Multi-method Simulation Model Research Articles

A multi-method simulation model to investigate the impact of sunflower seed segregation on silos

The South African sunflower industry is considering transferring to a quality-based marketing system driven by an incentive. However, the ability of silos to offer necessary segregation services is critical in such a transition (Baker et al., 1997) and the silo industry is concerned about the negative impact segregation could have on operations and finances. This paper proposes a multi-method simulation approach to quantify the impact of quality-based segregation and sunflower farmer response to the incentive on silo bin utilisation and the ability of the silo to store contents of arriving trucks (service level). A combination of agent-based simulation, discrete event simulation and Bayesian network sampling is used to capture system behaviour where data is scarce. Therefore, in this study, a mixed methods ABM and DES model is implemented in a new environment: a grade-based segregation problem in the South African silo industry. Several scenarios are modelled to cross-validate methods and to tease out the impact of farmer response on the results. The model is applied to a case study silo complex to test the concept. Results obtained for the case study silo show a significant negative impact on costs due to lower service levels and bin utilisation, incurring relocation and opportunity costs. Overall, this study highlights that it is necessary to consider the impact that sunflower segregation could have on each unique silo complex and provides a method to quantify the stated impact.

Modeling the influence of safety aid market penetration on traffic safety: Case of collision warning system for powered two-wheelers

Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU “Vision Zero” goal: “reduce road deaths to almost zero by 2050”. MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

Multimethod simulation approach for capacity design of a truck parking area in city ports

The port induced freight can cause traffic congestion problems in city ports if road freight transportation is used. The secondary congestion problem arises from the pooling of the trucks at the port gates because of the delays of the port operations. The absence of the truck parking areas inside the port causes additional truck trips between the port and auxiliary truck servicing areas around the port. For reducing the impacts of the associated problems of the port induced truck traffic, truck parking areas can be used as buffer zones between the port and city. The purpose of this study is to develop a Decision Support System (DSS) with using multimethod simulation and cost optimization model for the capacity design of a truck parking area for a city port. The preliminary design of the parking area is used to estimate the development cost and outputs of the simulation model is coupled for the capacity optimization for truck arrival scenarios. The methodology is implemented for a case study of the İzmir city port in Turkey. The results of the study indicated that significantly different parking area capacities are required for different truck dwell times for the time restricted and unrestricted truck arrivals

Modeling Patient Flow in an Emergency Department under COVID-19 Pandemic Conditions: A Hybrid Modeling Approach.

Emergency departments (EDs) had to considerably change their patient flow policies in the wake of the COVID-19 pandemic. Such changes affect patient crowding, waiting time, and other qualities related to patient care and experience. Field experiments, surveys, and simulation models can generally offer insights into patient flow under pandemic conditions. This paper provides a thorough and transparent account of the development of a multi-method simulation model that emulates actual patient flow in the emergency department under COVID-19 pandemic conditions. Additionally, a number of performance measures useful to practitioners are introduced. A conceptual model was extracted from the main stakeholders at the case hospital through incremental elaboration and turned into a computational model. Two agent types were mainly modeled: patient and rooms. The simulated behavior of patient flow was validated with real-world data (Smart Crowding) and was able to replicate actual behavior in terms of patient occupancy. In order to further the validity, the study recommends several phenomena to be studied and included in future simulation models such as more agents (medical doctors, nurses, beds), delays due to interactions with other departments in the hospital and treatment time changes at higher occupancies.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents’ geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

A global scheduling approach for cycle time control in complex manufacturing systems

This paper proposes a novel global scheduling approach for cycle time control strategy in large complex manufacturing systems with multiple workcentres, such as semiconductor manufacturing systems. The interaction between workcentres is taken into account by using global information such as release quantities, the Work-In-Process (WIP), cycle time targets and machine capacities. Local scheduling decisions in workcentres are steered by production targets, i.e. quantities of products to complete in each operation and each period on a scheduling horizon. These global production targets are determined by a mathematical model (global scheduling model), which optimises the satisfaction of cycle time targets. One of the major innovations of the proposed model is that it relies on the temporal trace of the WIP. The mathematical model is coupled with a generic multi-method simulation model for evaluation purpose. Computational experiments conducted on industrial data show that our global scheduling approach efficiently controls the cycle times of products.

