Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm

  • Authors:

  • Hao Luo;George Q. Huang;Yingfeng Zhang;Qingyun Dai;Xin Chen

  • Affiliations:

  • Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong and The State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University ...;School of Information Engineering, Guangdong University of Technology, Guangzhou, China;School of Mechatronic Engineering, Guangdong University of Technology, Guangzhou, China

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2009

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Abstract

This research investigates a two-stage hybrid flowshop scheduling problem in a metal-working company. The first stage consists of multiple parallel machines and the second stage has only one machine. Four characteristics of the company have substantiated the complexity of the problem. First, all machines in stage one are able to process multiple jobs simultaneously but the jobs must be sequentially set up one after another. Second, the setup time of each job is separated from its processing time and depends upon its preceding job. Third, a blocking environment exists between two stages with no intermediate buffer storage. Finally, machines are not continuously available due to the preventive maintenance and machine breakdown. Two types of machine unavailability, namely, deterministic case and stochastic case, are identified in this problem. The former occurs on stage-two machine with the start time and the end time known in advance. The latter occurs on one of the parallel machine in stage one and a real-time rescheduling will be triggered. Minimizing the makespan is considered as the objective to develop the optimal scheduling algorithm. A genetic algorithm is used to obtain a near-optimal solution. The computational results with actual data are favorable and superior over the results from existing manual schedules.