Information-based dynamic manufacturing system scheduling

被引：1

作者：

Piramuthu, Selwyn ^{[1
]}

Shaw, Michael ^{[2
]}

Fulkerson, Bill ^{[3
]}

机构：

[1] Operations and Info. Management, Wharton School, University of Pennsylvania, Philadelphia, PA

[2] Dept. of Business Administration, University of Illinois, Urbana-Champaign, IL

[3] CIS-Technology Integration, Deere and Company, Moline, IL

来源：

International Journal of Flexible Manufacturing Systems | 2000年 / 12卷 / 02期

关键词：

D O I：

10.1023/a:1008151831821

中图分类号：

学科分类号：

摘要：

Information about the state of the system is of paramount importance in determining the dynamics underlying manufacturing systems. In this paper, we present an adaptive scheduling policy for dynamics manufacturing system scheduling using information obtained from snapshots of the system at various points in time. Specifically, the framework presented allows for information-based dynamic scheduling where information collected about the system is used to (1) adjust appropriate parameters in the system and (2) search or optimize using genetic algorithms. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. Experimental studies indicate the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a rael system. In particular, its relative performance improves further when there are frequent disruptions and when disruptions are caused by the introduction of tight due date jobs and machine breakdown - two of the most common sources of disruption in most manufacturing systems. From an operational perspective, the most important characteristics of the pattern-directed scheduling approach are its ability to incorporate the idiosyncratic characteristics of the given system into the dispatching rule selection process and its ability to refine itself incrementally on a continual basis by taking new system parameters into account.

引用

页码：219 / 234

页数：15

共 16 条

[1]

Aytug H., Bhattacharyya S., Koehler G.J., Genetic Learning Through Simulation: An Investigation in Shop-Floor Scheduling, Annals of Operations Research, 78, pp. 1-29, (1998)

[2]

Aytug H., Bhattacharyya S., Koehler G.J., Snowdown J.L., A Review of Machine Learning in Scheduling, IEEE Transactions on Engineering Management, EM-41, pp. 165-171, (1994)

[3]

Baker K.R., Sequencing Rules and Due-Date Assignments in a Job Shop, Management Science, 30, 9, pp. 1093-1104, (1984)

[4]

Hausman W.H., Lee H.L., Masri S.M., Dynamic Production Scheduling for Fuel Sender Manufacturing, (1987)

[5]

Jawahar N., Aravindan P., Ponnambalam S.G., Raghavendra L.N., Knowledge-Based Workcell Attribute Oriented Dynamic Schedulers for Flexible Manufacturing Systems, International Journal of Advanced Manufacturing Technology, 14, 7, pp. 514-538, (1998)

[6]

Kim C.O., Min H.S., Yih Y., Integration of Inductive Learning and Neural Networks for Multiobjective FMS Scheduling, International Journal of Production Research, 36, 9, pp. 2497-2509, (1998)

[7]

Li D.C., Wu C.S., Torng K.Y., Using an Unsupervised Neural-Network and Decision Tree as Knowledge Acquisition Tools for FMS Scheduling, International Journal of System Sciences, 28, 10, pp. 977-985, (1997)

[8]

Lin L., Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching, Machine Learning, 8, pp. 293-321, (1992)

[9]

Lo Z.-P., Bavarian B., Multiple Job Scheduling with Artificial Neural Networks, Computers and Electrical Engineering, 19, 2, pp. 87-101, (1993)

[10]

Piramuthu S., Park S.C., Raman N., Shaw M.J., Integration of Simulation Modeling and Inductive Learning in an Adaptive Decision Support System, Decision Support Systems, 9, pp. 127-142, (1993)

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