Modeling and solving multi-site scheduling problems

Multi-site scheduling deals with the scheduling problems of an enterprise with several distributed production sites, where sites are using the intermediate products of the other sites to manufacture the products of the enterprise, e.g. in car or airplane manufacturing. In the multi site-scheduling scenario we differentiate between a global and a local scheduling level. On the global level a global schedule is generated providing the requirements for the local level schedulers. The local schedulers then have to transform the global schedule into a concrete local schedule for manufacturing. Different goals have to be regarded on the different levels. In case of unforeseen events rescheduling and manual interaction is necessary on both levels. Due to the distribution of the problem to several units scheduling as well as coordination problems have to be solved. This paper presents an approach that adopts modeling and problem solving techniques used for local scheduling problems for the new global scheduling problems. Additionally, specific problems are solved by new approaches, e.g., coordination using a blackboard, heuristic and fuzzy techniques dealing with the highly dynamic environment and the imprecise knowledge. The multi-site scheduling problem as well as selected results of the approaches on the different levels will be presented.

[1]  Peter Mertens,et al.  Untersuchung wissensbasierter und weiterer ausgewählter Ansätze zur Unterstützung der Produktionsfeinplanung — ein Methodenvergleich , 2000, Wirtschaftsinf..

[2]  Hans-Jürgen Appelrath,et al.  Multi-site scheduling with fuzzy concepts , 1998, Int. J. Approx. Reason..

[3]  Norman Sadeh,et al.  A Blackboard Architecture for Integrating Process Planning and Production Scheduling , 1998 .

[4]  Austin Tate,et al.  Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Inititive , 1996 .

[5]  Wolfgang Slany,et al.  Scheduling as a fuzzy multiple criteria optimization problem , 1996, Fuzzy Sets Syst..

[6]  Ralf Bruns,et al.  Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling , 1993, ICGA.

[7]  Gerald L. Thompson,et al.  A mixed-initiative scheduling workbench integrating AI, OR and HCI , 1993, Decis. Support Syst..

[8]  Jürgen Sauer,et al.  Meta-scheduling using dynamic scheduling knowledge , 1993 .

[9]  Jürgen Dorn,et al.  Scheduling of production processes , 1993 .

[10]  Stephen F. Smith,et al.  Knowledge-based production management approaches, results and prospects , 1992 .

[11]  Vipin Kumar,et al.  Algorithms for Constraint-Satisfaction Problems: A Survey , 1992, AI Mag..

[12]  Pascal Van Hentenryck,et al.  Solving Large Combinatorial Problems in Logic Programming , 1990, J. Log. Program..

[13]  K. G. Kempf,et al.  Manufacturing planning and scheduling: where we are and where we need to be , 1989, [1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications.

[14]  Jürgen Saver Knowledge-Based Design of Scheduling Systems , 2001 .

[15]  Jürgen Sauer,et al.  Knowledge-Based Design of Scheduling Systems , 2001, Intell. Autom. Soft Comput..

[16]  Jürgen Sauer,et al.  Knowledge-Based Systems in Scheduling , 2000 .

[17]  Jürgen Sauer,et al.  Towards agent-based multi-site scheduling , 2000, PuK.

[18]  Jürgen Sauer,et al.  A Multi-Site Scheduling System , 1998 .

[19]  Volker Nissen,et al.  Betriebswirtschaftliche Anwendungen des Soft Computing , 1998 .

[20]  Jürgen Sauer,et al.  Ein Ablaufplanungssystem auf Basis Neuronaler Netze , 1998 .

[21]  Mark S. Fox,et al.  Intelligent Scheduling , 1998 .

[22]  Jürgen Sauer,et al.  Knowledge-Based Scheduling Systems in Industry and Medicine , 1997, IEEE Expert.

[23]  Jürgen Sauer,et al.  Scheduling and Meta-Scheduling , 1995, Logic Programming: Formal Methods and Practical Applications.

[24]  Jürgen Dorn,et al.  Iterative Improvement Methods for Knowledge-Based Scheduling , 1995, AI Commun..

[25]  Elizabeth Szelke,et al.  Artificial Intelligence in Reactive Scheduling , 1995, IFIP Advances in Information and Communication Technology.

[26]  Philippe Baptiste,et al.  Incorporating Efficient Operations Research Algorithms in Constraint-Based Scheduling , 1995 .

[27]  Jürgen Sauer,et al.  Wissensbasiertes Lösen von Ablaufplanungsproblemen durch explizite Heuristiken , 1993, DISKI.

[28]  I. B. Turksen,et al.  Fuzzy Logic-Based Expert Systems for Operations Management , 1992 .

[29]  Oliver Wauschkuhn,et al.  Untersuchung zur verteilten Produktionsplanung mit Methoden der logischen Programmierung , 1992, IWBS Report.

[30]  Stephen F. Smith,et al.  Issues in the Design of AI-Based Schedulers - Workshop Report , 1991, AI Mag..

[31]  J. Christopher Beck,et al.  This Is a Publication of The American Association for Artificial Intelligence , 2022 .