2020
DOI: 10.1007/s00500-020-05234-7
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Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

Abstract: This paper investigates permutation flow shop scheduling (PFSS) problems under the effects of position-dependent learning and linear deterioration. In a PFSS problem, there are n jobs and m machines in series. Jobs are separated into operations on m different machines in series, and jobs have to follow the same machine order with the same sequence. The PFSS problem under the effects of learning and deterioration is introduced with a mixed-integer nonlinear programming model. The time requirement for solving la… Show more

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Cited by 29 publications
(10 citation statements)
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“…Arık [50] Artificial bee colony The best component of IG combined with an artificial bee colony algorithm. Gyms et al [51] A new node decomposition scheme that combines dynamic branching and lower bound refinement strategies Arık [52] Population-based TS Hybrid solution method with crossover and mutation strategies for the problem under effects of learning and deterioration.…”
Section: Ts With Blocking Constrainmentioning
confidence: 99%
“…Arık [50] Artificial bee colony The best component of IG combined with an artificial bee colony algorithm. Gyms et al [51] A new node decomposition scheme that combines dynamic branching and lower bound refinement strategies Arık [52] Population-based TS Hybrid solution method with crossover and mutation strategies for the problem under effects of learning and deterioration.…”
Section: Ts With Blocking Constrainmentioning
confidence: 99%
“…In literature, FSSP has been proven to be NP-hard (Fernandez-Viagas et al 2017), and has been widely studied. For interested readers, please refer to (Wang et al 2020;Arık 2021;Kaya et al 2021). To further enhance production efficiency, some identical parallel machines are considered in the flow shop scheduling.…”
Section: Introductionmentioning
confidence: 99%
“…In literature, Meta-heuristic algorithms have been developed to solve PFSSP by several researchers. Most significant meta-heuristics used in literature are Artificial Bee Colony Algorithm (ABC) (Deng, Xu [35]; Han, Gong [36]; Li and Pan [37]; [38]), Differential Evolution Algorithm (DE) (Liu,Yin [39]), Evolutionary Algorithm (EA)(Qian, Wang [40], Yeh and Chiang [41]), Genetic Algorithms (GA) (Caraffa, Ianes [42]; Ruiz, Maroto [43]; Vallada and Ruiz [44]; Akhshabi, Haddadnia [45]; Andrade, Silva [46]), Hybrid Discrete Differential Evolution (HDDE)(Wang, Pan [47]), Hybrid Differential Evolution Algorithm (HDEA) (Liu,Yin [39]), Simulated Annealing (SA) (Laha and Chakraborty [48]; Lin and Ying [49]; Moslehi and Khorasanian [50]) Lin, Cheng [51], Tabu Search (TS)(Taillard [52]; Grabowski and Wodecki [53]; Grabowski and Pempera [54]; Arık [55]), TS and ABC (Li and Pan [37]), Hybrid Whale optimization algorithm(HWO) (Abdel-Basset, Manogaran [56]), Particle swarm optimization (PSO) (Zhao, Qin [57]) Evolution Strategy (ES) (de Siqueira, Souza [58]; Khurshid, Maqsood [59]), among others. These algorithms have found competitive results for different PFSSP's compared to heuristics; however, they require more computational time, as they initiate from a sequence that is constructed by heuristics and is iterated until termination criteria are achieved.…”
Section: Introductionmentioning
confidence: 99%