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

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

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

A multi-agent system with application in project scheduling

The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.

Integrated engineering environments for large complex products

An introduction is given to the Engineering Design Centre at the University of Newcastle upon Tyne, along with a brief explanation of the main focus towards large made-to-order products. Three key areas of research at the Centre, which have evolved as a result of collaboration with industrial partners from various sectors of industry, are identified as (1) decision support and optimisation, (2) design for lifecycle, and (3) design integration and co-ordination. A summary of the unique features of large made-to-order products is then presented, which includes the need for integration and co-ordination technologies. Thus, an overview of the existing integration and co-ordination technologies is presented followed by a brief explanation of research in these areas at the Engineering Design Centre. A more detailed description is then presented regarding the co-ordination aspect of research being conducted at the Engineering Design Centre, in collaboration with the CAD Centre at the University of Strathclyde. Concurrent Engineering is acknowledged as a strategy for improving the design process, however design coordination is viewed as a principal requirement for its successful implementation. That is, design co-ordination is proposed as being the key to a mechanism that is able to maximise and realise any potential opportunity of concurrency. Thus, an agentoriented approach to co-ordination is presented, which incorporates various types of agents responsible for managing their respective activities. The co-ordinated approach, which is implemented within the Design Co-ordination System, includes features such as resource management and monitoring, dynamic scheduling, activity direction, task enactment, and information management. An application of the Design Co-ordination System, in conjunction with a robust concept exploration tool, shows that the computational design analysis involved in evaluating many design concepts can be performed more efficiently through a co-ordinated approach

Operational design co-ordination : an agent based approach

Operational design co-ordination has been identified as the basis for an approach to engineering design management that is more comprehensive than those that currently exist. As such, an integrated and holistic approach to operational design co-ordination has been developed that enables design to be managed in a coherent, appropriate and timely manner. Furthermore, the approach has been implemented within an agent-based software system, called the Design Co-ordination System, which has been applied to an industrial case study involving the computational design analysis of turbine blades. This application demonstrates that managing and adjusting in real-time in an operationally co-ordinated manner enables reductions in the time taken to complete the turbine blade design process to be achieved

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications