Edward Lin

Bio: Edward Lin is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Process design & Automated planning and scheduling. The author has an hindex of 10, co-authored 16 publications receiving 1071 citations.

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Abstract: Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.

Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies

Abstract: This article presents initial results in the search for analytical models that can predict the performance of one-machine systems under periodic and event-driven rescheduling strategies in an envir...

Supply Chain Simulation Modeling Using the Supply Chain Operations Reference Model

Abstract: Simulation is a very useful tool for predicting supply chain performance. Because there are no standard simulation elements that represent accurately the activities in a supply chain, there exist a variety of approaches for developing supply chain simulation models. To improve this situation, this paper describes a novel supply chain simulation framework that follows the Supply Chain Operations Reference (SCOR) model. This framework has been used for building powerful simulation models that integrate discrete event simulation and spreadsheets. The simulation models are hierarchical and use submodels that capture activities specific to supply chains. The SCOR framework provides a basis for defining the level of detail in a way as to include as many features as possible, while not making them industry specific. This approach enables the reuse of submodels, which reduces development time. The paper describes the implementation of the simulation models and how the submodels interact during execution.Copyright © 2003 by ASME

Processing flows of information: From data stream to complex event processing

Abstract: A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications which process raw data coming from sensor networks to identify critical situations; or applications performing online analysis of stock prices to identify trends and forecast future values.Traditional DBMSs, which need to store and index data before processing it, can hardly fulfill the requirements of timeliness coming from such domains. Accordingly, during the last decade, different research communities developed a number of tools, which we collectively call Information flow processing (IFP) systems, to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this article, we survey these systems to help researchers, who often come from different backgrounds, in understanding how the various approaches they adopt may complement each other.In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the systems and mechanisms proposed so far.

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Abstract: Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.

A survey of dynamic scheduling in manufacturing systems

Abstract: In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.