a key feature of ABMS and is not easily inferred from the simple sum of the behavior of its components. By relying on both established engineering modeling techniques as well as advanced quantitative economic market principles, the ABMS approach is uniquely suited to addressing the strategic issues of interest to different market participants as well as those of market monitors and regulators.