2004
DOI: 10.1016/j.future.2004.03.014
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A new approach to solve hybrid flow shop scheduling problems by artificial immune system

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Cited by 236 publications
(111 citation statements)
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“…Artificial Immune Systems (AIS), for instance, have been used in [47] and in [232]. In the later case, the proposed AIS outperformed the GA proposed in [105].…”
Section: Metaheuristicsmentioning
confidence: 99%
See 1 more Smart Citation
“…Artificial Immune Systems (AIS), for instance, have been used in [47] and in [232]. In the later case, the proposed AIS outperformed the GA proposed in [105].…”
Section: Metaheuristicsmentioning
confidence: 99%
“…F Hm, ((P M (k) ) m k=1 ))|avail|several simulation, heuristics, SA [16] F H3, ((P M (k) ) 3 k=1 ))||Cmax agent-based approach [20] F Hm, ((P M (k) ) m k=1 ))|recrc|Ū w MPF, GA, lower bounds,checks processing [47] F Hm, ((P M (k) ) m k=1 ))||Cmax Artificial Immune Systems [91] F Hm, ((P M (k) ) m k=1 ))|blocking, skip|Cmax flow lines, MPF, TS, huristics F H2, ((1 (1) , P 2 (2) ))||Cmax B&B, GA, heuristics [41] F Hm, ((P M (k) ) m k=1 ))|recrc|T w dispatching rules, heuristics [61] F H2, ((1 (1) , P 2 (2) ))|no − wait, (p j = 1) 1 |Cmax exact method [96] F Hm, ((P M (k) ) m k=1 ))||{Cmax,C} review on exact solution methods [121] F H2, ((1 (1) , P M (2) ))|avail|Cmax B&B, heuristics, complexity [38] F H3, ((RM (k) ) 3 k=1 ))|prec, block, S nsd |Cmax MPR-TS [70] F H2, ((P M (k) ) 2 k=1 )||Cmax B&B [88] F Hm, ((P M (k) ) m k=1 ))||Cmax MPR-SA, lower bounds [107] F H2, ((P 2 (1) , 1 (2) ))|batch|Cmax TSP-based heuristics [37] F H3, ((RM (k) ) 3 k=1 ))|S sd , block, prec|Cmax MPF, lower bounds, TS [50] F Hm, ((P M (k) ) m k=1 ))|assign|ĒT TS, special problem [83] F Hm, ((P M (k) ) m k=1 ))|r j |Cost TS, SA, heuristics [85] F Hm, ((RM (k) ) m k=1 ))|lot, skip|Cost GA, SA, flow lines [100] F H3, ((P M (k) ) 3 k=1 ))||Cmax heuristics [151] (1) , P 2 (2) ))|assembly (2) |F heuristics [195] F Hm, ((P M (k) ) m k=1 ))|size jk |Cmax Particle Swarm Optimization [196] F H2, ((1 (1) , P 2 (2) ))|skip (1) |Cmax heuristics 2009 [90] F Hm, ((RM (k) ) m k=1 ))|S sd , r j |αCmax + (1 − α)Ū MPF, heuristics, dispatching rules, GA [95] F H2, ((P M (1) , 1 (2) ))||Cmax heuristics, product-mix [191] F Hm, ((P M (k) ) m k=1 ))|skip, block, reentry|Cmax GA mixed with LS [229] F Hm, ((P M (k) ) m k=1 ))|size jk |Cmax Iterated Greedy (IG) [19] F H2, ((P M (1) , P M (2) ))|batch (2) |Cmax heuris...…”
Section: Research Opportunities and Conclusionmentioning
confidence: 99%
“…Therefore, a lot of heuristics and meta-heuristics were put forward, such as the NEH algorithm [27], Palmer algorithm [28], CDS algorithm [29], and genetic algorithm [30]. More new methods were developed, including artificial immune algorithm [31], particle swarm optimization algorithm (PSO) [32], water-flow algorithm [33], quantum-inspired immune algorithm [34], iterated greedy algorithm [35], and intelligent hybrid meta-heuristic [36].…”
Section: Of 20mentioning
confidence: 99%
“…This emerging field of research is known as Artificial Immune System (AIS) [11]. Based on the different immune theories, various algorithms such as Danger Theory (DT) models [2], [3], Negative Selection Algorithms [5], [17], Immune Network Theory-based model [15], Clonal Selection Algorithms (CSA) [12], [18] are proposed and also applied on patter recognition [9],intrusion detection [10], [21], optimization problems [1], [14] and so on.…”
Section: Introductionmentioning
confidence: 99%