Population-based neighborhood search for job shop scheduling with interval processing time

2011 ◽  
Vol 61 (4) ◽  
pp. 1200-1208 ◽  
Author(s):  
Deming Lei
2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840112 ◽  
Author(s):  
Xiaoxing Zhang ◽  
Zhicheng Ji ◽  
Yan Wang

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.


Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane

AbstractIn job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.


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