Albert T. Jones

A Proposed Hierarchical Control Model for Automated Manufacturing Systems

Abstract The Automated Manufacturing Research Facility is being constructed at the National Bureau of Standards. This small, integrated, flexible manufacturing system will serve as a research test bed to aid in the identification, design, and testing of standards for the automated factory of the future. This paper describes the five layer hierarchical production control model proposed to manage these factories. Included is a discussion of the philosophy behind this model, the functional requirements of each layer within the model, a brief description of the data services needed to support this approach, and an overview of the techniques used to implement existing subsystems.

Survey of Job Shop Scheduling Techniques

The sections in this article are 1 Introduction 2 Mathematical Techniques 3 Dispatching Rules 4 Artificial Intelligence (AI) Techniques 5 Artificial Neural Networks 6 Neighborhood Search Methods 7 Fuzzy Logic 8 Swarm 9 Reactive Scheduling 10 Learning in Scheduling 11 Theory of Constraints 12 Summary and Conclusions

Toward modeling and simulation of critical national infrastructure interdependencies

Modern societys physical health depends vitally upon a number of real, interdependent, critical infrastructure networks that deliver power, petroleum, natural gas, water, and communications. Its economic health depends on a number of other infrastructure networks, some virtual and some real, that link residences, industries, commercial sectors, and transportation sectors. The continued prosperity and national security of the US depends on our ability to understand the vulnerabilities of and analyze the performance of both the individual infrastructures and the entire interconnected system of infrastructures. Only then can we respond to potential disruptions in a timely and effective manner. Collaborative efforts among Sandia, other government agencies, private industry, and academia have resulted in realistic models for many of the individual component infrastructures. In this paper, we propose an innovative modeling and analysis framework to study the entire system of physical and economic infrastructures. That framework uses the existing individual models together with system dynamics, functional models, and nonlinear optimization algorithms. We describe this framework and demonstrate its potential use to analyze, and propose a response for, a hypothetical disruption.

Enterprise simulation: a hybrid system approach

Manufacturing enterprise decisions can be classified into four groups: business decisions, design decisions, engineering decisions, and production decisions. Numerous physical and software simulation techniques have been used to evaluate specific decisions by predicting their impact on either system performance or product performance. In this paper, we focus on the impact of production decisions, evaluated using discrete-event-simulation models, on enterprise-level performance measures. We argue that these discrete-event models alone are not enough to capture this impact. To address this problem, we propose integrating discrete-event simulation models with system dynamics models in a hybrid approach to the simulation of the entire enterprise system. This hybrid approach is conceptually consistent with current business trend toward integrated systems. We show the potentials for using this approach through an example of a semiconductor enterprise.

A real-time production scheduler for a stochastic manufacturing environment

Abstract This paper proposes a framework lor addressing real-time scheduling problems in a stochastic manufacturing environment. The framework, which is based on the decomposition principles of mathematical programming, includes the specification of the problem which must be solved in order to generate, monitor, and update schedules in real-time. Concurrent simulation is used as the primary analysis tool in evaluating prospective schedules. Methodologies for addressing other problems will combine techniques from operations research and artificial intelligence.

Simulation-based shop floor control: formal model, model generation and control interface

In this paper, a structure and architecture for the rapid realization of a simulation-based real-time shop floor control system for a discrete part manufacturing system is presented. The research focuses on automatic simulation model and execution system generation from a production resource model. An Automatic Execution Model Generator (AEMG) has been designed and implemented for generating a Message-based Part State Graph (MPSG)-based shop level execution model. An Automatic Simulation Model Generator (ASMG) has been designed and implemented for generating an Arena simulation model based on a resource model (MS Access 97) and an MPSG-based shop level execution model. A commercial finite capacity scheduler, Tempo, has been used to provide schedule information for the Arena simulation model. This research has been implemented and tested for six manufacturing systems, including The Pennsylvania State University CIM laboratory.

A multi-leve/multi-layer architecture for intelligent shopfloor control

Abstract In this paper, we address the problem of designing an organizational structure to manage production activities in the factories of tomorrow. Those factories will have computers and other automated machinery performing many of the functions currently done by humans. We propose a multiplayer/multi-level control architecture to manage shopfloor activities, Each module in that architecture performs three functions: adaptation, optimization, and regulation. We describe these functions and discuss integration and future research issues.

Hierarchical production planning using a hybrid system dynamic-discrete event simulation architecture

Hierarchical production planning provides a formal bridge between long-term plans and short-term schedules. A hybrid simulation-based production planning architecture consisting of system dynamics (SD) components at the higher decision level and discrete event simulation (DES) components at the lower decision level is presented. The need for the two types of simulation has been justified. The architecture consists of four modules: enterprise-level decision maker, SD model of enterprise, shop-level decision maker and DES model of shop. The decision makers select the optimal set of control parameters based on the estimated behavior of the system. These control parameters are used by the SD and DES models to determine the best plan based on the actual behavior of the system. High level architecture has been employed to interface SD and DES simulation models. Experimental results from a single-product manufacturing enterprise demonstrate the validity and scope of the proposed approach.

Issues in the design and implementation of a system architecture for computer integrated manufacturing

Abstract The advent of sophisticated automation equipment and computer hardware and software is changing the way manufacturing is carried out. To be competitive, manufacturing companies must integrate these new technologies into their existing and future factories. In addition, they must integrate the planning, control and data management methodologies needed to make effective use of these technologies. This paper discusses several issues related to the design and implementation of a system architecture which can serve as the basis for integration. The architecture includes separate architectures for production planning and control, data management and data communications.

Modeling and monitoring of construction supply chains

The planning and management of supply chains require properly specifying the participating members and the relationships among them. Construction supply chains usually consist of numerous participants and are complex in structure. Representing construction supply chains using a network model can help understand the complexity, support re-configuration, identify the bottlenecks, and prioritize companys resources, as well as add values to the management of construction projects. Using a case example on the mechanical, electrical and plumbing (MEP) processes in a construction project, this paper demonstrates the modeling of construction supply chains using the Supply Chain Operations Reference (SCOR) framework developed by the Supply Chain Council (SCC). The SCOR modeling framework provides a structured and systematic way to model and decompose a supply chain from conceptual representation to process element specification. The SCOR framework is commonly used by corporations for strategic planning of their supply chains. This paper further presents a model-based service oriented framework that leverages the SCOR models for performance monitoring of construction supply chains. In the supply chain management and monitoring framework each supply chain process element is implemented as a discrete web service component. The framework is built on a service oriented collaborative system, namely SC Collaborator, that we have developed using web service technology, open standards, and open source technologies.