caa a22 4500 475778405 CHVBK 20180406123637.0 cr unu---uuuuu 170329e20000401xx s 000 0 eng 10.1023/A:1008151831821 doi (NATIONALLICENCE)springer-10.1023/A:1008151831821 Information-Based Dynamic Manufacturing System Scheduling [Elektronische Daten] [Selwyn Piramuthu, Michael Shaw, Bill Fulkerson] 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 dynamic 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 real system. In pa ticular, 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. Kluwer Academic Publishers, 2000 dynamic scheduling nationallicence genetic algorithms nationallicence inductive learning nationallicence Piramuthu Selwyn Operations and Information Management, Wharton School, University of Pennsylvania, Philadelphia aut Shaw Michael Department of Business Administration, University of Illinois, Urbana-Champaign aut Fulkerson Bill CIS-Technology Integration, Deere & Company, Moline, Illinois aut International Journal of Flexible Manufacturing Systems Kluwer Academic Publishers 12/2-3(2000-04-01), 219-234 0920-6299 12:2-3<219 2000 12 10696 https://doi.org/10.1023/A:1008151831821 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1008151831821 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Piramuthu Selwyn Operations and Information Management, Wharton School, University of Pennsylvania, Philadelphia aut NATIONALLICENCE 700 1- Shaw Michael Department of Business Administration, University of Illinois, Urbana-Champaign aut NATIONALLICENCE 700 1- Fulkerson Bill CIS-Technology Integration, Deere & Company, Moline, Illinois aut NATIONALLICENCE 773 0- International Journal of Flexible Manufacturing Systems Kluwer Academic Publishers 12/2-3(2000-04-01), 219-234 0920-6299 12:2-3<219 2000 12 10696 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer