34370031
Simulation for emergency response: a framework for modeling and simulation for emergency response
A number of modeling and simulation tools have been developed and more are being developed for emergency response applications. The available simulation tools are meant mostly for standalone use. Addressing an emergency incident requires addressing multiple interdependent aspects of the situation. The simulation tools addressing different aspects of an emergency situation need to be integrated to provide the whole picture to planners, trainers, and responders. A framework is required to ensure that modeling and simulation tools can be systematically integrated together to address the overall response. This paper proposes a framework for integration of modeling, simulation, and visualization tools for emergency response. The development and implementation of the proposed framework will significantly improve the nation's capability in the emergency response area.
18211780
Supply chain opportunities: panel session: opportunities for simulation in supply chain management
It has become a matter of survival that many companies improve their supply chain efficiency. This presents an opportunity for simulation. However, there are many challenges that must be overcome for simulation to be a contributor to play an effective role. Four contributors discuss the opportunities that they see for simulation to play a meaningful role in the area of supply chain management.
32884753
Expert Simulation For On-line Scheduling
In recent years, the automotive industry has realized the importance of speed of new products to market and has mounted efforts for improving it. The Expert System Scheduler (ESS) facilitates these efforts by enabling manufacturing plants to generate viable schedules under increasing constraints and demands for flexibility. The scheduler takes advantage of the Computer Integrated Manufacturing (CIM) environment by utilizing the real-time information from the factory for responsive scheduling. The Expert System Scheduler uses heuristics developed by an experiences factory scheduler. It uses simulation concepts and these heuristics to generate schedules. Forward and "backward" simulation are used at different stages of the schedule generation process. The system is used to control parts flow on the factory floor at one automated facility. This highly automated facility is a testbed for implementation of CIM concepts. The scheduler runs on a Texas Instruments (TI) Explorer II computer using software developed inhouse utilizing IntelliCorp's Knowledge Engineering Environment (KEE) shell and the LISP language. The scheduling computer is networked to the factory control computer, which actually controls the plant floor. The TI Explorer II acquires current plant floor information from the factory control system, generates a new schedule and sends it back within a short time. The configuration allows fast response to changes in requirements and plant floor conditions.
18971661
Development of a high-level supply chain simulation model
This paper describes an effort that involved development of a simulation model for evaluating the business processes and inventory control parameters of a logistics and distribution supply chain. A generic simulation tool, rather than a supply chain simulator, was developed for meeting customized needs of the effort. The paper describes, the approaches used to model at the selected level of abstraction, the development of interfaces for data and experimentation and the development and delivery of animation for communicating the approach and results to the client.
201885623
Manufacturing supply chain applications: modeling computer assembly operations for supply chain integration
Factory operations have been modeled for years to understand the relationship between the different design and policy factors and the performance measures of interest. The increasing awareness of the need to manage factories as a link in the supply chain places a corresponding requirement for an enhanced approach for factory modeling. This paper describes the modeling of a computer assembly factory for supply chain integration by including aspects of inbound and outbound logistics and relevant business processes. Lessons are drawn based on the experience.
6477912
Stress testing a supply chain using simulation
Simulation provides the capability to evaluate performance of a system operating under current or proposed configurations, policies and procedures. It is very applicable to evaluation of strategic and operational level plans for supply chains. It is especially useful for exploring the viability of a supply chain before beginning production. This paper describes the use of simulation for determining the readiness of a supply chain designed by a small company for providing sub-systems to a defense contractor. The supply chain had to be stress tested at surge and mobilization volume levels to meet the requirements. Simulation provided the ideal methodology to identify and demonstrate the behavior of the supply chain under stress and evaluate strategies to meet the defined volumes. It was used to prove the readiness of the supply chain reducing perceived risks in using the virtual operation.
6467955
Low degree metabolites explain essential reactions and enhance modularity in biological networks
BackgroundRecently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints.ResultsIn the metabolic networks of Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus, we identified metabolites with a low degree of connectivity, particularly those that are produced and/or consumed in just a single reaction. Using flux balance analysis (FBA) we also determined reactions essential for growth in these metabolic networks. We find that most reactions identified as essential in these networks turn out to be those involving the production or consumption of low degree metabolites. Applying graph theoretic methods to these metabolic networks, we identified connected clusters of these low degree metabolites. The genes involved in several operons in E. coli are correctly predicted as those of enzymes catalyzing the reactions of these clusters. Furthermore, we find that larger sized clusters are over-represented in the real network and are analogous to a 'network motif. Using FBA for the above mentioned three organisms we independently identified clusters of reactions whose fluxes are perfectly correlated. We find that the composition of the latter 'functional clusters' is also largely explained in terms of clusters of low degree metabolites in each of these organisms.ConclusionOur findings mean that most metabolic reactions that are essential can be tagged by one or more low degree metabolites. Those reactions are essential because they are the only ways of producing or consuming their respective tagged metabolites. Furthermore, reactions whose fluxes are strongly correlated can be thought of as 'glued together' by these low degree metabolites. The methods developed here could be used in predicting essential reactions and metabolic modules in other organisms from the list of metabolic reactions.
30237295
Crowd Control: Coordinating Processes in Parallel
Tutorials • Bridget Rosewell and Paul Ormerod " Agent Based Models and Social Simulation in the Policy Process " [Room: Aula Magna] This tutorial will focus on practical examples of agent based models which are actually being used as part of the decision making process of policy makers. The tutorial will also address key issues in integrating ABMs and social simulation into the policy making process. The tutorial will introduce the Multi-Agent Simulation Suite (MASS), a novel, integrated simulation environment for agent-based social simulation. MASS was designed with scientific modelers and not professional programmers in mind. Therefore, ease of use and prudent scientific practices were at the focus of the suite's design. The tutorial consists in a brief introductory part providing a generalized definition of the basic elements composing a simulation model. Then, it will be shown how LSD allows users to transparently adopt the theoretical definition, automatically generating all the technical code necessary for developing, running and analysing a simulation model. A series of examples will show the advantages of the definition proposed and the most relevant features of LSD. The tutorial will introduce the Multi-Agent Simulation Suite (MASS), a novel, integrated simulation environment for agent-based social simulation. MASS was designed with scientific modelers and not professional programmers in mind. Therefore, ease of use and prudent scientific practices were at the focus of the suite's design.
901128
Language Learning from Texts: Degrees of Intrinsic Complexity and Their Characterizations
Abstract This paper deals with two problems: (1) what makes languages learnable in the limit by natural strategies of varying hardness, and (2) what makes classes of languages the hardest ones to learn. To quantify hardness of learning, we use intrinsic complexity based on reductions between learning problems. Two types of reductions are considered: weak reductions mapping texts (representations of languages) to texts and strong reductions mapping languages to languages. For both types of reductions, characterizations of complete (hardest) classes in terms of their algorithmic and topological potentials have been obtained. To characterize the strong complete degree, we discovered a new and natural complete class capable of “coding” any learning problem using density of the set of rational numbers. We have also discovered and characterized rich hierarchies of degrees of complexity based on “core” natural learning problems. The classes in these hierarchies contain “multidimensional” languages, where the information learned from one dimension aids in learning other dimensions. In one formalization of this idea, the grammars learned from the dimensions 1, 2, …,  k specify the “subspace” for the dimension k +1, while the learning strategy for every dimension is predefined. In our other formalization, a “pattern” learned from the dimension k specifies the learning strategy for the dimension k +1. A number of open problems are discussed.
5422343
Costs of General Purpose Learning
Leo Harrington surprisingly constructed a machine which can learn any computable function f according to the following criterion (called Bc*-identification). His machine, on the successive graph points of f, outputs a corresponding infinite sequence of programs p0, p1, p2, ..., and, for some i, the programs pi, pi + 1, pi+2, ... each compute a variant of f which differs from f at only finitely many argument places. A machine with this property is called general purpose. The sequence pi, pi+1, pi+2, ... is called a final sequence. 
 
