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Sabuncuoglu and Hommertzheim [67,68] studied the prob- lems of scheduling machines and AGVs using an FMS simul- ation model in a single criterion environment. The influences of machine and AGV scheduling rules on the mean flow-time criterion were investigated. Machine scheduling rules were SPT.TOT, SPT/TOT, LPT.TOT, LPT/TOT, LWKR, MWKR,
FOPR, MOPR, FCFS, FAFS, and RANDOM. AGV scheduling rules were FCFS, LOQS, STD, LQS, MWKR, and FOPR. The FMS consisted of six machine centres with limited input buffers, one inspection station, one washing centre, one loading, and one unloading station. Two AGVs were employed to transport parts through the system. The FMS was modelled
using the SIMAN discrete simulation language and animated in CINEMA. The scheduling rules were tested with three factors, i.e. different utilisation levels, different queue capacities, and different AGV speeds. The results indicated that scheduling AGVs was as important as scheduling machines. The due-date of parts was not considered in the simulation model, and consequently no due-date-based rule or measure of performance was employed. Sabuncuoglu [69] then extended the studies under new experimental conditions. The same FMS was employed, but the objective was to measure the sensitivity of the rules to changes in processing time distributions, various levels of breakdown rates, and types of AGV priority scheme. Although similar results were obtained to those of the previous work [67,68], Sabuncuoglu concluded that scheduling of material handling systems is as important as the machining subsystem.
O’Keefe and Kasirajan [4] investigated the interaction between nine dispatching rules and four next station selection rules in a relatively large dedicated FMS with a simulation model using the RENSAM package. The FMS was modelled with constant operation times and no machine breakdowns or AGV failures. The model contained 16 workstations with local buffers, nine load/unload stations, three AGVs, and six part types. Dispatching rules used in the model were FIFO, SIO, LIO, FRO, MRO, SIO/TOT, LIO/TOT, SLACK, and SIOx.
Next station selection rules consisted of NS, WINQ, NINQ, and LUS. The only performance measure was weighted flow- time. The smallest value of weighted flow-time they found was related to SIO/TOT combined with WINQ. The best next station selection rule was WINQ and the worst was LUS. The main shortcoming of this study was its single criterion environment. Although due-date had been considered and two due-date-based rules were applied, no due-date-based perform- ance was measured.
Rohleder and Scudder [70] made a simulation model, with only one decision point, to investigate the influence of ten scheduling rules on the net present value (NPV) in a JIT production system. Scheduling rules were OPCR, ODD, OPSLK, CR, EDD, MOD, TSLK, MDD, SPT, and LWKR.
Performance measures were mean system inventory, NPV, mean tardiness, percentage of tardy jobs, average starting time of operations, number of jobs in process (WIP), number of jobs finished but not shipped (NFGS), and average total jobs in system (WIP NFGS). Due-date of jobs was assigned using TWK. The model was run with three due-date tightness (K 3, 6, and 9). The results showed that different scheduling rules had different influences on performance measures. For example, job-based allowance rules dominated the mean system inven- tory.
Rachamadugu et al. [71] studied the influence of sequencing flexibility on the performance of rules used, to schedule oper- ations in manufacturing systems, using a simulation model consisting of ten machines. The performance of ten scheduling rules was examined including FIQ, FIS, SPT, EDD, MDD, CR, EODD, MODD, OCR, and MSUC. The performance measures were mean flow-time, average tardiness, and pro- portion of tardy jobs. The results showed that the performances of all rules were improved, while levels of sequencing flexi- bility were increased. It was demonstrated that the performance