Capacity planning and production scheduling integration: improving operational efficiency via detailed modelling

Abstract

Successful capacity planning and production scheduling is built on the understanding of market opportunities and the costs of capacity, production, sourcing, inventory, and distribution over the planning horizon. Increasingly, companies attempt to integrate capacity planning and production scheduling to improve upon the commonly used sequential decision process, but most related research works fail to capture the granularity of actual operational decisions and therefore may overlook potential cost-saving opportunities. The contributions of this study include: (1) a detailed integrated capacity and production scheduling model with multiple discrete and continuous options for varying short and medium-term capacity, (2) a heuristic algorithm that exploits the problem structure to solve the nonlinear mixed integer problem, (3) an evaluation of the value of the integrated model relative to traditional practice and its sensitivity to parameters, (4) a review of past contributions to integrated planning, particularly focused on IJPR, and (5) a case study originated from a world-class automobile manufacturer illustrating how the model can be applied and confirming its value relative to hierarchical and less detailed modelling approaches.

Keywords:

• Production modelling
• capacity planning
• integration
• scheduling
• math programming

Disclosure statement

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

Data availability statement

The data that support this research come from a commercial source, where access is not granted to the public.

Additional information

Funding

This research was supported in part by grant GAC2408 from General Motors Holdings LLC.

Notes on contributors

Xufeng Yao

Dr. Xufeng Yao is a researcher of Chief Data and Analytics Office at General Motors. He received his BS, MS and Ph.D. in Industrial Engineering from Zhejiang University of Technology, Shanghai Jiaotong University and Arizona State University, respectively. His research interests include production system modelling and optimisation on the strategic and tactical levels, and future mobility and transportation system design and optimisation.

Nourah Almatooq

Dr. Nourah Almatooq is currently an Assistance Professor at the College of Engineering and Petroleum at Kuwait University. She obtained her Ph.D. in Industrial Engineering from Arizona State University in 2020. Her area of interest is Production Systems and Logistics. Dr. Nourah presented research-based papers in INFORMS 2018 conference. Her research activities are currently focused on Pricing Model for the effect of price elasticity of demand on the operational decisions of supply chains. She is also working on schedule generation problem via a hybrid algorithm combining column generation and branch and bound.

Ronald G. Askin

Dr. Ronald G. Askin is a Professor of Industrial Engineering at Arizona State University where he also served as Director of the School of Computing, Informatics, and Decision Systems Engineering. Prior to joining ASU he was a Professor and Department Head of Systems and Industrial Engineering at The University of Arizona. Dr. Askin received a BS in Industrial Engineering from Lehigh University and an MS in Operations Research and Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology. A Fellow of IISE and INFORMS, he has served on the IISE Board of Trustees, as President of the IISE Council of Fellows and Chair of the Council of Industrial Engineering Academic Department Heads. He has also served as Editor-in-Chief of IISE Transactions and INFORMS V.P. for Meetings. His research focuses on the application of operations research to the design and operation of production systems. He has received multiple Best Paper Awards, the IIE Joint Publishers Book-of-the Year Award, the Shingo Prize for Excellence in Manufacturing Research, an NSF Presidential Young Investigator award and the IISE Albert G. Holzman Distinguished Educator Award.

Greg Gruber

Greg Gruber is currently Principal Analytics Manager for Business Analytics at CAT Digital, the digital technology arm of Caterpillar. Greg's organisation focuses on predictive and prescriptive modelling to improve enterprise profitability. Previously, Greg spent over 25 years at General Motors, where he led a supply chain Operations Research team, and led Operations Research and Analytics projects in Research and Development, Supply Chain, Product Development, and Manufacturing. Greg has a MS in Operations Research. His research interests are supply chain planning under uncertainty, and supply chain complexity.