For Harrington's general purpose machine, for distinct m and n, the finitely many argument places where pi+m fails to compute f can be very different from the finitely many argument places where pi+n fails to compute f. One would hope though, that if Harrington's machine, or an improvement thereof, inferred the program pi+m based on the data points f(0), f(1), ..., f(k), then pi+m would make very few mistakes computing f at the "near future" arguments k + 1; k + 2, ..., k + l, where l is reasonably large. Ideally, pi+m's finitely many mistakes or anomalies would (mostly) occur at arguments x ≫ k, i.e., ideally, its anomalies would be well placed beyond near future arguments. In the present paper, for general purpose learning machines, it is analyzed just how well or badly placed these anomalies may be with respect to near future arguments and what are the various tradeoffs. 
 
In particular, there is good news and bad. Bad news is that, for any learning machine M (including general purpose M), for all m, there exist infinitely many computable functions f such that, infinitely often M incorrectly predicts f's next m near future values. Good news is that, for a suitably clever general purpose learning machine M, for each computable f, for M on f, the density of any such associated bad prediction intervals of size m is vanishingly small. 
 
Considered too is the possibility of providing a general purpose learner which additionally learns some interesting classes with respect to much stricter criteria than Bc*-identification. Again there is good news and bad. The criterion of finite identification requires for success that a learner M on a function f output exactly one program which correctly computes f. Bcn-identification is just like Bc*-identification above except that the number of anomalies in each program of a final sequence is ≤ n. Bad news is that there is a finitely identifiable class of computable functions C such that for no general purpose learner M and for no n, does M additionally Bcn-identify C. Ex-dentification by M on f requires that M on f converges, after a few output programs, to a single final program which computes f. A reliable learner (by definition) never deceives by false convergence; more precisely: whenever it converges to a final program on a function f, it must Ex-identify f. Good news is that, for any class C that can be reliably Ex-identified, there is a general purpose machine which additionally Ex-identifies C!
15285205
Program Synthesis in the Presence of Infinite Number of Inaccuracies
Most studies modeling inaccurate data in Gold style learning consider cases in which the number of inaccuracies is finite. The present paper argues that this approach is not reasonable for modeling inaccuracies in concepts that are infinite in nature (for example, graphs of computable functions).