WSC 2004

WSC 2004 Final Abstracts

Logistics, Transportation, and Distribution Track

Sunday 1:00:00 PM 2:30:00 PM
Simulation of Advanced Aviation Concepts

Chair: Frederick Wieland (Aegis Technology Group)

Modeling Time and Space Metering of Flights in the National Airspace System
Paul T.R. Wang, Craig R Wanke, and Frederick P. Wieland (The MITRE Corporation)

Metering flights at key points such as sector crossings is an important operational procedure in mitigating National Airspace System (NAS) traffic congestion due to high demand or changing weather conditions. The authors combine a mathematical model for minutes-in-trail or miles-in-trail (MIT) metering with discrete event simulation in a newly developed tool that can be used by analysts to examine or predict existing or developing bottlenecks within NAS. We define a penalty function recursively in terms of MIT delays between leading and following flights. With discrete event simulations, it is possible to examine the anticipated MIT delays for all the flights scheduled to arrive at any crossing point. Impacts of flight cancellations, route changes, and additional enroute delays as results of airport or sector congestion can all be evaluated during each simulation by updating the scheduled flight crossing times and the expected MIT delay penalties for all the trailing flights.

Traffic Flow Management Modeling and Operational Complexity
Brendan Patrick Hogan and Leonard A. Wojcik (The MITRE Corporation)

Traffic Flow Management (TFM) actions are commonly used to mitigate capacity/demand imbalances within the National Airspace System (NAS). Modeling TFM events has proven challenging in the past, partly because of weather forecast uncertainty, and partly because of the complexity and unpredictability associated with highly-interrelated traffic patterns and distributed decision-making in the NAS. In this paper, we present results of a simulation of a NAS TFM event in which weather effects are relatively small. This facilitates interpretation of the similarities and differences between simulation results and the actual event in terms of NAS operations and decision making, with relatively small weather-related complications. We conclude that TFM modeling shows promise as a tool to aid post-event TFM analysis, but the complex operational factors impose limits on the predictability of outcomes in TFM events. A CAASD-developed fast-time network simulation of the NAS was used for this analysis.

Estimating Efficacy of Progressive Planning for Air Traffic Flow Management
Lynne Fellman, James S. DeArmon, and Kelly A. Connolly (The MITRE Corporation)

Air traffic flow management (TFM) is a set of processes and procedures which seek to balance the demand for airspace resources with the capacity of these resources. Examples of resources are airports, sectors (airspace volumes managed by air traffic controllers), and fixes (imaginary points in space used for navigation). The Federal Aviation Administration (FAA) is continually looking for ways to provide new tools and techniques for TFM personnel. As the TFM function improves, flight efficiency improves, and the experience of the flying public is likewise improved. In this paper, we describe a simulation modeling exercise to assess the benefit, if any, of a proposed new feature of TFM called Progressive Planning (P2). P2 allows the flow manager to model the impact of multiple concurrent flow management actions. It is envisioned that the improved modeling leads to better decision-making, which leads to greater flight efficiency.

Sunday 3:00:00 PM 4:30:00 PM
Advanced Simulation Methods for Transportation I

Chair: Lisa Schaefer (MITRE/CAASD)

In 2001, Detroit Metropolitan Wayne County Airport (DTW) opened a new runway parallel to three existing runways. While this increases DTW’s runway capacity, the airport is served by an airspace (routes, procedures, and controller assignments) that was designed only for a three-runway airport. To increase the airport’s effective capac-ity, the Detroit-area Terminal Radar Approach Control facility (D21 TRACON) and nearby Air Route Traffic Control Centers (ARTCC) are redesigning their airspace. This paper describes the simulation modeling effort to estimate delay and cost benefits of the ARTCC redesign for arrival traffic. The model, written in the SLX simulation lan-guage, represents miles-in-trail (MIT) restrictions, as well as air traffic controllers’ ability to direct flights to different paths dynamically, based on predicted demand down-stream. The redesign work is part of the Federal Aviation Administration’s Midwest Airspace Capacity Enhancement (MACE) project.

