Worker Scheduling Based on Elite Non-Dominant Sorting Whale Optimization Algorithm (Ens-Woa) in C2m Model

With the continuous upgrading of industrial technology and information technology, consumers can deeply participate in the whole life cycle of products and realize customized production. These unprecedented changes have brought consumers and manufacturers closer together, resulting in the intelligent business model of "Internet + Customized Production" and "Customer to Manufacturer (C2M)". C2M has been adopted by more and more companies. However, the transition from traditional business models to C2M is a problem that every company must face and solve. Personalized orders of many varieties and small lots put enormous pressure on the production of mainly labor-intensive electronic assembly companies. The theoretical findings of Industry 4.0 and Lean Manufacturing show that people play a central role in assembly operations. As an important element of the production system, worker scheduling has a direct impact on delivery time and cost. Worker scheduling requires not only matching people to jobs, but also considering flexible employment. According to the "Learning Curve" theory, workers with learning potential can continuously enrich their skills and work efficiency will show dynamic changes. Therefore, under the condition of shortest order delivery time and lowest cost, worker scheduling considering the learning effect becomes a challenge for enterprise decision makers.Firstly, the production method of manufacturing industry in C2M environment is studied. Then, based on single-skill task and multi-skill task, respectively, a learning curve-based model of dynamic change in worker skill level is constructed. And this model is used as the input of the assembly line worker scheduling model. Second, an Elite Non-dominant Sorting Whale Optimization Algorithm (ENS-WOA) is designed for this multi-objective optimization problem. The correctness and feasibility of the proposed algorithm are verified by selecting classical arithmetic cases for experimental comparison with other algorithms. Finally, the established worker efficiency change model, worker scheduling model and the proposed algorithm are applied to optimize the assembly line of water pump products of Company B, which is being transformed to C2M, and solved by MATLAB software. The results show that the model proposed in this paper is effective, stable and practical compared with the number of workers, personnel cost and delivery period required to complete the order in the original assembly line, and outperforms the original Whale Optimization Algorithm (WOA) and NSGA-II algorithm in terms of convergence speed and quality of solution