Bilevel interactive optimisation for rebatching scheduling problem with selectivity banks in high variety flow line production

Abstract

Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.

KEYWORDS:

• Manufacturing
• rebatching problem in manufacturing
• selectivity banks in manufacturing
• bilevel interactive optimisation
• bilevel algorithm

Disclosure statement

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

Additional information

Notes on contributors

Wenchong Chen

Wenchong Chen is an Associate Professor at the College of Management, Hangzhou Dianzi University, Hangzhou, China. He received the BE degree and the MS degree in Logistics Engineering from Nanjing Agricultural University, Nanjing, China, and obtained his PhD degree in Management Science and Engineering from Tianjin University, Tianjin, China. His research interests include complex manufacturing system modelling, analysis, and optimisation.

Xuejian Gong

Xuejian Gong is a PhD candidate at the School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, USA. He received the MS degree in Mechanical Engineering from Georgia Institute of Technology. He obtained BE degree in Mechanical Engineering, Manufacturing and Automation from Harbin Engineering University, Harbin, China. His research interests include open design and manufacturing, engineering optimisation, as well as complex system modelling, simulation, and analysis.

Fangyu Liu

Fangyu Liu is a master student at the Department of Hospitality and Tourism Management, University of North Texas, Denton, USA. She received the BE degree in Tourism Management from Xian University of Science & Technology, Xian, China. Her research interests are complex system modelling and simulation.

Hongwei Liu

Hongwei Liu is an Associate Professor at the College of Management and Economics, Tianjin University, Tianjin, China. He received the BE degree in Mechanical Engineering and the MS degree in Management Science and Engineering from Yanshan University, Qinhuangdao, China. He obtained his PhD degree in Management Science and Engineering from Tianjin University, Tianjin, China. His research interests are smart manufacturing and adaptive production planning and scheduling.

Roger J. Jiao

Roger J. Jiao is with Faculty of Mechanical Engineering at Georgia Institute of Technology, Atlanta, Georgia, USA. Prior to Georgia Tech, He was with Faculty of Mechanical and Production Engineering at Nanyang Technological University in Singapore. He received his PhD in Industrial Engineering from Hong Kong University of Science and Technology, MEng in Mechanical Engineering from Tianjin University in China and BEng in Mechanical Engineering from Tianjin University of Science and Technology in China. More info about his research: https://scholar.google.com/citations?user=9yikEHAAAAAJ&hl.