Stream Option Manager
William P. Niedringhaus (The Mitre Corporation) and Michael J. White and Patrick L. Jones (The MITRE Corporation)

Stream Option Manager (SOM) is a set of mathematical tools developed at The MITRE Corporation’s Center for Advanced Aviation System Development (CAASD). While still under development, it holds the potential to provide modelers an automated capability for separation, sequencing, spacing and proper exit strategy of aircraft in en route airspace. SOM addresses these services in a single, integrated linear programming solution. SOM com-putes modified flight paths meeting all constraints while minimally deviating from preferred trajectories. A proto-type implementation of SOM is being evaluated in several applications. These include (a) an automated generator of sector complexity metrics e.g., by measuring the number and magnitude of separation actions needed to resolve a given traffic situation, (b) a means of performing system-wide analyses of revised conflict resolution requirements, e.g., revised separation standards, and (c) an automated controller-surrogate for simulation studies. The SOM metrics will be described, and example applications will be presented

Applying Statistical Control Techniques to Air Traffic Simulations
Kirk C. Benson, David Goldsman, and Amy R. Pritchett (Georgia Institute of Technology)

While the literature contains several adaptive sampling techniques for statistical comparison of competing simulated system configurations and for embedded statistical computations during simulation run-time, these techniques are often difficult to apply to air traffic simulations because of the complexity of air traffic scenarios and because of the variety of model and data types needed to fully describe air traffic. Adaptive sampling techniques can be beneficial to the study of air traffic; for example, adaptive techniques can use ranking and selection methods to compare the relative worth of the competing configurations and calculate the number of observations required for rigorous statistical comparison, often dramatically reducing the run-time duration of simulations. In this paper, we will describe the implementation of such procedures in the Reconfigurable Flight Simulator for air traffic simulations. We also discuss implications for the coordination of simulation, analysis, and design activities.

Simulating Airspace Redesign for Arrivals to Detroit-Wayne County Airport (DTW)
Justin Boesel and David Bodoh (The MITRE Corporation)

Monday 10:30:00 AM 12:00:00 PM
Advanced Simulation Methods for Transportation II

Chair: Bill Niedringhaus (MITRE/CAASD)

This paper describes an algorithm for approximating missing data in air traffic routes thereby allowing the lengths of different routes to be compared for our simulation analyses. We were given air traffic routes that had origin, destination, and complete route information inside of the problem design area. Analyst judgment was necessary for completing the routes outside of the design area. We automated the analyst decision processes so that comparisons could be made across scenarios. This process was required for thousands of routes. We analyze design performance measures and show that the resulting distances among the redesigned routes differ from baseline route lengths.

The Use of Simulation to Support Major Transportation Planning Decisions
Beth Carpenter Kulick (Automation Associates, Inc.)

When major transportation infrastructures such as freight corridors or port systems are being planned, there are typi-cally multiple phases of preliminary engineering required. During these phases, there are design decisions made that have impacts on investment required, level of service pro-vided, and the environment. The basic question that is typically asked during these phases is “What level of infra-structure is really needed to support the expected demands upon the system?” Simulation provides a framework to quantify the level of service provided when an infrastruc-ture design is imposed with projected demands. There are numerous challenges associated with constructing a simu-lation model of the magnitude needed to support planning initiatives. This paper describes a simulation modeling ap-proach that integrates needed planning flexibility with suf-ficient fidelity to understand infrastructure performance.

Airport Terminal-Approach Safety and Capacity Analysis Using an Agent-Based Model
Yue Xie, John Shortle, and George Donohue (George Mason University)

The consistent growth of air traffic demand is causing the operational volumes at hub airports to approach their maximum capacities. With this growth, delays are increasing, and safety is becoming a more crucial problem. The terminal approaching and landing phases are especially important since the airspace is more crowded and operational procedures are more complicated compared with the en route phase. We have developed an agent-based stochastic simulation model which is useful to analyze the relationship among airport arrival capacity, delay, and safety. We first present a simplified queue model to demonstrate key ideas. Then, we give a detailed agent-based model that is calibrated to Hartsfield Atlanta International Airport. We use the model to evaluate several operational scenarios and examine the trade-offs between system capacity and safety.

Automation of Human Decision Processes for Route Completion for Airspace Design Analysis
Lisa Ann Schaefer (American University)

Monday 1:30:00 PM 3:00:00 PM
Supply Chain Simulation

Chair: Silvanus Enns (University of Calgary)

Supply Chain Management Tradeoffs Analysis
Sanjay Jain (Virginia Polytechnic Institute and State University)

Supply chain management involves understanding com-plex interactions between many factors and using the un-derstanding to achieve balance between conflicting objec-tives. Simulation is a very useful technique to evaluate the impact of changes in factors such as inventory control and business process parameters. This paper describes a simu-lation based study for analyzing the tradeoffs among ser-vice level, inventory and lead times for a large logistics supply chain. The study highlights the use of simulation in understanding seemingly non-intuitive results and guiding the effort for performance improvement.

