The shifting bottleneck procedure for job shop scheduling
Management Science
Integration of simulation modeling and inductive learning in an adaptive decision support system
Decision Support Systems - Special issue on model management systems
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Reference architecture for holonic manufacturing systems: PROSA
Computers in Industry - Special issue on manufacturing systems
Fuzzy rule base generation for classification and its minimization via modified threshold accepting
Fuzzy Sets and Systems - Special issue on clustering and learning
Adaptive Search and the Management of Logistics Systems: Base Models for Learning Agents
Adaptive Search and the Management of Logistics Systems: Base Models for Learning Agents
Introduction to Multiagent Systems
Introduction to Multiagent Systems
A distributed shifting bottleneck heuristic for complex job shops
Computers and Industrial Engineering
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In this paper, we describe adaptation techniques for a hierarchically organized multi-agent-system (MAS) applied to production control of complex job shops. The system architecture of the production control system is based on three different control layers. The mid layer implements a distributed shifting bottleneck type solution procedure. The shifting bottleneck heuristic decomposes the overall scheduling problem into scheduling problems for parallel machines. The sequence of solving the resulting scheduling problems for parallel machines is determined by machine criticality measures. We can adapt this solution scheme in a situation dependent manner by choosing appropriate machine criticality measures. Furthermore, the performance of the shifting bottleneck scheme is also influenced by the selection of a proper subproblem solution procedure for each of the parallel machine scheduling problems. The subproblem solution procedures typically are given by heuristics. A situation dependent parameterization of these heuristics is highly desirable. In this paper, we sketch an overall concept for adaptation of our hierarchically organized multi-agent-system. We present results of computational experiments based on the simulation of a dynamic environment for the appropriate selection of machine criticality measures.