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

Robust optimization of information flows in global production networks using multi-method simulation and surrogate modelling

Low information exchange in global production networks results in long response time to disruption and negative performance impact. Digitalization enables a more intensive information exchange. This paper analyses the performance of order management, quality problem resolution and engineering change management in production networks with respect to different disruptions and information flows. Cause-effect relationships are revealed based on a multi-method simulation model and statistical experiments. Using surrogate modelling and robust optimization, a target picture for information exchange is determined. The benefits of the approach are demonstrated using a case study for the production of metal-plastic parts for the automotive supplier industry.

Homeostatic representation for risk decision making: a novel multi-method simulation approach for evacuation under volcanic eruption

All decision-making processes are complex and include a high number of variables. Particularly, decisions related to disaster risk reduction despite having an inherent uncertainty, depend on many factors associated with the inhabitants in danger, risk disaster management authorities, previous experiences, among others. Although considerable research has been carried out to understand the evacuation processes undergoing among different types of natural hazards, few of them consider psychosocial variables under a dynamic approach. This research proposes a novel way of representing decision making using the risk homeostasis theory approach. We developed a multi-method simulation model to gain an understanding of how individuals react and what are the decision-making processes undergoing a volcanic eruption. For this, we developed a system dynamics (SD) model that captures the psychosocial decision-making process of individuals facing a volcanic eruption. This theory proposes that individuals will make decisions to balance their levels of perception and acceptance of risk and that, in addition, they depend on certain motivational and cognitive variables. Once the evacuation decision making was captured by the SD model, we represented the evacuation process through agent-based simulation based on the relationship between evacuation probability and the difference between risk perception and risk acceptance levels. For experimentation and validation methods, we worked with the 2008 Chaiten (southern Chile) volcanic eruption and further population evacuation as a use case scenario.

Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

The environmental impact of supply chains has motivated many studies in this area. This research proposes a novel multi-method simulation approach which combines agent-based and discrete event modelling approaches to model a green closed-loop supply chain and optimise it through an optimisation via simulation technique. A closed-loop supply chain is developed under the demand uncertainty to minimise total cost and total greenhouse gases (GHG) emissions and maximise management preference of the supply chain in the presence of a competitor. Taguchi design of experiments method is used to generate scenarios, then total cost and total GHG emissions are recorded through simulating the scenarios. Management preference is determined based on decision makers' opinion. Scenarios are ranked against three attributes with the proposed panel-group TOPSIS method. Inventory replenishment parameters, pricing and advertising policies, and transportation type are determined via solving the model. An automotive industry case is provided to demonstrate the model's capabilities.

Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

Using simulation-based optimization in the context of IT service management change process

Today's IT systems and IT processes must be ready to handle change in an efficient and responsive manner to allow businesses to both evolve and adapt to a changing world. In this paper we describe an approach that consists of using simulation based multi-objective optimization to select optimal ITIL change management process strategies that help IT managers achieve process efficiency as a Critical Success Factor (CSF). A multi-method simulation model, which is based on agent-based and discrete-event simulation paradigms, has been built to simulate the whole process lifecycle, since the change initiation until its closure. As most engineering problems, assuring an efficient delivery of the change management process requires optimizing simultaneously the corresponding Key Performance Indicators (KPIs) in which the process-efficiency CSF can be rolled down. In this paper, we show the results of applying two well-known Multi-Objective Evolutionary Algorithms, namely NSGA-II and SPEA2, to obtain a set of optimal solutions for the KPIs associated with delivering process efficiency as a CSF. We also compare the results obtained with the output from the single-objective optimization algorithm provided by the simulation tool. The experimental work included shows how the approach can provide the IT manager with a wide range of high quality solutions to support them in their decision-making towards CSF achievement.

