An iterated local search for customer order scheduling in additive manufacturing

Abstract

This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis.

Keywords:

• Additive manufacturing
• customer order scheduling
• unrelated parallel machines
• batch processing machines
• iterated local search

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data that is used within the computational studies of this study are openly available at https://data.mendeley.com/datasets/vntcx6zfc7/2.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 Please see Section 3.1 and Pinedo (2012) for details on the used notation.

2 All test instances used are provided online via https://data.mendeley.com/datasets/vntcx6zfc7/2

3 In this example we refer to instance 2 of Configuration 1. The complete input data can be found in file Instance_o4_ipo5_m2_m2_pT30_ddr50_id2.json of the test data.

Additional information

Notes on contributors

Benedikt Zipfel

Benedikt Zipfel is a research assistant at the Chair of Industrial Management at Technische Universität Dresden, Germany, where he also received his Diploma in 2018. His research focuses on the design and application of exact and heuristic approaches for various optimisation problems in scheduling and packing. In his Ph.D. thesis, he especially aims for developing efficient planning tools for integrated problems of scheduling and packing in additive manufacturing environments.

Janis Neufeld

Dr. Janis S. Neufeld is a senior researcher at the Chair of Industrial Management and leader of the research group Operations Management at Technische Universität Dresden, Germany, where he also received his Ph.D. and habilitation. In 2020/2021 he served as an interim professor at the Chair of Management Science, Otto-von-Guericke-Universitt Magdeburg. His major research interests are the development and application of optimisation methods to complex planning problems in manufacturing and logistics. He has worked on several operations research projects with industry partners, covering topics such as railway crew scheduling, predictive maintenance, scheduling in cellular manufacturing, and lot sizing.

Udo Buscher

Professor Udo Buscher is full professor for Industrial Management at Technische Universität Dresden, Germany, where he teaches courses on operations management and operations research. He studied at the University of Göttingen, Germany, received a Ph.D. from Technische Universität Dresden, and finished his habilitation at the University of Würzburg, Germany. His research interests include production planning and control, supply chain management, and scheduling with a strong focus on operations research, decision science, applied algorithms, and game theory. He carried out a large number of projects with companies in which the application of OR methods plays a central role. His research results are published in leading international academic journals.