An efficient adaptive genetic algorithm for energy saving in the hybrid flow shop scheduling with batch production at last stage. Issue 2 (15th February 2021)

• Record Type: Journal Article
• Authors: Lu, Hong; Qiao, Fei
• Other Names: Wu Desheng Dash guestEditor.; Hall Jon guestEditor.; Belezamo Baloka guestEditor.; Eken Süleyman guestEditor.; Avci Cafer guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-15
• Subjects: adaptive genetic algorithm -- batch production -- energy consumption -- hybrid flow shop scheduling; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12678
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: This article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy‐intensive steelmaking‐continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic algorithm (TGA), the calculation of the fitness function as well as adaptive crossover and mutation are designed. Due to the complexity of the problem in this article, we then propose an efficient adaptive genetic algorithm (EAGA) to improve the search ability of TGA. The EAGA has new features including layered strategies and enhanced adaptive adjustment method. To evaluate the proposed model and algorithm, we conduct computational experiments under practical background and compare the EAGA with the several algorithms presented previously. The results illustrate that scheduling with our model can greatly reduce the extra energy consumption. Meanwhile, the proposed EAGA is very efficient in comparison.