An expert system for the selection of scheduling rules in a job shop

Alexander, Suraj M.

doi:10.1016/0360-8352(87)90010-6

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…ISA [33], PAMS [39], and [8] are three examples of rule-based scheduling systems. The expert systems approach has also been used for the selection of heuristic scheduling rules in a job-shop [2].…”

Section: G Expert Systems and Decision Support Systemsmentioning

confidence: 99%

Knowledge-based approaches for scheduling problems: a survey

Noronha

Sarma

1991

IEEE Trans. Knowl. Data Eng.

104

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Abstract-Scheduling is the process of devising or designing a procedure for a particular objective, specifying the sequence or time for each item in the procedure. Qpical scheduling problems are railway time-tabling, project scheduling, production scheduling, and scheduling computer systems as in flexible manufacturing systems and multiprocessor scheduling. Further, there are a number of related problems belonging to the larger class of planning problems, such as the early stage of project management and resource allocation in a job shop.Scheduling is a rich area demanding the application of efficient methods to tackle the combinatorial explosion that results in real world applications. Recent developments in artificial intelligence (AI) have led to the use of knowledge-based techniques for solving scheduling problems. In this paper, we survey several existing intelligent planning and scheduling systems with the aim of providing a guide to the main AI techniques used. In view of the prevailing difference in usage of the terms planning and scheduling between AI and OR, we present a taxonomy of planning and scheduling problems. We illustrate the modeling of real world problems b m closed deterministic worlds to complex real worlds with the project scheduling example. We survey some of the more successful planning and scheduling systems, and highlight their features. Finally, we consolidate the AI approaches to knowledge representation and problem solving in the project management context. Index Tern-Intelligent systems, knowledge-based systems, planning, project management, scheduling. I. INTRODUC~ONCHEDULING is the process of devising or designing S a procedure for a particular objective, specifying the sequence or time for each item in the procedure. Typical scheduling problems are railway time-tabling, project scheduling, production scheduling, mass transit scheduling, hydropower scheduling, scheduling nurse shifts in a hospital, etc. Emerging application examples of scheduling in computer systems are in flexible manufacturing systems, multiprocessor scheduling, robot activity scheduling, scheduling in very large scale networks and hard real-time scheduling. Further, there are a number of related problems belonging to the larger class of planning problems, e.g., the early stage of project management, and resource allocation in a job shop. Even this small sample shows the wide range and diversity of scheduling problems, and their importance in almost every sphere of human life stems from the need to utilize limited resources in as efficient a way as possible, simultaneously satisfying several domain-specific constraints.Classical approaches to scheduling problems, as practiced in the management science and operations research (OR) fields, are characterized by a reduction of the problem into a mathematical programming formulation, and subsequent solution by formal algorithmic methods. These approaches face the difficulty that the problem quickly becomes computationally infeasible for even moderately big problems,...

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Section: G Expert Systems and Decision Support Systemsmentioning

confidence: 99%

Knowledge-based approaches for scheduling problems: a survey

Noronha

Sarma

1991

IEEE Trans. Knowl. Data Eng.

104

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“…JSP is a classic combinatorial optimization problem. To solve this problem, many different types of optimization algorithms have been proposed, such as heuristic rules [4,5] , exact algorithms [6] , approximate algorithms [7] , meta-heuristic algorithms [8] , expert systems [9] , neural networks [10] and different kinds of artificial intelligence method [11−13] . Among them, the meta-heuristic algorithm is the most effective one, because it has the ability to jump out of the local optimum during its search [14] .…”

Section: Introductionmentioning

confidence: 99%

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Gui,

Li,

Zhang

et al. 2024

Tsinghua Sci. Technol.

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Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe. By combining domain knowledge of the specific optimization problem, the search efficiency and quality of meta-heuristic algorithms can be significantly improved, making it crucial to identify and summarize domain knowledge within the problem. In this paper, we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem (JSP). Firstly, this paper delves into the importance of domain knowledge in optimization algorithm design. After that, the development of different methods for the JSP are reviewed, and the domain knowledge in it for meta-heuristic algorithms is summarized and classified. Applications of this domain knowledge are analyzed, showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms. Finally, this paper analyzes the relationship among domain knowledge, optimization problems, and optimization algorithms, and points out the shortcomings of the existing research and puts forward research prospects. This paper comprehensively summarizes the domain knowledge in the JSP, and discusses the relationship between the optimization problems, optimization algorithms and domain knowledge, which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

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“…To accomplish optimum scheduling, an ES should be able to analyze the production environment, match it with existing theoretical results and suggest the best possible match. Alexander (1987) presented an ES framework that has the potential to select an appropriate scheduling rule where the jobshop data are available.…”

Section: Introduction 2 Production Schedulingmentioning

confidence: 99%

Knowledge based scheduling systems: a framework

1990

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Scheduling is a complex problem which occurs often in a manufacturing environment. There are many possible variations within the context of a scheduling problem, enough to overwhelm anyone but an expert. A production manager, faced with the task of job scheduling for a particular process, might well benefit from the advice of a scheduling expert. Even though the scheduling literature is full of optimizing rules for a variety of manufacturing environments few, if any, of these rules have been used in practice. The bewildering variety of theoretical results, the lack of a mechanism to match theoretical results to practical problems and the difficulty in establishing the practical efficacy of the theoretical results are among the reasons for this gap between scheduling theory and practice. This paper describes a scheduling expert system framework which addresses these problems and helps bridge this gap. The solution techniques available in SCHEDEX produce a specific schedule for the problem according to a procedure which has been mathematically proven to provide a satisfactory and often optimal solution, given the criteria for the problem. A simulation model also tests for the effectiveness of a selected technique with respect to the chosen performance measure(s).

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An expert system for the selection of scheduling rules in a job shop

Cited by 18 publications

References 10 publications

Knowledge-based approaches for scheduling problems: a survey

Knowledge-based approaches for scheduling problems: a survey

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Knowledge based scheduling systems: a framework

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