Enhancing Understanding of Discrete Event Simulation Models Through Analysis

Simulation is used increasingly throughout research, development, and planning for many purposes. While model output is often the primary interest, insights gained through the simulation process can also be valuable. Insights can come from building and validating the model as well as analyzing its behaviors and output; however, much that could be informative may not be easily discernible through these existing traditional approaches, particularly as models continue to increase in complexity. This research extends current work in model analysis and program understanding to assist modelers in obtaining more insight into their models and the systems they represent. A primary technique for model understanding is analysis of model output; this research has developed new, complementary techniques. A significant point of this research is that the created tools do not necessitate that a modeler or model user be able to encode the model or have any coding expertise. Some of the information presented here could be produced by existing software development tools; however, most modelers today do not have the technical background to use such tools or to make use of the reports they can produce. Additionally, one of the significant details of this research is the focus on model aspects rather than simulation aspects: the tools developed here detail the model embedded in implementation code, not the code necessary for implementation. Source code tends to involve many issues unrelated to the model itself, such as data collection, animation, and tricks for efficient run-time behavior. Even when the modeler is an expert programmer, this other code often can obscure features of the model as implemented. Results indicate these tools and techniques, when applied to even modest simulation models, can reveal aspects of those models not readily apparent to the builders or users of the models. This work provides both model builders and model users with additional techniques that can give them improved understanding of their models

ANWB automates and improves repair men dispatching

ANWB, the Dutch automobile association, provides assistance, car repair andreplacement services to its nearly 4 million members. ANWB services around 1.3 millionrequests per year in The Netherlands. Historically, the operational planning process ofassigning requests to service men was regionally organized, and human planners solvedthe sometimes large and hectic planning situations in real time. At a national level, some50 planners were required to do the job, and the quality of the planning and operationswere largely unknown. In a large business process reengineering project, ANWBredesigned the planning processes, leveraging state of the art IT and operations researchtechniques. As a result, the 24/7 planning processes are smoothened, can be executed byas few as 14 planners who work at a national level, and the operational planning andperformance have improved. As new competitors entered the market, ANWB has beenable to sustain its extraordinary high customer ratings and market share, while adaptingits proposition to the competitive prices dictated by the market.Economics (Jel: A)

The dynamic traveling repairman problem

Includes bibliographical references (p. 30-32).Partially supported by the National Science Foundation. ECS-8717970Dimitris Bertsimas, Garrett van Ryzin

A stochastic and dynamic vehicle routing problem in the Euclidean plane

"February 1990."Includes bibliographical references (p. 29-31).Research supported by the National Science Foundation. DDM-9014751 Research supported by a grant from Draper Laboratory.Dimitris J. Bertsimas, Garrett van Ryzin

A stochastic and dynamic vehicle routing problem in the Euclidean plane

"February 1990."Includes bibliographical references (p. 29-31).Research supported by the National Science Foundation. DDM-9014751 Research supported by a grant from Draper Laboratory.Dimitris J. Bertsimas, Garrett van Ryzin

Stochastic Dynamic Vehicle Routing in the Euclidean Plane: The Multiple-Server, Capacitated Vehicle Case

In a previous paper [12], we introduced a new model for stochastic and dynamic vehicle routing called the dynamic traveling repairman problem (DTRP), in which a vehicle traveling at constant velocity in a Euclidean region must service demands whose time of arrival, location and on-site service are stochastic. The objective is to find a policy to service demands over an infinite horizon that minimizes the expected system time (wait plus service) of the demands. We showed that the stability condition did not depend on the geometry of the service region (i.e. size, shape, etc.). In addition, we established bounds on the optimal system time and proposed an optimal policy in light traffic and several policies that have system times within a constant factor of the lower bounds in heavy traffic. We showed that the leading behavior of the optimal system time had a particularly simple form which increases much more rapidly with traffic intensity than the system time in traditional queues (e.g. M/G/1). In this paper, we extend these results in several directions. First, we propose new bounds and policies for the problem of m identical vehicles with unlimited capacity and show that in heavy traffic the system time is reduced by a factor of 1/m2 over the single server case. Policies based on dividing the service region into m equal subregion

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS