Knowledge-Based Production Management: Approaches, Results and Prospects

Abstract

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 behavior and reactive decision-making to manage executional uncertainty. The bulk of the paper focuses on summarizing 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.