Sortie Generation Rate (SGR) is an important metric for air dominance. Lockheed Martin must demonstrate that the Air System can fly the sorties during an allotted time and deliver the capability to the war fighter. Aircraft turnaround time- the time between when the aircraft touches down, refuels, rearms, and completes inspections in order to release the aircraft, to aircraft wheels up - plays an important role in achieving the SGR requirement.
The advantages of combined simulation techniques have been already frequently discussed and are well-covered by the recently published literature. In particular, many case studies have been presented solving similar domain-specific problems by different multi-paradigm simulation approaches. Moreover, a number of papers exist focusing on theoretical and conceptual aspects of hybrid simulation. However, it still remains a challenge to decide, whether combined methods are appropriate in certain situations and how they can be applied. Therefore, domain-specific user guides for multi-paradigm modeling are required combining general concepts and best practices to common steps. In this paper, we particularly outline three major processes targeting to define structured hybrid approaches in domain-specific contexts, and we focus on some practical issues aiming to a sustainable model development. Finally, an example hybrid methodology for problems in healthcare will be presented.
The weather is unpredictable and can have a large impact on the profitability of seasonal businesses, particularly if staffing requirements are highly temperature-dependent. In this paper we describe our efforts in developing a what-if analysis tool to assist affected Small and Medium Enterprises in determining the best case scenario for timing hiring new staff and deciding the optimum length of temporary employment contracts.
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).
Although research into simulation of construction continues to advance and thrive in the academic world, application of simulation in the construction industry remains limited. Stakeholders on construction projects have yet to adopt simulation as their default tool of choice for managing large complex projects, instead of traditional techniques, which are often inadequate. This paper describes the building of an asphalt paving simulator, as an example of the rigor and effort required in developing construction simulation models, and then briefly describes an alternative model building method currently being researched which may potentially make it easier and faster for stakeholders to quickly build construction simulation models.
Prospective Health Technology Assessment allows early decision making for innovative health care technologies. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. 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.
This paper describes the results of a practical experience of business process improvement and change. the business modeling approach, carried out through an agent-based model, has been applied to an operational process with the aim to reduce the overlapping of the operational phases and to improve the time-efficiency. Simulation outcomes and results are discussed.
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.
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.