The application of nature-inspired algorithms and priority hybrid dispatching rules for simultaneous scheduling and dispatching of automatic guided vehicles (AGVs) are observed to be highly significant for the best utilization of the flexible manufacturing system (FMS) facility. The earlier studies on the use of AGVs in the FMS have generally focused on minimizing the complexity of AGV operations by optimizing their material handling schedule and their routing in different types of FMS configurations. However, this is achieved only by using an appropriate optimizing algorithm and dispatching rule under different FMS operating conditions. The aim of this paper is to use the simulation methodology so as to compare and analyze the combined effect of four experimental factors, namely four types of priority hybrid dispatching rules, three different nature-inspired algorithms, two levels of loading/unloading times and two levels of machine failures, on the different performance parameters of the FMS. The performance parameters of the FMS analyzed are simultaneous minimization in distance travel and backtracking of AGV, the total production rate of the FMS, mean AGV utilization and mean work center utilization in the FMS. Additionally, in order to find the mean and interaction effect of the experimental factors on the aforementioned performance parameters of the FMS, an analysis of variance (ANOVA) is also carried out. From the results, it is observed that the interaction between experimental factors, namely nature-inspired algorithms and loading-unloading time, has a significant effect on the performance measures, namely simultaneous reduction in distance travel and backtracking, mean work center utilization (%) and total production rate of the FMS. The interaction between aforesaid experimental factors has no significant effect on mean AGV utilization (%) in the FMS. However, the interaction between priority hybrid dispatching rules and loading/unloading times is found to have significant effect on the mean work center utilization (%) in the FMS facility.
Order review and release in job shop with sequence-dependent setup times is one of the most complex job shop scheduling problems. This article analyses the effect of sequencing rules on different order release policies for performance measures i.e. mean throughput time, total setups, mean setup time, number of tardy jobs and makespan in a stochastic and dynamic job shop with sequence-dependent setup time. Four sequencing rules, viz. first-come-first-serve, planned release date, earliest due date, shortest processing time and five order review and release policies i.e. aggregate workload trigger, workcentre workload trigger, corrected workload trigger, upper bound release and lancaster university management school corrected order release are taken into consideration. A simulation model using Promodel® is developed for experimental purpose. Results indicate that for a given order review and release policy, there is an effect of sequencing rule in a stochastic and dynamic environment with consideration of sequence-dependent setup time and the best performing sequencing rule is different for different order review and release policies for a given performance measure.
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