Masters' Theses A Study on FMS Scheduling Problems Based on Priority Functions Generated by Genetic Programming
2015.07.29 12:18
| year | 1997 |
|---|---|
| author | ChangUk Kim |
| Keyword | FMS, Scheduling, Priority Functions, Genetic programming, Simulation |
| Abstract | Most of the past studies on FMS scheduling problems may be classified into two classes, namely off-line scheduling and on-line scheduling approach. The off-line scheduling methods are used mostly for FMS planning purposes and may not be useful for real time control of FMSs, because it generates solutions only after a relatively long period of time. The on-line scheduling methods are used extensively for dynamic real-time control of FMSs although the performance of on-line scheduling algorithms tends to vary dramatically depending on various configurations of FMS. Current study is about finding a better on-line scheduling rules for FMS operations. In this study, we propose a method to create priority functions that can be used in setting relative priorities among jobs or machines in on-line scheduling. The priotity functions reflect the configuration of FMS and the user-defined objective functions. The priority functions are generated from diverse dispatching rules which may be considered a special priority functions by themselves, and are used to determine the order of processing and transporting parts. Overall system of our work consists of two modules, the Priority Function Evolution Module (PFEM) and the FMS Simulation Module (FMSSM). The PFEM generates new priority functions using input variables from a terminal set and primitive functions from a function set by genetic programming. And the FMSSM evaluates each priority function by a simulation methodology. Based on these evaluated values, the PFEM creates new priority functions by using crossover, mutation operation and probabilistic selection. These processes are iteratively applied until the termination criteria are satisfied. We considered various configurations and objective functions of FMSs in our study, and we seek a workable solution rather than an optimum or near optimum solution in scheduling FMS operations in real time. To verify the viability of our approach, experimental results of our model on real FMS are included. |
| c | MS |
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