Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

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

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

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine