AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware

As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application

A hybrid approach to space power control

Conventional control systems have traditionally been utilized for space-based power designs. However, the use of expert systems is becoming important for NASA applications. Rocketdyne has been pursuing the development of expert systems to aid and enhance control designs of space-based power systems. The need for integrated expert systems is vital for the development of autonomous power systems

Learning multiple fault diagnosis

This paper describes two methods for integrating model-based diagnosis (MBD) and explanation-based learning. The first method (EBL) uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of "learning while doing" model-based troubleshooting and could be called "online learning." The second diagnosis and learning method described here (EEL-STATIC) involves ''learning in advance." Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD while EBL can cause performance slow-down

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

Described here is the progress made by Levels 1, 2, and 3 of the Space Station Freedom in developing and applying advanced automation and robotics technology. Emphasis was placed on the Space Station Freedom program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) Progress Report 13, and issues of A&R implementation into the payload operations integration Center at Marshall Space Flight Center. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for Space Station Freedom

A two-level structure for advanced space power system automation

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed