Solving and optimizing a bi-objective open shop scheduling problem by a modified genetic algorithm

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

“…Lastly, the scheduling problems which is the most discussed category of MOCOPs consists of open shop scheduling problem [125], [126], job shop scheduling problem (JSSP) [127], [128], [129], [130], [131], [132], FSP [133], [134], [135], [136], [137], [138], project scheduling problem (PSP) [139], resource constrained PSP (RCPSP) [140], [141], [142], [143], [144], [145], timetabling problem [146], cross-docking scheduling problem [147], task scheduling problem [148], [149], [150], [151], [152], [153], [154], machine scheduling problem [155], [156], [157], [158], [159], [160], [161], satellite range scheduling problem [162], multi-objective satellite data transmission scheduling problem [163], satellite scheduling of large areal tasks [164], operating room scheduling [165], [166], harvest scheduling problem [167], energy-efficiency scheduling problem…”

“…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…”

“…The operation-based order crossover (OX), job position mutation, and process plan mutation were used as genetic operators for the proposed NSGA-II. Azadeh et al [126] introduced a new bi-objective mixedinteger open shop scheduling problem and solved it using an extended NSGA-II in which the initial population was generated differently to improve algorithm speed, simulated annealing crossover operators, and variable neighbourhood search (VNS) mutation operator were used. To validate the proposed algorithm's efficiency, small, moderate, and largesized instances were randomly generated, and augmented ECM (AUGMECON) was used for the comparison.…”

Los grupos de interés en la programación de producción de un sistema de manufactura “Job Shop”

“…En la literatura de corriente principal ((Azadeh et al, 2016), (Geng et al, 2018), (Kaplanoğlu, 2016), (Robaninejat et al, 2016), (Shivasankaran, 2015), (Yan y Yue, 2015)) y sus antecedentes, se observa que los procesos de investigación en sistemas "Job Shop", se han orientado prioritariamente hacia la incorporación de diversidad de variables, las cuales se encuentran relacionadas con el campo de la eficiencia técnica. De esta forma, se pretende explicar y mejorar el desempeño económico de tal clase de sistema productivo.…”

“…En este sentido, la revisión de la literatura de corriente principal, permite discriminar las siguientes categorías, en el campo de la programación multiobjetivo de los sistemas "Job Shop": Evaluación de eficiencia en cuánto a asignación de carga de trabajo. Para el caso, se relacionan entre otros, 6 desarrollos, los cuales buscan minimizar el comportamiento de las variables: tiempo de proceso, total de carga de trabajo y total de carga de trabajo en la operación crítica ((Azadeh et al, 2016), (Huang y Süer, 2015), (Kaplanoglu, 2016), (Ojstersek et al, 2018), (Pérez y Hernández, 2018), (Zhu et al, 2016)).…”