An optimization algorithm for integrated remanufacturing production planning and scheduling system

It is known that production planning and scheduling are mutual influence and restriction. In this paper, we aim to obtain the minimum remanufacturing time of recycling parts by use of birandom variables and further optimize an integrated remanufacturing production planning and scheduling system under uncertain conditions. An integrated production planning and scheduling optimization model with birandom variable restraints is firstly established. Then we develop a hybrid intelligent algorithm including random simulation technique, neural network, and genetic algorithms to optimize an integrated remanufacturing production planning and scheduling system. Furthermore, we generate a random variable samples matrix through random simulation technique and a trained neural network is embedded into genetic algorithm. Finally, this hybrid intelligent algorithm is applied to optimize an integrated remanufacturing production planning and scheduling system through a case study.