Parallel Multi-objective Job Shop Scheduling Using Genetic Programming

A Learning Automata-Based Multiobjective Hyper-Heuristic

“…Some offline learning techniques have also been seen in recent MOHHs studies, e.g., genetic programming techniques in ( [36], [37], [38], [39], [40]), grammatical evolution [41] in [42] and top-down induction of decision trees in [43].…”

Genetic programming for production scheduling: a survey with a unified framework

“…GP has been applied a wide range production scheduling problems, ranging from single machine scheduling (Dimopoulos and Zalzala, 2001;Jakobovic and Budin, 2006;Nie et al, 2010;Yin et al, 2003;Geiger et al, 2006), parallel machine scheduling (Jakobovic et al, 2007;Durasevic et al, 2016), to (flexible) job shop scheduling (Miyashita, 2000;Jakobovic and Budin, 2006 and Ho, 2008;Vazquez-Rodriguez and Ochoa, 2011;Nie et al, 2011bNie et al, , 2013aNguyen et al, 2013aNguyen et al, ,b, 2014dHunt et al, 2014b;Hart and Sim, 2016;Karunakaran et al, 2016a). Most machines considered in these problems are the same in terms of capability (eligibility to handle a job) and assumptions (e.g.…”

“…The results show that the SASEGASA method can cope better with the states of exception in the simulation than island based methods. In a very recent study, Karunakaran et al (2016a) investigated GP with different topologies of the island model to deal with multi-objective job shop scheduling. Their experimental results showed that the proposed techniques outperform some general-purpose multi-objective optimization methods, including NSGA-II and SPEA-2.…”

Genetic programming for production scheduling: a survey with a unified framework

“…GP has been applied in a wide range of production scheduling problems, ranging from single machine scheduling [30,38,59,100,142], parallel machine scheduling [31,60], to (flexible) job shop scheduling [42,53,59,63,79,81,88,89,95,102, Chen et al [26] Durasevic et al [31] Freitag and Hildebrandt [35] Hart and Sim [42] Karunakaran et al [63] Park et al [115] Park et al [116] Riley et al [123] Branke et al [19] Mei and Zhang [78] Karunakaran et al [64] Masood et al [75] Mei et al [79] Nguyen [84] Nguyen et al [99] Production Scheduling Problems 104,135,136]. Most machines considered in these problems are the same in terms of capability (eligibility to handle a job) and assumptions (e.g.…”

“…The results show that the SASEGASA method can cope better with the states of exception in the simulation than island-based methods. In a very recent study, Karunakaran et al [63] investigated GP with different topologies of the island model to deal with multi-objective job shop scheduling. Their experimental results showed that the proposed techniques outperform some general-purpose multi-objective optimization methods, including NSGA-II and SPEA-2.…”