A novel data-driven approach to support decision-making during production scale-up of assembly systems |
| |
| Affiliation: | 1. Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK;2. School of Engineering, London South Bank University, London SE1 0AA, UK;1. Shenzhen Research Institute, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, Hunan 412001, China;3. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden;1. Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States;2. Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060, United States;1. Department of Mechanical Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109, United States;2. Department of Aerospace Engineering, University of Michigan, 1320 Beal Ave, Ann Arbor, MI, 48109, United States;1. Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;2. State Key Lab of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China;1. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, China;2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, China;3. Andalusian Research Institute DaSCI “Data Science and Computational Intelligence”, University of Granada, 18071, Granada, Spain;4. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia |
| |
| Abstract: | In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort. |
| |
| Keywords: | Manufacturing systems Production planning Scale-up Demand amplification Demand uncertainty Data-driven method Discrete-Event Simulation DES Multi-objective optimisation Evolutionary optimisation algorithm Genetic Algorithm GA Kinematic modelling |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
