Högskolan i Skövde

Batching and scheduling orders for ovens in manufacturing is a typical combinatorial optimization problem, and it is critical for production efficiency and customer satisfaction. Customized settings and randomness happened in practical production including the maximum waiting time of an order, capacity thresholds, and availabilities of ovens make the problem more complex. In this paper, we build a simulation model in ten ovens for a heat-treatment process line of cutting tools according to real data from an industrial case, and the model embeds a detailed control logic that integrates existed scheduling methods with various dispatching rules and parameters. After determining an optimal number of running ovens in the production line by simulation-based optimization, we then propose a multi-objective optimization (MOO) enhanced deep reinforcement learning (DRL) approach to schedule orders for the ovens. The DRL agent learns to use an appropriate dispatching rule at a scheduling time point to select orders and formulate a batch. Along with reducing the average tardiness of the orders and maximizing the average effective utilization of the ovens, we find that the explored MOO enhanced DRL approach is more flexible and effective than the heuristic method.