Investigating Causal Relationships between Disruptions, Product Quality and Network Configurations in Global Production Networks

Companies nowadays act in global production networks. These networks offer advantages such as production close to market as well as the exploitation of low factor costs. However, due to their interlinkage and complex structure, the resulting networks are characterized by a high susceptibility to disruptions. This paper presents an approach, which quantitatively examines the effects of disrupting events in production networks. The main focus of the study is on the question of how product quality is influenced by quality-related disruptions. To describe the analyzed relationships best, a Kriging based metamodel is adapted to the behavior of the developed multimethod simulation model. The results of the investigation are formalized causal relationships between the error probabilities in production networks, the inspection frequencies of the production systems involved, the respective generic network configurations and the observed PPM quality score.

A multi-method simulation approach for evaluating the effect of the interaction of customer behaviour and enterprise strategy on economic viability of remanufacturing

The economic viability of remanufacturing is shown to be significantly affected by the interaction between customer behaviour and strategic decisions of the manufacturer. The paper investigates this interaction using a multi-method simulation model combining a multi-agent-based model of customers and a system dynamics based model of the remanufacturing enterprise. The model uses validated approaches for each component and is used to investigate, analyse and forecast the long-term effects of strategic decisions of the remanufacturing enterprise on customer behaviour and the consequent effects on the business environment that determine the profitability of the enterprise.

UTASiMo: a simulation-based tool for task analysis

Research on task analysis and human performance has focused on the development of adequate tools, models, and methods to understand, analyze, and improve the relationship between humans and systems. As technology continues to advance and to change the nature of human work, techniques of analysis are changing to meet the new needs. This work attempts to fill the gaps in the current task analysis tools and describes the architecture and development of a simulation model, named UTASiMo. UTASiMo is a simulation tool that aims to enhance task analysis by automatically generating a multi-method simulation model for well-defined tasks based on a spreadsheet template filled in by the user. The generated model analyzes and simulates tasks performed by individual simulated agents representing human operators while accounting for the estimation of an operator’s utilization and error prediction. This work highlights the design, development, and evaluation of the hybrid architecture (discrete event and agent based) of UTASiMo. The development of the system dynamics model, which is responsible for the human error assessment, is a work in progress and is excluded from the present paper. A real-world case study has been adapted to evaluate the hybrid architecture of UTASiMo. The same case study was modeled using the Micro Saint simulation tool. The results produced by UTASiMo were compared with the real-world data as well as with the results produced by Micro Saint for validation purposes. The comparisons indicate the validity of the UTASiMo-generated model and also that the hybrid architecture produces more variability in the results than using only one method. The comparisons also show promise that the tool will reduce the time and effort of the task analysis simulation.

A Multi-Method Simulation Modelling for Semiconductor Manufacturing

This paper proposes a flexible multi-method simulation model in order to implement and validate a novel approach for semiconductor facility. The model combines discrete event and agent based simulation methods. The general framework of the model includes two main agents which are lot and tool group. The process inside the tool group agents is using discrete event simulation. The model is developed based on real semiconductor manufacturing process data and is re-usable since external data are read from external files. A set of experiments on real-world data illustrate the performance of the model on extracting relevant information, detecting critical problems in manufacturing processes and so applying new objectives and strategies. Two scenarios in which lots of one product type are accelerated are discussed and compared.

Action Driven Decision Modeling Framework towards Formulating Software Project Management Tacit Knowledge

Managing the tacit knowledge in organizations raises substantial challenges in regards of the associated processes. In software project management decision making has the critical role in this scenario since it defines the manager’s responsibilities and stems from the various sources linked to the process. With the respect of tacit knowledge, the decision making constructs the essential foundation and thereby it needs a reliable framework for modeling of the decision structure. In this paper a conceptual multi-method simulation based framework will be introduced in a modality to cover multiple levels of the decision structure over software project management process. The methods used are integrated towards a multi-method simulation model whereas each of these methods exclusively realizes distinct aspect of software project management. The framework evolves the manner of decision making by a paradigm which establishes the foundation for a tactical level understanding and decision support for practitioners. At the results section an optimal policy for the framework will be presented.