A novel method for customer-oriented scheduling with available manufacturing time windows in Cloud Manufacturing. (June 2022) - Tong, Huagang; Zhu, Jianjun
Highlights: Considering the importance of nonrational customers, we use the prospect theory to calculate the customer satisfaction based on the tardiness and earliness. To fully use the manufacturing capacity, we study the manufacturing services with several available manufacturing windows. We study the adjustable speed of manufacturing services. For the multi-objectives scheduling model, we design an improved artificial bee colony algorithm. A new Adaptive Variable Neighborhood Search mechanism based the solver is designed. Abstract: Cloud Manufacturing is customer-oriented manufacturing, customers' satisfaction is important for the platform. To enhance the satisfaction of customers, previous studies aimed at maximizing the Quality of Services, but the timeliness which means delivering the products to customers on time has not been paid enough attention. For timeliness, because the customer is not rational, the satisfaction is not linear with tardiness and earliness, we use the prospect theory, a famous theory of psychology, to calculate the satisfaction based on tardiness and earliness in scheduling, not minimize the total tardiness and earliness like previous studies. Then, the manufacturing services may only provide several available manufacturing time windows to the platform, because part of the time was occupied by the previous customers or the manufacturing services' own production before. To utilize the potential manufacturing capacity, we study the manufacturing services with time windows. Also, the adjusted speed of the manufacturing services is studied. Finally, to solve the scheduling problem, we proposed an improved hybrid artificial bee colony algorithm. For our proposed algorithm, to guide the evolution of the algorithm on the stage of employer bee, we design a new selecting mechanism based on the grey wolf algorithm. On the onlooker bee, we regard the solver as part of the algorithm, the solver is used to find new effective scheduling sequences. We also verify the algorithm's effectiveness through numerical experiments.