George K. Hutchinson

The economic value of flexible automation

Abstract Flexible Manufacturing Systems (FMS) have the potential to substantially improve the productivity of mid-volume, mid-variety production. The introduction of these systems into industry must be done on the basis of cost justification. Such justification has historically been possible through the use of easily measurable costs such as labor, inventory reduction, and reduced scrap. In the case of flexible automation, the value of flexibility is difficult if not impossible to measure. This paper attempts to provide a measure of the value of flexibility by comparing the costs of a typical fixed automation approach, with those of a transfer line (TL) in the same management environment. It is concluded that there is economic value to flexibility under many circumstances.

A quantification of the value of flexibility

Abstract Managers of production systems are often faced with the decision of acquiring production capacity to meet forecast demand. Return on investment (ROI) models have traditionally been used, but unfortunate results have occurred in some instances, because the benefits of flexibility were not quantified. This paper concentrates on two aspects of flexibility, the ability to change manufacturing mission and capacity. They are shown to have quantifiable economic value in a particular situation by use of a decision theoretic model. System parameters of interest are investigated to estimate their influence, including uncertainty.

Supporting complex real-time decision making through machine learning

Abstract This paper presents FMS-DSS, a system for supporting complex, real-time decision making in the FMS scheduling and control domain. FMS-DSS differs from traditional DSSs in that it can acquire scheduling and control knowledge from historical data comprising prior decisions. This knowledge is applied to support subsequent decision making. It manages complexity through hierarchically structuring the users objectives, and can deal with noise in the form of missing, inaccurate, or erroneous data. Results indicate that a machine learning based approach can provide effective support for repetitive real-time decision making and outperform static scheduling rules.

Intelligent decision support for flexible manufacturing: Design and implementation of a knowledge-based simulator

There is considerable research in decision making for the flexible manufacturing systems (FMS) domain. Much of it tends to be fragmentary due to differences in assumptions, constraints, modeling techniques and solution strategies. This paper suggests a common basis for support of FMS decision making as an attempt to alleviate these problems. It describes the architecture of an intelligent knowledge-based simulator KBSim, that provides systematic FMS research capability. KBSim is applied to an industrial FMS scheduling problem to reduce both mean flow time and tardiness when compared to several common scheduling heuristics. It is also used in a research-oriented modified job shop scheduling application. In both cases, it outperformed traditional decision making heuristics. Its efficiency, ease of use, and portability suggest that KBSim will prove useful in the automation of adaptive system control, facilitating periodic review of FMS decisions, and giving management a competitive edge.

Production system design: A directed graph approach

Abstract The problems of designing advanced production systems are considered from the viewpoints of two quite different social systems and found to be similar. The problems are characterized as being large, complex, and dynamic. Simulation is found to be a good general methodological approach, and directed graphs a particularly useful means of specifying production problem relationships for both analysis and simulation. The Appendices contain discussions of the computer systems, both based on directed graphs, in use in these countries.

A synergistic approach to manufacturing systems control using machine learning and simulation

This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach.

The design of an automated material handling system for a job shop

Abstract This paper describes the application of techniques developed for the management and control of Flexible Manufacturing Systems (FMSs) to a traditional job shop manufacturing sheet metal parts on typical sheet fabricating machines. The problem was the design and evaluation of an automated material handling subsystem (MHS) to support the shop. The MHS controlled the shop loading and floor control by its choice of orders to transport and enter the shop. Detailed simulation models were used to simulate the existing shop, the shop with a MHS, and several algorithms for loading and sequencing orders through the shop. This paper describes the shop, the models developed, the algorithms tested, and the simulation results. A highlight of the study was the ease and speed with which multiple models were developed using an interactive simulation generator, CAPS. Of particular interest is the procedure for shop scheduling, downstream pull, which uses heavily loaded machines to provide sequencing alternatives with the objective of “pulling” work through them to reach more lightly loaded machines. Shop output was estimated to increase from 26 orders per day to 45, a 73% increase, and turnaround time to decrease from ten days to two days.

Simulation of advanced manufacturing systems

Flexible manufacturing systems (FMSes) use a variety of direct numerical control machines embedded within unique material handling systems. An FMS is a hybrid between fixed production and job shop facilities. Possible order-of-magnitude decreases are foreseen in unit manufacturing costs through the use of an FMS. The input-output conversion process of an FMS is so complex that evaluation is most readily accomplished by simulation within experimental designs. A Simscript model is in use to: determine FMS physical configuration; create decision rule sets for online computer controls; and reduce the management uncertainty in considering investment in FMS installations.

Productivity of programmable automation—A simulation-based analysis of a robotic system for component insertion

Abstract Flexible automation vendors must consider hardware and software investments to improve their equipment competitive position through productivity. A rational approach requires comparing the expected improvement of a potential investment with its cost. This paper demonstrates the use of simulation and sensitivity analysis to estimate productivity potential for investments in hardware and software of a two-handed robotic system used in populating circuit boards.

Information organization in flexible automation systems

Abstract Felxible Automation Systems ( FAS ) are comprised of three major sybsystems: the workstations, material handling, and computer control. Much effort and research have been devoted to the first two and to system control, the loading and scheduling of the system. Little effort has been expended, however, on the information requirements of FASS , although their control is information intensive. Every move of every axis of every device must be coordinated, monitored, and controlled. Information must be stored and transmitted to the needed device at the appropriate time. The status of the system must be continuously surveyed, updated, and verified. Hierarchical systems have been suggested for the control of FMSS , and this has implications for their structure, which in turn influences computing and communication requirements, system performance, reliability, and failure recovery. This paper discusses storage and data flow requirements as a function of the systems manufacturing mission. Two control architectures, centralized and two-level distributed, are considered, and the computing, storage, and communications requirements calculated for each. Analysis of results indicates that there is a break-even point between centralized and decentralized systems that is a function of the manufacturing mission but independent of FAS size and operating environment.