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Abstract
Smart manufacturing in the “Industry 4.0” strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the realtime status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the realtime scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.
Highlights
With the introduction of the concept of Industry 4.0, there has recently been an emphasis on advancing manufacturing technologies in developed and developing countries[1]
The real-time hybrid flow shop scheduling problem (HFSP) under a smart manufacturing environment can be described as follows[9]: New orders successively arrive at the shop floor with various kinds of information, such as arrival time, due time, and processing time at different stages of jobs
The scheduling rule that should be used to select a proper job to optimize the scheduling performance is the primary concern of real-time scheduling
Summary
With the introduction of the concept of Industry 4.0, there has recently been an emphasis on advancing manufacturing technologies in developed and developing countries[1]. Reference [24] developed a priority-based hybrid parallel genetic algorithm with a predictive reactive complete rescheduling strategy for an energy-efficient dynamic flexible flow shop scheduling problem. Reference [25] proposed an improved particle swarm optimization method to address the dynamic flexible flow shop scheduling problem considering new job arrivals and machine breakdowns. A perusal of the existing literature concludes that most of the existing publications focused on the traditional shop floor environment, in which the real-time data of the production status cannot be used to aid scheduling decision making. This paper proposes a real-time scheduling approach to automatically constructing efficient scheduling rules in real time for HFSP in a smart manufacturing environment. (2) A real-time scheduling approach for different types of shop floors under a smart manufacturing environment is developed. Xiuli Wu et al.: Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment
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