International Journal of Production Research · 2017 · 13 citations · 58 references
Load Balancing (Computing)EngineeringProduction SystemsDynamic Resource AllocationIndustrial EngineeringEnergy EfficiencySingle Optimisation RunsOperations ResearchSystems EngineeringParallel ComputingProcess OptimizationUnbalanced WorkloadCapacity ManagementComputer EngineeringCapacity PlanningEnergy ManagementMultiple-objective OptimisationProduction SchedulingBusinessScheduling (Production Processes)Resource AllocationMulti-objective Optimisation
Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these studies: (1) only some predetermined degree of imbalance and patterns of imbalance have been evaluated against the perfectly balanced configuration, making it hard to form a general conclusion on these factors; (2) only a single objective has been set as the target, which neglects the fact that different patterns of imbalance may outperform with respect to different performance measures. Therefore, the aim of this study is to introduce a new approach to investigate the performance of unpaced production lines by using multiple-objective optimisation. It has been found by equipping multi-objective optimisation with an efficient, equality constraints handling technique, both the optimal pattern and degree of imbalance, as well as the optimal relationship among these factors and the performance measures of a production system can be sought and analysed with some single optimisation runs. The results have illustrated that some very interesting relationships among the key performance measures studied, including system throughput, work-in-process and average buffer level, could only be observed within a truly multi-objective optimisation context. While these results may not be generalised to apply to any production lines, the genericity of the proposed simulation-based approach is believed to be applicable to study any real-world, complex production lines.
58
Kalyanmoy Deb, Himanshu Jain · IEEE Transactions on Evolutionary Computation · 2013 · 6.4K citations
Simulation modeling and analysis
Journal of Manufacturing Systems · 1999 · 3.8K citations
Engineering, Process Simulation Model, Systems Engineering +6