Coordination in a Supply Chain for Bulk Chemicals
Henk de Swaan Arons, Eelco van Asperen, Rommert Dekker, and Mark Polman (Erasmus University Rotterdam)

A chemical plant in The Netherlands uses large annual supplies of a bulk chemical. A number of suppliers deliver their parcels from overseas by short sea vessel to a trans-shipment point where they are stored using a tank farm. Transportation from the transshipment point to the plant takes place by barge. Coordination of the schedules of vessels and barge provides the opportunity for board to board loading. Board to board loading provides clear benefits for the plant’s operator, as it requires less handling and intermediate storage at the transshipment point. We demonstrate this by experiments conducted with a simulation model. The results are confirmed by analytical means.

Easy-SC: A Supply Chain Simulation Tool
Juqi Liu, Wei Wang, Yueting Chai, and Yi Liu (Tsinghua University)

To assist the decision makers, we develop a new supply chain simulation software: Easy-SC, a Java-based tool that simplifies the supply chain simulation. In its current state of development, Easy-SC is a modeling tool for assessing the pros and cons of new facility locations, resource allocations and different combinations of policies. It can be used in the modeling of small projects such as single inventory units to large-scale projects such as world wide supply chains. This paper introduces Easy-SC by an examination of its current software module architecture, modeling elements, basic features and simulation processes.

Monday 3:30:00 PM 5:00:00 PM
Advanced Methods for Transportation III

Chair: Sanjay Jain (Virginia Tech)

This paper introduces and summarizes a comprehensive systems approach guiding an ongoing project addressing these significant challenges confronting logistics transformation. Currently sponsored by the Army Aviation and Missile Command (AMCOM), this project involves several supporting organizations both within and external to the Army and DOD. Although initially focused on aviation-specific Class IX (spare parts and components) as a test bed, the goal is to develop a prototype that will provide the foundational ``analytical architecture'' to support, guide and accelerate Army Logistics Transformation. Conditions which motivate this research and analysis include (1) the changed nature of our geopolitical landscape resulting in the Army's transition to a ``capabilities-based'' force, (2) the opportunity to consider, adapt and extend, where appropriate, integrating ``supply chain'' design, management and analysis concepts that have been driven by increasing competition in the corporate world, and (3) the enabling potential of information technology.

Analyzing Losses From Hazard Exposure: A Conservative Probabilistic Estimate Using Supply Chain Risk Simulation
Léa Amandine Deleris (Stanford University), Debra Elkins (General Motors R&D Center) and M. Elisabeth Paté-Cornell (Stanford University)

We present a supply chain risk analysis that is based on a Monte Carlo simulation of a Generalized Semi-Markov Process (G.S.M.P.) model. Specifically, we seek to estimate the probability distribution of supply chain losses caused by disruptions. This distribution is computed conditional on conservative hypotheses which are the following: (1) no additional risk reduction measures are implemented beyond those already in place, (2) all the products whose production has been canceled are counted as losses at their market value. The simulation thus yields conditional probabilities of loss levels that firms may reasonably use in the evaluation of business interruption costs and insurance coverage limits. The model also enables the comparison of supply chain designs based on their resilience in recovering from risk events. The approach is novel for it connects stochastic modeling of risks from an insurance perspective with supply chain network design.

Integration of Simulation and Geographic Information Systems: Modeling Traffic Flow on Inland Waterways
William E. Biles and Daniel Sasso (University of Louisville) and Jerry K. Bilbrey (Francis Marion University)

This paper describes the integration of Geographic Information Systems (GIS) with simulation modeling of traffic flow on inland waterways. Two separate modeling efforts are described: (a) GIS/AutoMod modeling of barge traffic on the Ohio River, and (b) GIS/Arena modeling of the transit of ocean-going vessels through the Panama Canal. These modeling efforts demonstrate the benefits that accrue both to modeling realism and to the initialization process with discrete-event models of traffic flow on these waterways.

