Academic articles

Partial Paradigm Hiding and Reusability in Hybrid Simulation Modeling Using the Frameworks Health-DS and I7-Anyenergy

Many complex real-world problems which are difficult to understand can be solved by discrete or continuous simulation techniques, such as Discrete-Event-Simulation, Agent-Based-Simulation or System Dynamics. In recently published literature, various multilevel and large-scale hybrid simulation examples have been presented that combine different approaches in common environments.

A Multi-Paradigm Modeling Framework for Modeling and Simulating Problem Situations

This paper proposes a multi-paradigm modeling framework (MPMF) for modeling and simulating problem situations (problems whose specification is not agreed upon). The MPMF allows for a different set of questions to be addressed from a problem situation than is possible through the use of a single modeling paradigm.

Iterative Simulation and Optimization Approach for Job Shop Scheduling

In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing).

Electric Vehicle Driver Simulation using Agent-Based Modeling

Recargo has been developing an agent-based model with the AnyLogic tool to help us simulate the charging patterns of electric vehicle drivers in California. Our goal is to better understand the potential value from delivering electricity grid services with these vehicles. Development has only been underway for a few weeks, but in that time we’ve been able to use AnyLogic’s accessible interface and Java coding tools to quickly build and test a proof-of-concept model with which we can explore the potential for a more sophisticated and complex effort.

Investigating Output Accuracy for a Discrete Event Simulation Model and an Agent Based Simulation Model

In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study.

Dynamic Learning in Human Decision Behavior for Evacuation Scenarios under BDI Framework

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed.