EngineeringIndustrial EngineeringManufacturing EngineeringProduction EngineeringProduction PlanningProduction SystemsGenetic AlgorithmsManufacturing Resource PlanningMulti-objective SchedulingProduction OptimizationProduction Planning SystemsProduction SchedulingGenetic Algorithm OptimizationLot Size AnalysisManufacturing Resource SchedulingMulti-product ProductionProduction Resource PlanningProduction Scheduling OptimizationScheduling Problems
Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.