Academic articles

Signal Phase Timing Impact on Traffic Delay and Queue Length-a Intersection Case Study


Traditional intersection traffic signal control strategy is pre-determined signal with certain phase timing length for each circle. Studies focusing on adaptive traffic signal strategy have somewhat achieved the goal of reducing traffic system delay to some extent. However, few of them capture the benefit of using the queue length as the criteria under the connected vehicle environment, and this paper focuses on firstly identifying the potential saving of average system delay with agent-based simulation modeling, and secondly finding out the relationship between average system delay and average queue length for traffic approaching the signalized intersections. Through applying the agent-based simulation modeling approach in AnyLogic, findings show that average system delay could be reduced using optimized parameters (e.g. arrival rate, signal phase length, etc.), specifically, 5.29% saving of total average system time, 4%-28% traffic queue reduction for different traffic lanes, and a positive relationship between average system de-lay and the average traffic queue length is detected.

Simulation testbed for the analysis of beneficial business strategies for the airbus A350 production ramp-up


The production ramp-up of new aircraft is characterized by high complexity and planning and control chal-lenges caused by complex product design, supply chain and production processes. In the past, this resulted in significant delays and increased costs of the production ramp-up. Novel business strategies and planning and scheduling technologies promise better production control and risk mitigation during the ramp-up phase. The European research project ARUM has developed those business strategies and a new distributed decision support solution based on knowledge processing technologies. A simulation testbed was used to identify the most beneficial business strategies and to evaluate linked control strategies for the industrial use case of the Airbus A350 production ramp-up. This paper discusses the potential of simulations for the business strategy definition and for the validation of linked control strategies from the industrial end-user perspective.

Auction policy analysis: an agent-based simulation optimization model of grain market


National grain reserve is important in terms of responding to disasters and the unbalance between supply and demand in many countries. In China, the government supplements grain supply through online auctions. This study focuses on the auction policy of national grain reserve. We develop an agent-based simulation model of China’s wheat market with detail descriptions of different agents, including national grain reserve, grain trading enterprises and grain processing enterprises. Based on this model, the Optimal Computing Budget Allocation (OCBA) simulation optimization method is adopted to analyze the characteristics of optimal decision variables under different scenarios, with an objective to minimize the fluctuation of wheat price. We obtain some insights about operations of national grain reserve. As the first agent-based simulation model about national grain reserve and grain market, this model can be widely used in agricultural economics, and can provide policy supports to the government.

Increasing capacity utilization of shuttle trains in intermodal transport by investing in transshipment technologies for non-cranable semi-trailers


For shuttle trains with a fixed transport capacity which are the dominant operating form in intermodal transport, increasing capacity utilization is of crucial importance due to the low marginal costs of transporting an additional loading unit. Hence, offering rail-based transport services for non-cranable semi-trailers can result in additional earnings for railway companies. However, these earnings have to compensate for the investment costs of the technology. Based on a dynamic investment calculation, this paper presents a simulation model to evaluate the economic profitability of transshipment technologies for non-cranable semi-trailers from the railway company’s perspective. The results depend on the capacity utilization risk faced by the railway company. In particular, if the railway company does not sell all the train capacity to freight forwarders or intermodal operators on a long-term basis, investing in technology for the transshipment of non-cranable semi-trailers can be economically profitable.

A structured approach for constructing high fidelity ED simulation


This paper presents a structured approach to building a high-fidelity simulation for an emergency department. Our approach has three key features. First, we use the concept of modules as a building block for modeling. A module is a minimum unit that has clinical or administrative meanings in ED operation, and it consists of low level operational activities. Second, we use a structured template to formally represent modules, and we adopt notations and grammars from the business process modeling notation. This provides an enhanced clarity and transparency, which proves very useful in extracting necessary data from a hospital database or from interviewing ED staff. Finally, we define an interface, specifically data structure and handler, for converting information represented in the modules into simulation languages. This interface makes it possible to seamlessly link the modeling process to the implementation process in the simulation construction.

An agent-based framework to study occupant multi-comfort level in office buildings


With the trend towards energy efficient buildings that diminish fossil fuel usage and carbon emissions, achieving high energy performance became a necessity. Allowing occupants to be actively involved during the design and operation phases of buildings is vital in fulfilling this goal without jeopardizing occupant satisfaction. Although different occupant behavior types were considered in prior research efforts, recent tools did not however examine simultaneously visual, thermal and acoustic comfort levels. This paper presents work targeted at efficiently studying occupant multi-comfort level using agent-based modeling with the ultimate aim of reducing energy consumption within academic buildings. The proposed model was capable of testing different parameters and variables affecting occupant behavior. Several scenarios were examined and statistical results demonstrated that the presence of different occupant behavior types is deemed necessary for a more realistic overall model, and the absence of windows results in an acoustic satisfaction with a decrease in (HVAC) use.

Improving patient access to a public hospital complex using agent simulation


This paper uses agent based simulation to assess the effect of redesigning the points of access to a major public hospital complex in Chile, where nearly 15,000 people will pass through daily. The study is carried out by simulating pedestrian traffic in order to calculate density maps and service levels in hospital access and ramps. The simulation allows us to evaluate the flow of people and assess the layout performance, by identifying high patient flow areas and congested pedestrian traffic zones. By using this approach, it is possible to suggest changes to the original design and to improve pedestrian flow at hospital access points and ramps. The suggested changes reveal that pedestrian indicators could be improved, which in turn would improve the level of satisfaction of patients, relatives, and hospital personnel. A higher satisfaction level would help to reduce stress linked to hospital facilities and crowded spaces.

From desktop to large-scale Model Exploration with Swift/T


As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any “black box” application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.

Agent-Based Modeling: an Introduction and Primer


Agents are self-contained objects within a software model that are capable of autonomously interacting with the environment and with other agents. Basing a model around agents (building an agent-based model, or ABM) allows the user to build complex models from the bottom up by specifying agent behaviors and the environment within which they operate. This is often a more natural perspective than the system-level perspective required of other modeling paradigms, and it allows greater flexibility to use agents in novel applications. This flexibility makes them ideal as virtual laboratories and testbeds, particularly in the social sciences where direct experimentation may be infeasible or unethical. ABMs have been applied successfully in a broad variety of areas, including heuristic search methods, social science models, combat modeling, and supply chains. This tutorial provides an introduction to tools and resources for prospective modelers, and illustrates ABM flexibility with a basic war-gaming example.

Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms


Alcohol misuse is a complex systemic problem. The aim of this study was to explore the feasibility of using a transparent and participatory agent-based modelling approach to develop a robust decision support tool to test alcohol policy scenarios before they are implemented in the real world. Methods A consortium of Australia’s leading alcohol experts was engaged to collaboratively develop an agent-based model of alcohol consumption behavior and related harms. As a case study, four policy scenarios were examined.