Hybrid simulation with loosely coupled system dynamics and agent-based models for prospective health technology assessments

Due to the ageing of the world population, the demand for technology innovations in healthcare is growing rapidly. All stakeholders (e.g., patients, healthcare providers and health industry) can take profit of innovative products, but the development degenerates often into a time consuming and cost-intensive process. Prospective Health Technology Assessment (ProHTA) is a new approach that combines the knowledge of an interdisciplinary team and uses simulation techniques to indicate the effects of new innovations early before the expensive and risky development phase begins. In this paper, we describe an approach with loosely coupled system dynamics and agent-based models within a hybrid simulation environment for ProHTA as well as a use-case scenario with an innovative stroke technology

The service productivity learning cockpit – a business-intelligence tool for service enterprises

Computer simulation is a way to imitate business processes based on reality. Due to the fact that the environment in hospitals is highly dynamic with local autonomy of stakeholders participating in the business processes, we found an agent–based modeling and simulation (ABMS) approach to be most suitable and it is therefore applied in this context. From an inception to a running simulation, followed by an analysis of the output, we need to keep in mind our user’s physical problem as well as their capability of digesting the results. An interface between a computer modeler/programmer’s deliverable and a user like a hospital manager who learns from the simulated behavior of physical reality, is a visualization tool. We call this tool a “Learning Cockpit” (LC). Although a manager has experience in managing their business and they use personal qualities to positively drive their organization in challenging business environments, a simulation provides them additional support in decision process. With the help of simulation, they should be able to clearly and concisely grasp the information about the current operations, the resources involved and the inherent costs to get an output. They should be able to measure the performance of the current setup, and if necessary, make some changes and bring more value to the organization

Saving and Restoring Anylogic 6 Model Snapshot

We are pleased to announce a new feature available in AnyLogic 6 Professional Edition: now you are able to save the full state of a model (the snapshot) during runtime to a file, restore it at a later time and continue running simulation from the same point. This feature may be useful in several cases:

  • Resilience: when a simulation takes very long time to complete, it may make sense to save its state periodically so that you do not have to start everything...

A multi-structural framework for adaptive supply chain planning

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multistructural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a “one-way” partial optimization to the feedbackbased, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management

Supply chain multi-structural (re)-design

In the framework of supply chain (re)- design (SCD), different structures (functional, organizational, informational, etc.) are (re)- formed. These structures are interrelated and change in their dynamics. How is it possible to avoid structural incoherency and consistency and to achieve comprehensiveness by (re)- designing supply chains? This paper introduces a new approach to simultaneous multi-structural SCD with structure dynamics considerations. We elaborate a new conceptual model and propose new tools for multi-structural SCD – multi-structural macro-states and dynamical alternative multi-graphs. The research approach is theoretically based on the combined application of operations research, agent-based modelling, and control theory. The results show the multi-structural and interdisciplinary treatment allows comprehensive and realistic SCD problem formulation and solution. We emphasize the flexibility of the proposed approach and optimization-supported simulation. The proposed methodology enhances managerial insight into supply chains at the strategic and tactical levels and serves to assist decision-makers in SCD

The aero-engine value chain under future business environments

Agent-based modelling is gaining popularity for investigating the behaviour of complex systems involving interactions of many players or agents. In this paper an agent-based simulation modelling technique is applied to understand the long term implications of strategy decisions for an aerospace value chain. The industry has unique elements including new business models, high levels of collaboration, long product lifecycles and long periods before positive paybacks are realised. Forecasting market conditions over this long term lifespan is inherently problematic and adds further complexity when devising a strategy. The model described includes all the major players and entities in the value chain and their interactions. Illustrative results are presented to demonstrate how the simulation approach can be used to evaluate strategy and policy decisions and their operational implications over the long term

Using AnyLogic and agent based approach to model consumer market

In the highly dynamic, competitive and complex market environments (such as telecom, insurance, leasing, health, etc) the consumer’s choice essentially depends on a number of individual characteristics, inherent dynamics of the consumer, network of contacts and interactions, and external influences that may be best captured within the Agent Based modeling paradigm. The Agent Based modeling is especially advantageous in the consumer market domain as it allows to leverage the full amount of individual-centric data from the CRM (Customer Relationships Management) systems highly available these days. Although there are no universal straightforward instructions for building Agent Based models, there are certain common steps and patterns. The goal of this paper is to introduce the patterns in consumer market modeling most frequently met in our consulting practice. The modeling language of AnyLogic is used throughout the paper

A hybrid simulation optimization approach for supply chains

The main idea of our approach is to combine discrete-event simulation and exact optimization for supply chain network models. Simulation models are constructed in order to mimic a real system including all necessary stochastic and nonlinear elements. Such simulation models are used as proving grounds for analyzing and improving a real situation on a trial-and-error basis. A traditional optimization method on top of a simulation model has major disadvantages: The optimization method uses the simulation model as a black-box. Information about the structure of the problem is not available and cannot be used for an intelligent optimization strategy

Fully agent based modellings of epidemic spread using AnyLogic

The problem that we are going to conquer subsequently is a slight modification of the ARGESIM Comparison 17. This comparison does ask for the simulation of a SIR-type epidemic by means of lattice gas cellular automata (LGCA). At the end of this paper we will compare the outcome of such an approach with our ABS-result. The task is to model a SIR-type epidemic, an epidemic simplified in several ways. For example we assume a constant population over the whole simulation, thus no births or deaths...

Heterogeneity and network structure in the dynamics of diffusion

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.