Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops

2014 ◽  
Vol 52 (12) ◽  
pp. 3688-3709 ◽  
Author(s):  
Gabriel Zambrano Rey ◽  
Abdelghani Bekrar ◽  
Vittaldas Prabhu ◽  
Damien Trentesaux
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.


2009 ◽  
Vol 16-19 ◽  
pp. 743-747
Author(s):  
Yu Wu ◽  
Xin Cun Zhuang ◽  
Cong Xin Li

Solve the flexible dynamic scheduling problem by using “dynamic management & static scheduling” method. Aim at the property of flexible Manufacturing systems, the dynamic scheduling methods are analyzed and a coding method based on working procedure is improved in this paper. Thus it can be efficiently solve the problem of multiple working routes selection under the active distribution principle. On the other hand, the self-adaptive gene is provided and the parameters of the genetic algorithm are defined. In such a solution, the scheduling is confirmed to be simple and efficient.


2018 ◽  
Vol 22 (S6) ◽  
pp. 15439-15446
Author(s):  
Xiaoqiang Wang ◽  
Ren Qing-dao-er-ji

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