scholarly journals Energy-Oriented Scheduling for Hybrid Flow Shop With Limited Buffers Through Efficient Multi-Objective Optimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34477-34487 ◽  
Author(s):  
Sheng-Long Jiang ◽  
Long Zhang
2014 ◽  
Vol 1082 ◽  
pp. 529-534
Author(s):  
Zheng Ying Lin ◽  
Wei Zhang

Due to several mutual conflicting optimized objectives in the hybrid flow shop scheduling problem, its optimized model, including three objectives of make-span, flow-time and tardiness, was firstly set up, instead of the single optimized objective. Furthermore, in order to improve the optimized efficiency and parallelism, after comparing the normal multi-objective optimized methods, an improved NSGA-II algorithm with external archive strategy was proposed. Finally, taking a piston production line as example, its performance was tested. The result showed that the multi-objective optimization of hybrid flow shop scheduling based on improved NSGA-II provided managers with a set of feasible solutions for selection in accordance to their own preference. Therefore the decision could be made more scientific and efficient, and thus brings to the factory more economic benefits.


Author(s):  
Binghai Zhou ◽  
Wenlong Liu

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.


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