Neural Combinatorial Optimization for Production Scheduling with Sequence-Dependent Setup Waste

One of the main objectives of production planning is to minimize the usage of resources and manufacturing-related costs while meeting the customer’s requirements, such as delivery dates and quality. Production planners deal with various scheduling problems that are often NP-hard and can not be optimally solved by humans. Solving such problems often relies on methods from the Operations Research (OR) field. Recently, Neural Combinatorial Optimization (NCO) has emerged as a promising field of research that aims at tackling different optimization tasks using the latest advancements in machine learning, including deep reinforcement learning. These methods can be successfully used for short-term production planning because of their flexibility and speed. In this paper, we examine the applicability and scalability of neural combinatorial optimization methods in the context of production planning. We define an evaluation metric to investigate the stability and quality of the solutions. Furthermore, we develop an experimental setup allowing to compare various approaches for production scheduling with sequence-dependent setup costs under real-world production conditions. Although an optimality gap is observed when compared to established OR methods, our experiments demonstrate the superiority of NCO in terms of scheduling time.