An NSGA-II-based multiobjective approach for real-time routing selection in a flexible manufacturing system under uncertainty and reliability constraints

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

“…selection [183], resource scheduling in fog computing [184], inter-site earthmoving optimization [185], process planning and scheduling [186], [187], [188], cross-trained workers scheduling [189], cross-docking scheduling [190], workforce scheduling problem [191], multi-objective optimized operation of integrated energy system with hydrogen storage [192] and multi-objective integrated optimization of configuration generation and scheduling [193].…”

“…The proposed approach has better convergence and solution quality as compared to the classical NSGA-II. Real-world TTSP Experiment & a TTSP for 2 units under test UUTs GA, Genetic simulated annealing algorithm (GASA) [141] iMOPSE Benchmark Dataset 5 DEGR, NSGA-II [136] Standard benchmark instances 6 NSGA-II [139] Problem instances from (PSPLIB) 7 AUGMECON2, MOPSO [151] Experiment NSGA-II, NSPSO [177] Test Instances generated through RaGEN software ECM [126] Randomly Generated AUGMECON [181] -NSGA-II [155] Experiment conducted through randomly generated data NSGA-II & SPEA-II [184] Simulation Scenario RANDOM, FIRMM [189] Experimental test NSGA-II [144] -- [127] Modified Deterministic FJSPs into Stochastic Problems NRGA [188] Randomly Generated Controlled elitist NSGA, NSGA-II [125] Test [193] -- [147] Randomly Generated Numerical example MOPSO [191] Randomly Generated - [183] -GA [161] Randomly generated instances for unrelated parallel machine - [195] -- [212] Randomly Generated SPEA [203] Sample test problems ECM [202] Sample test problems MOPSO [200] Randomly Generated NSDE [204] Randomly Generated Greedy Method, ISA, ISA-LOCAL [196] CAB & AP datasets 10 AUGMECON [209] -- [206] Randomly Generated Weighted Metric Approach [207] -ECM, Multi-objective simulated annealing (MOSA) [198] Randomly Generated SSPMO, WSM & ECM…”

“…Souier et al [183] developed a decision support system for the best solution to the multi-objective problem of selecting multiple routing for a flexible manufacturing system in an uncertain environment. NSGA-II has better performance than the GA in obtaining the trade-off solutions to the problem.…”

“…The choice of the best-compromised solution among the set of all compromised solutions is made by decision-makers using the preference information. Many techniques based on MCDM, data clustering, fuzzy logic, and some other criteria that are applied to find the best-compromised solution in the studied literature is given in Table XII [201], [136], [139], [49], [183], [54] t-test [127], [45], [199], [133], [117], [50] Wilcoxon sign rank test [141], [168], [109], [208], [93] Wilcoxon rank-sum test [201], [114], [205], [80] Statistical hypothesis tests [159], [47], [107], [194] Kruskal-Wallis test [201], [94], [54] Friedman test [184], [110], [208] Wilcoxon paired test [137], [45] Shapiro-Wilk statistical hypothesis test [45], [54] ANOM test [79], [85] Sign test, Permutation test, Bonferroni/Dunn's test, Welch test, Levene test, Kolmogorov-Smirnov (K-S) test, Post-hoc test [137], [116], [110], [54], [54],…”

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

“…Huang [14] proposed a hybrid genetic algorithm to solve the scheduling problem while considering transportation time. Souier et al [15] used NSGA-II for real-time scheduling under uncertainty and reliability constraints. Ahmadi et al [16] used NSGA-II and non-dominated ranking genetic algorithm (NRGA) to optimize the makespan and stability under the disturbance of machine breakdown.…”