Simulation Optimization of Integrated Scheduling for Distributed Factory Collaborative Production and Transportation

In order to achieve balanced allocation of production capacity among multiple factories in a group and reduce operating costs, a multi-agent simulation optimization model with the goal of minimizing total cost and maximum completion time was established for the collaborative production and transportation integration scheduling problem of multiple factories in a distributed layout. Based on the actual production situation, an improved NSGA-II algorithm was proposed for solution. The algorithm is based on order splitting, allocation, and processing, and a three-stage encoding structure is designed. Through simulation decoding, it guides the action execution of each agent. Design a semi random initialization mechanism based on the characteristics of the problem to improve the performance of the initial solution, which includes three strategies: the number of sub orders after splitting, the size of sub orders, and factory allocation; And introduce a unique equal probability insertion and splitting strategy; At the same time, a multi-point crossover operation with an adjustment strategy is designed, taking into account the impact of sub order size on the target value, and two unique mutation methods are proposed. Finally, an experiment was conducted using a production scheduling example from a certain yeast group, and the results showed that the proposed improved algorithm is effective and superior in solving the proposed problem.