6148667
Supply chain management tradeoffs analysis
Supply chain management involves understanding complex interactions between many factors and using the understanding to achieve balance between conflicting objectives. 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 simulation based study for analyzing the tradeoffs among service 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.
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.
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.
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.
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.
45469476
Predictive learning models for concept drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the concept shifts may not have to satisfy any further requirements and may occur infinitely often. Within this work is studied to what extent it is still possible to predict or learn values for a data sequence produced by drifting concepts. Various ways to measure the quality of such predictions, including martingale betting strategies and density and frequency of correctness, are introduced and compared with one another. For each of these measures of prediction quality, for some interesting concrete classes, usefully established are (nearly) optimal bounds on permanence for attaining learnability. The concrete classes, from which the drifting concepts are selected, include regular languages accepted by finite automata of bounded size, polynomials of bounded degree, and exponentially growing sequences defined by recurrence relations of bounded size. Some important, restricted cases of drifts are also studied, e.g., the case where the intervals of permanence are computable. In the case where the concepts shift only among finitely many possibilities from certain infinite, arguably practical classes, the learning algorithms can be considerably improved.
16991232
Learning from Multiple Sources of Inaccurate Data
Most theoretical studies of inductive inference model a situation involving a machine M learning its environment E on following lines. M, placed in E, receives data about E, and simultaneously conjectures a sequence of hypotheses. M is said to learn E just in case the sequence of hypotheses conjectured by M stabilizes to a final hypothesis which correctly represents E.
31485886
Machine Induction Without Revolutionary Paradigm Shifts
This paper provides a beginning study of the effects on inductive inference of paradigm shifts whose absence is approximately modeled by various formal approaches to forbidding large changes in the size of programs conjectured.
15346743
Some Independence Results for Control Structures in Complete Numberings
Acceptable programming systems have many nice properties like s-m-n-Theorem, Composition and Kleene Recursion Theorem. Those properties are sometimes called control structures, to emphasize that they yield tools to implement programs in programming systems. It has been studied. among others by Riccardi and Royer. how these control structures influence or even characterize the notion of acceptable programming system. The following is an investigation, how these control structures behave in the more general setting of complete numberings as defined by Mal'cev and Ergov. ?
33820262
Learning with Refutation
Abstract In their pioneering work, Mukouchi and Arikawa modeled a learning situation in which the learner is expected to refute texts which are not representative of L , the class of languages being identified. Lange and Watson extended this model to consider justified refutation in which the learner is expected to refute texts only if it contains a finite sample unrepresentative of the class L . Both the above studies were in the context of indexed families of recursive languages. We extend this study in two directions. First, we consider general classes of recursively enumerable languages. Second, we allow the machine to either identify or refute the unrepresentative texts (respectively, texts containing finite unrepresentative samples). We observe some surprising differences between our results and the results obtained for learning indexed families by Lange and Watson.