The dynamic rescheduler: conquering the changing production environment

Brown, Mike

doi:10.1109/caia.1988.196100

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…In addition, when it is necessary in advance to schedule several parallel activities, which share resources, the quality of manually generated schedules deteriorates with time due to unplanned events. This can cause disturbances and disruption to plans requiring modification actions or even rescheduling (Brown, 1988). Frequent rescheduling often results in instability and lack of continuity in detailed schedule execution.…”

Section: Introductionmentioning

confidence: 99%

Development of a knowledge model for managing schedule disturbance in steel-making

Roy

Adesola

Thornton³

2004

International Journal of Production Research

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This paper presents the development of a knowledge model, which describes the reasoning process in managing schedule disturbance (MSD) in steelmaking. Literature review shows the lack of research in developing a knowledge model for decision making in steelmaking. In this paper the knowledge model distinguishes three knowledge categories: the task knowledge, the inference knowledge and the domain knowledge. The knowledge is captured for ten most common types of disturbances in steelmaking. It is observed that a common inference model exists for the disturbance management. A knowledge elicitation methodology called XPat [eXpert Process Knowledge Analysis Technique] combined with CommonKADS approach was used to capture process knowledge for managing schedule disturbance in steelmaking.Finally the knowledge model is validated through paper based simulations of three common disturbance scenarios. The validation process consisted of three components: accuracy, completeness and consistency.

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Section: Introductionmentioning

confidence: 99%

Development of a knowledge model for managing schedule disturbance in steel-making

Roy

Adesola

Thornton³

2004

International Journal of Production Research

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“…As indicated above, the dominating characteristics of a given manufacturing environment have a significant influence the utility of various approaches and heuristics. Al-based production management research has addressed a variety of application domains: * Job shop scheduling -As has been noted in previous surveys [147,651, job shop manufacturing disciplines have historically received the most attention and much work continues in this area (e.g., [5,9,18,29,33,37,44,92,52,53,58,63,68,71,76,82,84] * Wafer fabrication -A problem domain that has gained increasing interest in recent years is wafer fabrication, which exhibits problem characteristics common to both job shop and flow shop environments [27,40,50,149,141,159].…”

Section: Al In Production Management: Dimensions and Scopementioning

confidence: 99%

“…This work starts from the assumption that better factory performance requires better solutions to the complexity problem (i.e., generation of schedules that more accurately reflect actual production constraints and objectives), and has emphasized the development of techniques capable of providing better predictive guidance. Under such approaches, the problem of executional uncertainty is commonly seen as one of effectively managing the current predictive schedule when mismatches are recognized relative to the current execution state (e.g., [18,26,29,119,138,155]). It is assumed that such unexpected circumstances lead to either incremental schedule revision or complete schedule regeneration relative to the constraints know known to be true about the current factory state, depending on the flexibility/decomposability of the scheduling techniques employed.…”

