Multi-Dimensional Manufacturing Resource Collaborative Optimization Scheduling Technology for Workstations and Workers

In order to address the multi-dimensional collaborative optimization scheduling problem of manufacturing resources for workstations and workers, this paper proposes a multi-dimensional manufacturing resource collaborative optimization scheduling technology for workstations and workers. By constructing mathematical models for orders, equipment, and workers, the objectives are to minimize the maximum completion time of all orders and minimize the total delay of all orders, while considering complex constraints such as process sequence constraints, worker skill levels, and equipment allocation. To solve the issues of local optimization and slow convergence speed in traditional particle swarm optimization (PSO), an improved adaptive particle swarm optimization is proposed. This method adopts a dynamic probability inheritance mechanism, inertia weight adjustment, process crossover operators, and load balancing mechanisms, significantly enhancing the algorithm's global search capability and local convergence efficiency. Experimental results show that the improved adaptive particle swarm optimization algorithm outperforms the standard PSO, genetic algorithm, and hybrid algorithms of PSO and genetic algorithm in multiple test cases. In particular, it demonstrates the effectiveness and feasibility of the model and algorithm in minimizing the maximum completion time and total delay.