William I. Bullers

Artificial Intelligence in Manufacturing Planning and Control

Abstract This paper explores some of the typical problems in manufacturing systems planning and control, particularly those pertinent to the automatic operation, and describes how artificial intelligence methods can be applied. We demonstrate how predicate logic and theorem proving techniques using resolution can be used in a manufacturing environment. Assertions of fact and axioms representing the knowledge required are given in an underlying data base. Illustrative problems demonstrate how user problems, such as assignment of jobs to machines when conflicts occur, can be handled by a decision support system in the framework of resolution in a problem reduction approach.

CONTROL AND DECISION SUPPORT IN AUTOMATIC MANUFACTURING SYSTEMS.

Abstract A new concept of Manufacturing Operating System (MOS) is presented as an approach to decision support as well as actual control of automatic manufacturing. The E-Net technique is used to model the MOS environment including reference data, operational data, and decision logic. Scheduling problems in a DNC line are illustrated.

Modified PERT versus fractile assessment of subjective probability distributions

Abstract A technique of subjective probability distribution assessment of uncertain quantities based on a most likely, optimistic, and pessimistic estimate as used in PERT (Program Evaluation and Review Technique) to manage large-scale projects, was compared to five direct fractile assessments. A beta probability density function was also fitted to each of the assessed distributions, compared, and then its parameters manipulated simultaneously using a scaling constant, to improve external calibration and accuracy. PERT assessments yielded somewhat looser subjective probability distributions than direct fractile assessments, and therefore fewer “surprises.” Beta density functions fit to tail fractiles were considerably tighter than beta densities fit to central fractiles. Tail fractile beta fits were also tighter than the assessed distributions, while central fractile beta fits were looser. For group generated proportions, the average underestimation bias of high valued uncertain quantities and overestimation bias of low valued uncertain quantities appeared to be reasonably well behaved. This suggested that each beta parameter might be manipulated separately using individual scaling constants as a method of recalibrating the assessor, to mitigate both the inherent biases of overconfidence (second moment bias) and departure of the .50 fractile from the true value (first moment bias). Further implications with respect to subjective probability distribution assessment and future research are discussed.

Toward a comprehensive conceptual framework for computer integrated manufacturing

Abstract Effective Computer Integrated Manufacturing (CIM) requires computer software to control automated processes, regulate production facilities, and generate information to support operational, tactical, and strategic decision making. An analysis of decision problems, managerial functions, and information systems is a prerequisite for the design of a CIM system. Four basic types of IS are analyzed as potential components in a CIM design methodology which seeks to integrate computerized manufacturing systems and management information systems.

A processing algorithm for master-detail records in a relational database

Large numbers of data processing applications require processing of master records and their associated detail records. These relationships between master and detail files may be classified as one‐to‐many, zero‐or‐one‐to‐many and many‐to‐many. All are important to information systems, but database management system implementations differ widely in their ability to represent and process such relationships. Processing of zero‐or‐one‐to‐many relationships, in particular, is difficult in most relational databases. An obscure relational algebra operator, left outer join, that provides such a capability is described and its importance to master‐detail processing is illustrated. An implementation of left outer join is presented for dBase III, a database management system providing some relational‐like capability.