Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Table 3

FJSSP with advanced forms of GA.

Ref	Year	Article type	Algorithm details	Objective	GA parameters			Benchmark
					Mutation	Crossover	Selection
[19]	2002	J	Controlled GA	, sum of machine workloads	Artificial mutation	Modified crossover	Elitism	KA
[103]	2005	J	Multistage operation based GA		Local-search mutation	One-cut point crossover	Random	KA
[104]	2005	C	LEGA		-	-	-	KA
[20]	2006	J	Iterative GA		Random	SPX, job-based order crossover	Roulette wheel	FT, LA
[105]	2006	C	GA with Choquet integral		-	-	-	KA
[106]	2007	J	Learning GA		Random, algorithm based	TPX, random	Linear scaling, stochastic universal sampling	Mesghouni, BR
[107]	2008	C	TPGA	Min of fuzzy	Random	GOX, generalization of PPX	Tournament selection	KA
[108]	2008	J	GA with Choquet integral	, sum of weighted earliness and weighted tardiness, sum of production cost	-	-	-	KA
[109]	2009	C	Course grained parallel GA based on island model parallelization technique		Sublot step mutation, Sublot swap mutation, random operation AssM, intelligent operations AssM, operations sequence shift mutation	SPX-, SPX-2 (SPC-2), operation to machine ACX, job level operations sequence crossover, subplot level operations sequence crossover	Tournament selection	Other
[110]	2009	C	Adaptive GA		Adaptive	Adaptive, precedence operation crossover	Roulette wheel	KA
[111]	2010	C	GA with learning by injection of sequences		Mutation by direct exchange, mutation by random exchange, mutation by inversion, Mutation by close exchange, mutation by gap of all the elements	SPX	Random	FT, other
[112]	2010	C	Cooperative coevolutionary GA		Random, SM	Row crossover, column crossover, precedence order crossover	Roulette wheel	BR
[113]	2010	J	Parallel GA		PPS, AssM, IM	ACX, POX	Tournament selection	Other
[114]	2010	C	Matrix coded GA		Machine dimension mutation, operation dimension mutation	Machine dimension crossover, operation dimension crossover	Tournament	Other
[115]	2010	J	Decomposition integration GA		Swap operator	Generalized position crossover, generalization of PPX	Tournament selection	KA
[116]	2010	J	LEGA		Random, operational memory based mutation	TPX, random	Tournament	LA
[117]	2010	C	Adaptive GA		Random	OPX, three-point crossover	League selection	Industry
[118]	2010	C	Coevolutionary genetic algorithm	Fuzzy	SM; Random	TPX, discrete crossover	Tournament selection	KA
[119]	2010	C	GA based on immune and entropy principle		Random	IPOX, MPX	-	KA
[120]	2011	J	GA with heuristics		-	-	Elitism, tournament selection	BR, other
[121]	2011	C	Adaptive GA		Random	OPX	Elitism	KA
[122]	2011	C	MSCEA		Neighborhood mutation	TPX	Random	BR
[123]	2012	J	Multiobjective GA	, total machining time	SM	SPX	-	Other
[124]	2012	C	GA with learning		Random	OPX	Elitist	LD, BR
[125]	2012	J	Coevolutionary GA	Fuzzy	SM	TPX, extension of PPX	Modified tournament selection	Other
[126]	2012	J	Jumping genes GA	, flow time of products with AGV, completion of the products	EM	SPX	Tournament selection	Other
[127]	2013	J	Real coded GA		Random	Extended intermediate crossover, OPX	Roulette wheel, binary tournament, elitism, replacement	BR
[128]	2014	J	NSGA-II based on blood variation	, processing cost, energy consumption, cost-weighted processing quality	Modified mutation	Blood relation based crossover	Modified quick sorting ranking	Industry
[129]	2016	CP	Immune GA	Maximization of due time satisfaction, minimize the total processing costs	Random	SPX	Roulette wheel	FT
[130]	2016	J	Extended GA	Maximize satisfaction degree	SM	TPX, POX	Tournament selection	Other
[131]	2016	CP	GA with comprehensive search		Random	Operation-based crossover	Roulette wheel	Other