Knowledge-based production management approaches, results and prospects

Abstract

Over the past decade, a large (and continually increasing) number of efforts (both research and development) have sought to investigate and exploit the use of artificial intelligence (AI) concepts and techniques in production management applications. In some cases, AI-based concepts have provided frameworks for making traditional operations research (OR) techniques more accessible and usable in practical production management settings. In others, novel concepts and techniques have been developed that offer new opportunities for more cost-effective factory performance. While this field of ‘knowledge-based’ production management is still fairly young and the literature is still dominated by experimental research systems, results are nonetheless starting to have an impact in actual production environments. In recent years, several systems have made their way into operation, and many have been attributed with substantial manufacturing performance gains.

In this paper, we provide an overview of research in the field of knowledge-based production management. We begin by examining the important sources of decision-making difficulty in practical production management domains, discussing the requirements implied by each with respect to the development of effective production management tools, and identifying the general opportunities in this regard provided by AI-based technology. We then categorize work in the field along several different dimensions, indicating the principal types of manufacturing domains that have received attention, the particular production management and control activities that have been emphasized, and the various perspectives that have emerged with respect to the tradeoff that must be made in practical production management contexts between predictive decision making to optimize behaviour and reactive decision-making to manage executional uncertainty. The bulk of the paper focuses on summarising the dominant approaches to knowledge-based production management that have emerged. Here, we identify the general concepts, principles, and techniques that distinguish various paradigms, characterize the strengths and weaknesses of each paradigm from the standpoint of different production management requirements, and indicate the results that work within each paradigm has produced to date. Among the paradigms for knowledge-based production management considered are rule-based scheduling, simulation-based scheduling, constraint-based scheduling, fuzzy scheduling, planning and scheduling, iterative scheduling, and interactive scheduling. We also examine work aimed at integrating heterogeneous planning and scheduling methods (both AI and OR based) and the construction of systems for multi-level production management and control. Finally, we survey more recent research in the areas of distributed production management and automated learning of factory floor control policies from experience. We conclude by discussing the current and future prospects of this work. In doing so, we also identify some of the important obstacles and challenges currently facing the field.

Notes

Stephen F. Smith is a Senior Research Scientist in the Robotics Institute at Carnegie Mellon University, and Director of the Production Control Laboratory within the Institute's Center for Integrated Manufacturing Decision Systems. He holds a B.S. degree in mathematics from Westminster College, and M.S. and Ph.D. degrees in Computer Science from the University of Pittsburgh. His research interests include constraint-based planning and scheduling, integration of predictive and reactive decision-making, distributed problem solving, temporal reasoning, machine learning, and knowledge-based production management. Dr. Smith has been a principal architect of several innovative knowledge-based scheduling systems for complex manufacturing and space applications. His current work focused on the design of adaptable production management systems, which continually refine and adjust decision-making knowledge and strategies based on observed performance in the executing environment.