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

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, pp. 2640–2647

Abstract

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.

Authors 5

1. Aymen Gannouni Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

2. Владимир Самсонов Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

3. Mohamed Behery Aachen Knowledge-Based Systems Group

   RWTH Aachen University

   Affiliation as printed

   Knowledge-Based Systems Group, RWTH Aachen University, Aachen, Germany

4. Tobias Meisen

   University of Wuppertal

   Affiliation as printed

   University of Wuppertal, Wuppertal, Germany

5. Gerhard Lakemeyer Aachen Knowledge-Based Systems Group

   RWTH Aachen University

   Affiliation as printed

   Knowledge-Based Systems Group, RWTH Aachen University, Aachen, Germany