Enabling a Transforming Army at War: Analysis to Improve Logistics Network Efficiency and Effectiveness
Greg H Parlier (University of Alabama in Huntsville)

Tuesday 8:30:00 AM 10:00:00 AM
Supply Chain Simulation II

Chair: Brendan Hogan (MITRE/CAASD)

Simulation is well suited to the development and analysis of supply chain models, since the problems of interest tend to be complex and encompass uncertainty. However, there are typically multiple performance objectives that tend to conflict. A major problem in supply chain studies is that assumptions need to be made about the performance trade-offs involved. Therefore, the conclusions may not be gen-eral. In this paper we develop an approach that allows both delivery performance and inventory levels to be considered over a range of tradeoffs. By developing tradeoff curves and analyzing the area under each we are able to reach conclusions that are more general and can be shown to be statistically valid.

Hongwei Ding, Lyès Benyoucef, and Xiaolan Xie (INRIA (The French National Institute for Research in Computer Science and Control)) and Carl Hans and Jens Schumacher (BIBA (Bremen Institute of Industrial Technology and Applied Work Science))

In today's rapidly changing business environment, an advanced decision support system offers decision-makers flexibility for problem solving and credibility in resulting solutions. We present in this paper a newly developed tool "ONE" for the assessment, design and improvement of supply chain networks using simulation and optimization. The tool provides user-friendly modeling interfaces and a comprehensive simulation engine. Most importantly, an innovative simulation-based optimization module is integrated, which enables the optimization of supply chain structures, quantitative and qualitative operational parameters with multi-criteria considerations. The design principles, architecture and main functional modules of tool ONE are described. A case study is presented to demonstrate how the tool can be used to solve problems under real-life conditions.

Simulation-Based Optimization for Material Dispatching in a Retailer Network
Ganesh Subramaniam and Abhijit Gosavi (The State University of New York)

This paper presents preliminary work done on simulation-based optimization of a stochastic material-dispatching system in a retailer network. The problem we consider is one of determining the optimal number of trucks and quantities to be dispatched in such a system. Theoretical solution models for versions of this problem can be found in the literature. Unlike most theoretical models, we can accommodate many real-life considerations, such as arbitrary distributions of the governing random variables, and all important cost elements, such as inventory-holding costs, stock-out costs, and transportation costs. We have used two techniques, namely, neuro-response surfaces and simulated annealing, for optimizing our system. We have also used a problem-specific heuristic, known as the mean demand heuristic, to provide us with a good starting point for simulated annealing and a benchmark for our other methods. Some computational results are also provided.

Evaluating the Performance of Supply Chain Simulations with Tradeoffs Between Multiple Obljectives
Pattita Suwanruji and Silvanus T. Enns (University of Calgary)

Tuesday 10:30:00 AM 12:00:00 PM
Advanced Methods for Transportation IV

Chair: Justin Boesel (Aptima)

This paper presents a research project being developed at the Industrial and Systems Engineering Graduate Pro-gram at the Catholic University of Paraná (Brazil). The objective is to develop a system to aid professionals from management and logistics areas to evaluate the perform-ance of supply chains through computer simulation. Among the several possibilities for analysis, simulation can allow one to perform detailed studies on the bullwhip ef-fect in supply chains, caused by the demand variation from the point-of-sale to the suppliers. Two performance meas-ures are of particular interest: average inventory level and service level, both for each stage at and for the whole sup-ply chain. The structure considered in this project is the traditional supply chain composed by suppliers, manufac-tures, distributors (or wholesalers), retailers and customers. A first version of the proposed Arena simulation models is under development and is presented in this paper.

Optimization of Traffic Signal Light Timing Using Simulation
Kasun N. Hewage and Janaka Yasantha Ruwanpura (University of Calgary)

Traffic congestion is one of the worst problems in many countries. Traffic congestion wastes a huge portion of the national income for fuel and traffic-related environmental and socioeconomic problems. Computer simulation is a powerful tool for analyzing complex and dynamic scenarios. It provides an appealing approach to analyze repetitive processes. Simulation helps decision makers identify different possible options by analyzing enormous amounts of data. Hence, computer simulation can be used effectively to analyze traffic flow patterns and signal light timing. This paper discusses a special-purpose simulation (SPS) tool for optimize traffic signal light timing. The simulation model is capable of optimizing signal light timing at a single junction as well as an actual road network with multiple junctions. It also provides signal light timing for certain time periods according to traffic demand. Traffic engineers at the University of Moratuwa, Sri Lanka are testing the developed tool for actual applications.

Ideas for Modeling and Simulation of Supply Chains with Arena
Guilherme Ernani Vieira (Pontifícia Universidade Católica do Paraná (PUCPR))