14572238
A simulation architecture for manufacturing interoperability testing
Manufacturing systems are often costly to develop and operate. Simulation technology has been demonstrated to be an effective tool for improving the efficiency of manufacturing system design, operation, and maintenance. But manufacturing simulations are usually developed to address a narrow set of industrial issues, e.g., the purchase of new equipment or the modification of a manufacturing process. Once the analysis is complete a particular simulation model may not be used again. If simulations could be made more modular and easily integrated, they could have tremendous value as tools for manufacturing interoperability testing. This paper presents a modular reference architecture to facilitate the integration of manufacturing simulation systems with other support and testing applications. Opportunities for testing are also discussed that will be enabled by the implementation of the architecture.
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.
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.
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.
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.
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.
34788653
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+?, where ? 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 MadditionallyBcn-identify C. Ex-identification 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 reliablyEx-identified, there is a general purpose machine which additionallyEx-identifies C!
70017
Learning Languages from Positive Data and Negative Counterexamples
In this paper we introduce a paradigm for learning in the limit of potentially infinite languages from all positive data and negative counterexamples provided in response to the conjectures made by the learner. Several variants of this paradigm are considered that reflect different conditions/constraints on the type and size of negative counterexamples and on the time for obtaining them. In particular, we consider the models where 1) a learner gets the least negative counterexample; 2) the size of a negative counterexample must be bounded by the size of the positive data seen so far; 3) a counterexample may be delayed. Learning power, limitations of these models, relationships between them, as well as their relationships with classical paradigms for learning languages in the limit (without negative counterexamples) are explored. Several surprising results are obtained. In particular, for Gold’s model of learning requiring a learner to syntactically stabilize on correct conjectures, learners getting negative counterexamples immediately turn out to be as powerful as the ones that do not get them for indefinitely (but finitely) long time (or are only told that their latest conjecture is not a subset of the target language, without any specific negative counterexample). Another result shows that for behaviourally correct learning (where semantic convergence is required from a learner) with negative counterexamples, a learner making just one error in almost all its conjectures has the “ultimate power”: it can learn the class of all recursively enumerable languages. Yet another result demonstrates that sometimes positive data and negative counterexamples provided by a teacher are not enough to compensate for full positive and negative data.
67075
Strong separation of learning classes
Abstract Suppose LC 1 and LC 2 are two machine learning classes each based on a criterion of success. Suppose, for every machine which learns a class of functions according to the LC 2 criterion of success, there is a machine which learns this class according to the LC 2 criterion. In the case where the converse does not hold LC, is said to be separated from LC 2. It is shown that for many such separated learning classes from the literature a much stronger separation holds: (∀𝒞∈LC 1) (∃𝒞' ∈LC 2 - LC 1(( [' ⊃𝒞] It is also shown that there is a pair of separated learning classes from the literature for which the stronger separation above does not hold. A philosophical heuristic toward the design of artificially intelligent learning programs is presented with each strong separation result.
5522355
Program Size Restrictions in Computational Learning
Abstract A model for a subject S learning its environment E could be described thus: S , placed in E , receives data about E , and simultaneously conjectures a sequence of hypotheses. S is said to learn E just in case the sequence of hypotheses conjectured by S stabilizes to a final hypothesis which correctly represents E . Computational learning theory provides a framework for studying problems of this nature when the subject is a machine. A natural abstraction for the notion of hypothesis is a computer program. The present paper, in the above framework of learning, presents arguments for the final hypothesis to be succinct , and introduces a plethora of formulations of such succinctness. A revelation of this study is that some of the “natural” notions of succinctness may be uninteresting because learning capability of machines under these seemingly natural constraints is dependent on the choice of programming system used to interpret hypotheses.