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confidence: 99%

Knowledge-based production management approaches, results and prospects

Smith

1992

Production Planning & Control

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Puoc reonr,rr burden forgirs -oileton of ,nforrralo-s.' esil-ted to A-eaq I'onur oer rcwn,*."nCludmng the time tor reviewing instrucions. searcmhrnq eiift-ri data sources gathering and maintaining the oata needed. and con'pieting and reviewing h CC. A1BSTRACT WMaxirrnum,10woras) In this paper we provide an overview of research in the field of knowledge-based production management. We begin by examining the important sources of decision-making difficulty in practical production management domains, discussing the requirements implied by each with respect to the development of effective production management tools, and identifying the general opportunities in this regard provided by Al-based technology. We then categorize work in the field along several different dimensions, indicating the principal types of manufacturing domains that have received attention, the particular production management and control activities that have been emphasized, and the various perspectives that have emerged with respect to the tradeoff that must be made in practical production management contexts between predictive decision-making to optimize behavior and reactive decision-making to manage executional uncertainty. The bulk of the paper focuses on summarizing the dominant approaches to knowledge-based production management that have emerged. Here, we identify the general concepts, principles, and techniques that distinguish various paradigms, characterize the strengths and weaknesses of each paradigm from the standpoint of different production management requirements, and indicate the results that work within each paradigm has produced to date. Among the paradigms for knowledge-based production management considered are rule-based scheduling, simulation-based scheduling, constraint-based scheduling, fuzzy scheduling, planning and scheduling, iterative scheduling, and interactive scheduling. We also examine work aimed at integrating heterogeneous planning and scheduling methods (both Al and CIR Lngoj) ,Indi th', rn-nctinn nf z)-wn fcnr mth~ijirv prniutn m neni ind cicnirol, Finalig-we 'in'ev morn. SUBJECT TERMS AbstractOver the past decade, a large (and continually increasing) number of efforts (both research and development) have sought to investigate and exploit the use of Artificial Intelligence (AI) concepts and techniques in production management applications. In some cases, Al-based concepts have provided frameworks for making traditional Operations Research (OR) techniques more accessible and usable in practical production management settings. In others, novel concepts and techniques have been developed that offer new opportunities for more cost-effective factory performance. While this field of "knowledge-based" production management is still fairly young and the literature is still dominated by experimental research systems, results are nonetheless starting to have an impact in actual production environments. In recent years, several systems have made their way into operation, and many have been attributed with substan...

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“…The impacts of the above factors are behind the observed fact that many companies have to re-schedule a high percentage of their planned production [17] [68]. According to a recent survey on the state of the art of real-time control (scheduling) issues in FMS (Flexible Manufacturing Systems) by [160], few of the reviewed control and scheduling systems have had the capability of quick re-scheduling needed in practice.…”

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confidence: 99%

Reactive Scheduling in Real Time Production Control

Szelke

Monostori

1999

Modeling Manufacturing Systems

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Reactive operation managenlent -Predictive, reactiveand proactive schedulingContinuous, steady improvement of manufacturing operation management from information technology (IT) perspective is a key requirement to manufacturing enterprises competing under the pressure of changing market demands.Manufacturing systems often operate in complex environments rife with uncertainty. The growing complexity of manufacturing operation management lies in the nonexpected tasks/events, nonlinearities, and a multitude of interactions which arise when attempting to control various activities in dynamic shop floors. This complexity and uncertainty seriously limit the effectiveness of conventional control and scheduling approaches. Reactive/proactive systems of shop floor control and scheduling with adaptive behaviour that is ensured by some embedded learning capability and based on real-time monitoring of executed processes, represent the viable alternative.The broad goal of manufacturing operation management, like other resource constrained, multi-agent planning/scheduling problems, is to achieve a co-ordinated efficient behaviour of manufacturing in servicing production demands while responding to changes in shop floors in a timely and cost-effective manner [164]. Operation scheduling, viewed a major issue of manufacturing operation management, has remained a complex co-ordination task despite the recent trends and efforts towards a structural rationalisation in manufacturing (e.g., through the implementation of group technology and JIT strategies). Quality of the operation scheduling process generally has a profound effect on the overall factory performance. Advance generation of the factory schedules is considered central to co-ordinate manufacturing activities in order to meet some organisational objectives, usually the three-pronged objective of producing parts of higher quality at lower cost within a shorter lead time. The generation of advance (predictive) schedules is also demanded for anticipating potential performance obstacles (e.g., resource contention), and for timely assuring physical preconditions (availability of all the needed resources, presetting of processes) to the execution of scheduled manufacturing activities, in order to minimise the disturbing effects on the overall manufacturing system operation.

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The dynamic rescheduler: conquering the changing production environment

Cited by 13 publications

References 4 publications

Development of a knowledge model for managing schedule disturbance in steel-making

Development of a knowledge model for managing schedule disturbance in steel-making

Knowledge-based production management approaches, results and prospects

Reactive Scheduling in Real Time Production Control

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