Research on online EDI order scheduling optimization strategy in manufacturing enterprises based on time-varying Markov chains.

In the era of Industry 5.0 and artificial intelligence, with the continuous development of online EDI orders, manufacturing enterprises adopting a combined online and offline order acceptance model face the challenge of optimizing production scheduling for online EDI production lines. The primary difficulties stem from time-varying order demand and the unique service paradigm of online scheduling. To address this decision-making problem, this study first models the online EDI production line service system within the ERP framework as a resource-sharing queue. Time-varying Markov chains and the uniformization method are employed to model and analyze key performance indicators, including order sojourn time, queue length, and production line overtime. Subsequently, building upon this system evaluation methodology, a heuristic algorithm based on Variable Neighborhood Search (VNS) is proposed to solve the production line scheduling problem. Finally, numerical experiments are conducted using real production order data from a traditional manufacturing enterprise to validate the accuracy of the time-varying Markov chain modeling. The results demonstrate that the proposed algorithm yields scheduling solutions superior to the actual schedules generated by the factory's ERP system. This leads to more rational allocation of production line working hours, reduced order sojourn times, controlled order backlog within the system, and exhibits strong robustness. The research presented holds practical significance for enhancing the operational management of online EDI order systems in traditional industrial manufacturing enterprises. .