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Artificial Intelligence

Knowledge-Based Systems Techniques and Applications in Scheduling

Profile image of Nur NabilahNur Nabilah

Abstract

Scheduling problems are found in a lot of application domains. Well known is the scheduling of production where manufacturing operations have to be assigned to limited resources like machines, but also other applications are important, e.g., the scheduling of airline crews, space missions, projects in different domains, clinical surgery, even timetabling and processor scheduling include scheduling problems. In general, scheduling deals with the temporal assignment of activities to limited resources where a set of constraints has to be regarded. Due to the exponential size of scheduling problems it is quite difficult to create good or optimal schedules shown by optimized goal functions or other evaluation criteria. But not only the generation of a schedule is a hard problem, even harder-and the normal case in everyday work-is the adaptation of an existing schedule to the changing scheduling environment. The changes include events like resource breakdowns as well as limitations or prescriptions interactively given by the user of the scheduling system. Scheduling systems have been built to support the users in performing their scheduling tasks. The systems include scheduling knowledge as well as presentation and database components. In this chapter an overview of scheduling problems, techniques to solve scheduling problems, scheduling systems as well as research issues in scheduling is given.

Related papers

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Artificial Intelligence in Engineering, 1988

The aim of this paper is to present a job-shop scheduling system designed to detect and react to inconsistencies between a scheduling plan and the actual course of events on a shop floor. More precisely, the SONIA system integrates both predictive and reactive scheduling components. A schedule management system is used to update schedule descriptions and detect inconsistencies as the scheduling components make decisions and unexpected events happen on the shop floor. Analysing components can be used to evaluate both predictive and reactive problem-solving contexts in order to choose the most appropriate actions and determine which heuristics the scheduling components should use. A control system built on a blackboard architecture coordinates the use of the various components of SONIA and adjusts their behaviour according to the problem-solving context. Preliminary experiments are made with the help of a simulator. These experiments should provide us with pieces of knowledge about the rapidity of various reactive strategies and their ability to respect various scheduling criteria.

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Constraint-Based Scheduling : A Tutorial
Claude M Le Pape

2005

Given a set of resources with given capacities, a set of activities with given processing times and resource requirements, and a set of temporal constraints between activities, a “pure” scheduling problem consists of deciding when to execute each activity, so that both temporal constraints and resource constraints are satisfied. Most scheduling problems can easily be represented as instances of the constraint satisfaction problem (Kumar, 1992): given a set of variables, a set of possible values (domain) for each variable, and a set of constraints between the variables, assign a value to each variable, so that all the constraints are satisfied.

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Knowledge Media Institute Intelligent scheduling - A Literature Review
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KB-SCHED: Knowledge-based scheduler for complex and dynamic systems
Cemalettin Kubat

Journal of Intelligent Manufacturing, 2000

Scheduling problems are becoming more and more complex everyday. This makes the current rules and algorithms dif®cult to comply with the requirements. New machines with the capabilities of processing more than one jobs is being developed. Sometimes one job is divided into parts and processed by more than one machine at the same time. These make the current algorithms insuf®cient. Arti®cial intelligence technologies, especially expert systems are proven to deal with such dynamic complex problems in several domains. In this study, an example of such a complex problem is introduced and knowledge-based scheduling for these kind of problems is elaborated with a real life industrial example.

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A Knowledge-Based System for Scheduling in a Discrete Manufacturing Process
R. Pesenti

IFAC Proceedings Volumes, 1992

Scheduling in a manufacturing environment generally is based on sevezal kinds of information. some of which cannot be handled by a pure numerical approach. In this paper a possible structure fCX' the integration of a nwnerical and a knowledge-based scheduling procedure is presented. The various types of information are described in detail. and the interaction of the components of the system is examined. Keywords. Manufacturing processes. artificial intelligence. heuristic programming. flexible manufacturing. production control. decision theory.

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Model for the classification of scheduling problems based on ontology
Soulef Khalfallah

2021

In this paper, we propose an ontology-based architecture for the classification of a scheduling problem. In fact, planning and scheduling is a major concern for the production enterprise as it contributes to its profit. Most of the time, researchers propose algorithms that relax some reel situations’ constraints; given that reel shop description are not taken directly from the floor supervisor, or given the complexity of the problem. The objective of ontology for the classification of scheduling problem is to build a bridge between practitioners and researchers in order to identify the reel classification of the problem. Thus, in this paper, we start by presenting a general model for the classification of scheduling problem. On this model, we build specific domain ontology. The specific domain ontology functions as a knowledge base and a data access point for the scheduling problem classification system.

