Hybrid simulation challenges and opportunities: a life-cycle approach

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Construction projects are characterized by their dynamic nature and operational details. This paper presents a hybrid simulation methodology; designed to model construction projects. The methodology utilizes Discrete Event Simulation (DES) and System Dynamics (SD). DES has been widely used in modeling construction operations; however, it lacks the ability to model the global aspects of operations being modeled and the cause-effect relations of simulation variables. SD is utilized to circumvent these limitations. Both simulation methods provide valuable decision support but none is individually capable of capturing the holistic nature of the operation being modeled. The developed methodology integrates DES and SD to utilize their respective advantages in simulating construction operations. The developed methodology encompasses five stages: 1) identification of model objectives, 2) decision criteria to assist in selecting simulation methodology, 3) building simulation model and identification of interface variables, 4) computation framework and 5) implementation and testing. The paper describes the essential features of the developed methodology and its computational framework and focuses primarily on the modeling aspects of SD. A case study project is analyzed to demonstrate the use of the developed methodology and to highlight its capabilities.

With increased market competition and the development of Industry 4.0, production operations is being driven towards increased flexibility. Multi-product flow and variable production processes on the shop floor complicate waste identification, analysis, and reduction. To continuously improve based on real-time information in dynamic production processes, this paper proposes an integrated approach of dynamic value stream mapping and hybrid simulation(DVSM-HS). The dynamic value stream mapping(DVSM) is extended from time temporal and economic dimensions respectively to decompose and integrate work-in-process sequences and processes. A dynamic cost model is constructed based on time-driven activity-based costing to extract the variable costs of production resources. By combining hybrid simulation of discrete events and system dynamics, variations in multi-product flows are captured and mapped, and their economic impact over time is quantified. A case study in an automotive painting workshop demonstrates the practical application of waste reduction and decision-making across different scenarios. The results show that the proposed DVSM-HS can address process variability by identifying the temporal and spatial characteristics of waste. Based on waste localisation and economic evaluation, this study supports managers diagnose problems and conduct Lean improvements in real-time operations, optimising resource and capability allocation to enhance production profitability.

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

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.

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.

This paper provides an empirical study on the comparison of model building in Discrete-Event Simulation (DES) and System Dynamics (SD). Verbal Protocol Analysis (VPA) is used to study the model building process of ten expert modellers (5 SD and 5 DES). Participants are asked to build a simulation model based on a prison population case study and to think aloud while modelling. The generated verbal protocols are divided into 7 modelling topics: problem structuring, conceptual modelling, data inputs, model coding, validation & verification, results & experimentation and implementation and then analyzed. Our results suggest that all modellers switch between modelling topics, however DES modellers follow a more linear progression compared to SD modellers. DES modellers focus significantly more on model coding and verification & validation, whereas SD modellers on conceptual modelling. This quantitative analysis of the processes followed by expert modellers contributes towards the comparison of DES and SD modelling.

For complex systems that embed automation, but also rely on human interaction for guidance and contingency management, holistic models are needed that provide for an understanding of the individual human and computer elements, and address the critical interactions of such complex systems. Discrete event simulation (DES) models and system dynamics (SD) models are two different approaches that can be used to address these requirements. Both modelling approaches can support the designers of future autonomous vehicle (AV) systems by simulating the impact of alternate designs on vehicle, operator, and system performance. However, the DES modelling approach is likely best suited for using probabilistic distributions to accurately model an operator who is a serial processor of discrete tasks, as well as an environment with randomly occurring events. The SD modelling approach is better suited for modelling continuous performance feedback that is temporally dependent and is affected by qualitative variables such as trust.

Computer simulation has defined itself as a reliable method for the analysis of stochastic and dynamic complex systems in both academic and practical applications. This is largely attributed to the advent and evolution of several simulation taxonomies, such as, Discrete Event Simulation, Continuous Simulation, System Dynamics, Agent-Based Modeling, and hybrid approaches, e.g., combined discrete-continuous simulation, etc. Each of these simulation methods works best for certain types of problems. In this paper, a discrete-continuous simulation approach is described for studying train and pedestrian traffic interactions for purposes of decision support. A practical operations problem related to commodity train operation within two small towns in Alberta, Canada, is then used to demonstrate the implementation of the approach within the Simphony.NET simulation system. Simulation results generated are presented.

To be valuable to biological or biomedical research, in silico methods must be scaled to complex pathways and large numbers of interacting molecular species. The correct method for performing such simulations, discrete event simulation by Monte Carlo generation, is computationally costly for large complex systems. Approximation of molecular behavior by continuous models fails to capture stochastic behavior that is essential to many biological phenomena. We present a novel approach to building hybrid simulations in which some processes are simulated discretely, while other processes are handled in a continuous simulation by differential equations. This approach preserves the stochastic behavior of cellular pathways, yet enables scaling to large populations of molecules. We present an algorithm for synchronizing data in a hybrid simulation and discuss the trade-offs in such simulation. We have implemented the hybrid simulation algorithm and have validated it by simulating the statistical behavior of the well-known lambda phage switch. Hybrid simulation provides a new method for exploring the sources and nature of stochastic behavior in cells.

This paper presents the decision problem of selecting a collision-warning activation algorithm for Rear-End Collision Alert Signal (RECAS) systems installed in motorcycles and other powered two-wheelers. Using Agent-Based Modelling, Discrete Event Simulation and System Dynamics, we have developed a hybrid simulation model of three scenarios of rear-end collisions to measure the performance of several algorithms found in the literature. The best-performing algorithm, by Hirst & Graham, has reduced the probability of collisions on average by 31.21% in comparison with the baseline scenario of using a standard brake light. Using this algorithm also resulted in up to 11.20% reduction in the estimated severity of injuries when a rear-end collision was not averted. As the Hirst & Graham algorithm was the most effective in all the tested emergency braking scenarios and across all the tested speed limits, we recommend this algorithm to be used in RECAS systems in powered two-wheelers.

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.

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.

For about a decade, we recognize a growing interest in hybrid simulation. Mixing different modeling paradigms promises to overcome limitations inherent to single paradigm approaches and help build models that are both realistic and accessible at the same time. However, a commonly accepted formalism to specify this kind of model on the conceptual level is barely found. We introduce a hybrid agent-based modeling formalism that integrates elements from the most widely applied paradigms: agent-based modeling and simulation, discrete event simulation, and system dynamics. The so-called HABM formalism is founded on hybrid automaton theory and DEV&DESS. It supports, among others, the specification of dynamic structure agent-based networks. We apply HABM to a strategic workforce planning problem by modeling an organization as a complex adaptive system.

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.