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Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate an optimized MCSC that can meet the diverse requirements of customers. However, many available MCSs, deployed in the CMfg platform, have the same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with a high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm (WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, adaptive crossover inspired by the genetic algorithm is developed to balance exploration and exploitation. The Lévy flight is designed to expand the search space of the WOA and accelerate the convergence of the WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on various benchmark functions and different scale QoS-MCSC problems. The QoS attributes of the problems are randomly and symmetrically generated. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms. [ABSTRACT FROM AUTHOR]

It is of more practical significance to carry out research on the integrated process planning and scheduling/rescheduling (IPPSR) to achieve a global improvement for the performance of a manufacturing system with sustained pursuit and various uncertainties. In this paper, a framework and a unified dynamic rescheduling model for IPPSR with three typical types of situations normally encountered in a production system, i.e. arrival of new jobs, machine breakdown and order cancellation have been constructed. Meanwhile, an improved evolutionary algorithm (IEA) for the integrated process planning and scheduling (IPPS) to generate an optimal initial scheduling plan has been developed. In order to improve the performance of the algorithm for IPPS and facilitate the extension of it to rescheduling situation, new genetic representation for the scheduling plan combined with process plans and corresponding genetic operators are developed. Based on the dynamic rescheduling model and initial optimised result, the integrated rescheduling strategies for the above three types of situations have been developed. In order to verify the feasibility and performance of the proposed strategies, experimental studies were conducted and comparisons were made for different performance measures; the results show that the proposed approaches not only provide more effective result for IPPS but also for integrated rescheduling with the above three types of uncertainties. [ABSTRACT FROM PUBLISHER]