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Constraint logic programming and scheduling problems
Christophe Varnier

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929)

Classical approaches f o r the resolution of combinatorial problems are of two kinds: exact methods or approximate ones. Scheduling problems enter in this framework. Up to now, Operational Research tools were proposed to consider such problems. Recently, new resolution methods were developped. This is the case for the familly of stochastic methods or genetic algorithms. But, real life problem that come from the industrial environment require more jlexible tools. This is the purpose of Constraint Logic Programming approach. It allows one to ally versatility and eficiency. We will show in this paper, why this new generation of tools seems to be interesting for solving scheduling problems.

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A Web Interface for Accessing Scheduling Methods in a Distributed Knowledge Base
Maria Leonilde R. Varela

IFIP International Federation for Information Processing, 2004

With the increase of the Internet and virtual communities, interfaces for web applications and automated services are becoming an emergent necessity. In this paper we propose a web interface for enabling access to different manufacturing scheduling methods, which can be remotely available and accessible from a distributed knowledge base. Through this interface users can register and join together as a community and share their scheduling knowledge. Scheduling problems can occur in several different manufacturing environments and the web system searches in the distributed knowledge base for appropriate solving methods. Therefore, through this interface once suitable available methods, for a given problem, are identified, it enables running one or more of them, made accessible by the system. Moreover, new methods can be continuously incorporated in the system's distributed repository, in a user-friendly way, feeding the knowledge base.

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Constraint-Based Scheduling: An Introduction for Newcomers
Roman Barták

IFAC Proceedings Volumes, 2003

Constraint-based scheduling is an approach for solving real-life scheduling problems by stating constraints over the problem variables. By providing generic constraint satisfaction techniques on one side and specialised constraints on the other side, constraint programming achieves a very good generality and efficiency and thus it becomes very popular in solving real-life combinatorial (optimisation) problems. In this paper we present some constraint satisfaction techniques used in constraint-based scheduling. Our goal is to introduce the technology to newcomers rather than to provide a deep survey of the area or to describe some new results there.

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Design of Scheduling Algorithms
Jan Riezebos

Behavioral Operations in Planning and Scheduling, 2010

The academic field of production research has been growing rapidly over the last decades with researchers proposing numerous analytical and heuristic optimization methodologies for the solution of planning & scheduling problems. However, adaption by manufacturing companies is lagging behind. This paper suggests that the basic reason behind this imbalance is the inadequate representation of the planning & scheduling process when designing decision support systems. Hence, the algorithms that are designed and included in these systems might not reflect the problems that actually have to be solved in practice. In this paper we discuss the basic factors that are important for the development of planning & scheduling decision support systems. These factors will be based on insights from cognitive psychology, computer science, and operations management.

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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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In this paper we discuss a number of issues related to the design and development of scheduling systems for manufacturing. We first discuss database and knowledge base issues, and describe the advantages and disadvantages of the various types with regard to their use in scheduling systems. We then focus on schedule generation issues and the design of scheduling engines. We describe the various architectures and their potential. Furthermore, we discuss the various forms of user interfaces that are used in scheduling systems. Finally, the implementation issues are considered

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A knowledge based framework for developing and customizing schedulers
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This paper describes a general purpose sched1tler into which one can add knowledge necessary to solve specific scheduler problems. We believe our approach typifies how software should be developed for specific classes of applications. Using a general scheduler is analogous to the use of an expert system shell to which one adds application specific rules. We have performed experiments for adding knowledge to three classes of schedulers -job shop schedulers, transportation sched ulers and time tabling schedulers. We show how the framework provides to flexibility in performing exper iments. 1 Scheduling -A Model Domain for Knowledge Based Software Scheduling, the allocation of resources to tasks sub ject to constraints [Bak74], is a common activity. Many computer programs have been and continue to be developed to perform scheduling. When several dif ferent schedulers exist within a single organization, a question arises as to whether effort expended in devel oping one scheduler, for example dispatching trucks to warehouses, might be useful for another, such as as signing technicians to repair jobs. This paper concerns Choose Task Choose Resource Allocate Prop.I(Jate Cons_t_;ain ts and Updat

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