Recall cost-time tradeoffs for remanufacturing shop lot streaming scheduling problem with non mixed production using an improved non-dominated sorting genetic algorithm

In this paper, we study the problem of lot streaming scheduling in a remanufacturing shop with consistent sublots, where mixed production is not allowed between sublots possessing different types of remanufacturable parts. The problem is formulated as a multi-objective optimization problem with optimization objectives of recall cost and completion time. Such problems are NP-hard and need to be solved using an improved non-dominated sorting genetic algorithm. Two vectors regarding sublot size allocation and sublot processing order determination together form a solution. In order to improve the quality of the solution, the algorithm uses a randomization strategy and two heuristics to initialize the population and introduces dynamic genetic operations to advance the population diversity. On the one hand, the designed four types of genetic operators are dynamically selected according to the number of iterations. On the other hand, the elite retention strategy is improved, i.e., based on the probability that one of the individuals performing the crossover operation can come from the memory bank. Both numerical experiments and real case solving verify the effectiveness of the developed algorithms.