FMS performance under balancing machine workload and minimizing part movement rules

A. Pereira
2011 International Journal of Simulation Modelling  
A simulation model is developed to evaluate the performance of a flexible manufacturing system with respect to time in system. The new rule to assign parts to machine-tools we propose, OOM (Only One Machine), designed to minimize parts movements performs poorer than WINQ (Work In Queue), a rule directed at balancing machine workload. Different numbers of automated guided vehicles (AGV) produce significantly different results with the best performance resulting with five AGVs. Three AGVs are too
more » ... Three AGVs are too few to handle the transportation requirements, whereas seven may, to some extent, increase AGV blockage. The number of parts that can be entirely processed on one single machine is found to impact performance, but the impact is not consistent across the experimental conditions. Three rules to sequence parts to be processed are found to have a moderate impact when OOM assignment is employed, but have no impact under the WINQ assignment rule. Pereira: FMS Performance under Balancing Machine Workload and Minimizing Part ... parts that can be entirely processed on one single machine, machine commonality (M), is set at two levels: seven parts out of a total of ten can be entirely processed on one single machine (high level), and three out of ten parts (low level). The performance will then be evaluated across thirty-six different configurations. LITERATURE REVIEW Over the past decades, considerable research effort has been devoted to the domain of manufacturing systems scheduling. In recent years however, attention has been directed towards planning and scheduling problems concerning FMSs [3] . FMS management requires the optimization of several components that can be classified into two groups: design or structural problems and operational problems [4] [5] [6] [7] [8] . The first group is concerned with the optimal selection of all the FMS components involving strategic decisions concerning the FMS hardware to meet the user goals and requirements; the second group deals with tactical and control decisions problems such as process planning, machine grouping, part type selection, resource allocation and loading [4] . Shang and Sueyoshi [9] developed a structured framework to support management's deliberation on issues of FMS design and planning specifically intended to assist managers in selecting the most appropriate FMS design. Caprihan and Wadhwa [3] simulated a hypothetical semi-automated FMS with six flexible machines to measure performance with respect to makespan, the time required to completely process all required parts of all types. Using a Taguchi experimental design with 5 factors (routing flexibility, dispatching rule, sequencing rule, number of pallets, and information delay) each set at 5 levels, they show information delay and number of pallets are significant in terms of their effect on makespan. Routing flexibility, dispatching rule and sequencing rule appear to be relatively less significant. Reddy and Rao [10] developed a hybrid genetic algorithm to address the combined machine and vehicle-scheduling problem to generate an optimum machine and vehicle schedule that is best with respect to the makespan, mean flow time and mean tardiness. Iwata et al. [11] provided a heuristic approach using a decision rule to obtain a good feasible solution for a practical large-scale problem. Three proposed decision rules were investigated through a case study with respect to makespan, mean flow time, mean utilization of machine tools, and mean utilization of transport devices. Shanker and Tzen [12] simulated a FMS to compare the solutions of loading given by mixed integer programming and heuristics methods with respect to CPU time, unbalance and number of tool slots used, and to investigate the effect of loading policies in conjunction with four dispatching rules on system performance as measured by machine utilization. Ozden [13] conducted a simulation study of multiple-load-carrying AGV in a FMS whose performance was measured with respect to throughput, average utilization of the machining station, and average utilization of the AGVs. Kuzgunkaya and ElMaraghy [14] present a fuzzy multi-objective mixed integer optimization model to evaluate reconfigurable manufacturing system investments used in a multiple product demand environment and compare the performance of flexible and reconfigurable systems. Singh [15] addresses the multi criteria dynamic scheduling problem with machine breakdowns in a flexible job shop.
doi:10.2507/ijsimm10(2)4.182 fatcat:b6vxyunbazcitlqwlpwg6fyj34