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Multi-objective dynamic distributed flexible job shop scheduling problem considering uncertain processing time
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AI
DOI：10.1007/s10586-024-04803-x.png)
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Abstract
En 中文
In this paper, a dynamic distributed flexible job-shop scheduling problem considering uncertain processing time of operation (DDFJSPT) is proposed for the first time. A two-stage efficient memetic algorithm (EMA) is presented to solve the DDFJSPT aiming at minimizing the maximum processing time and maximum energy consumption. In the EMA, a new initialization method is designed to balance the load of the initial population, and some efficient crossover and mutation operators and an effective local search operator are proposed to expand the solution space and improve the solution diversity. In addition, three different rescheduling strategies are designed to obtain high quality solutions under different types of disturbances on the processing time of operations. Through a large number of experiments, the superiority of the proposed EMA is verified by comparing its experimental results with the ones of other three well-known algorithms.
Keywords:
Dynamic distributed flexible job-shop scheduling problem
Uncertain processing time
Dynamic rescheduling
Memetic algorithm
Multi-objective optimization
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K