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

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

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

A Decision Support System for Ship Maintenance Capacity Planning

In this paper, the basic framework and algorithms of a decision support system are discussed, which enhance process and capacity planning at a large repair shop. The research is strongly motivated by experiences in a project carried out at a dockyard, which performs repair, overhaul and modification programs for various classes of navy ships. We outline the basic requirements placed upon order acceptance, process planning and capacity scheduling for large maintenance projects. In subsequent sections a number of procedures and algorithms to deal with these requirements, in particular a procedure for workload-based capacity planning, a database system to support process planning are developed, as well as a resource-constrained project scheduling system to support work planning at a more detailed level. The system has been designed to support decision making at the Navy Dockyard in particular, however, we believe that, due to its generic structure, it is applicable to a wide range of project-based manufacturing and maintenance environments

Power plant preventive maintenance scheduling problem: a 0/1 mixed integer linear programming approach based on cost and reliability to establish an effective policy

The problem addressed is the Power Plant Preventive Maintenance Scheduling. The problem is taken from the power industry and it requires determining the perioThis paper addresses the Power Plant Preventive Maintenance Scheduling Problem. The problem is taken from the power industry, where providing electric energy to the consumers without interruption is of high importance. This problem is usually treated in the long-term exploitation of electric production systems. It requires determining the period for which generating units of an electric power utility should be taken offline for planned preventive maintenance over a time horizon, usually on a yearly basis. The objective is to minimize the total operating cost while a set of constraints is satisfied, with emphasis on realiability.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech