Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time

Komaki, G.M.; Kayvanfar, Vahid

doi:10.1016/j.jocs.2015.03.011

Cited by 245 publications

(94 citation statements)

References 17 publications

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“…Grey Wolf Optimizer is recently developed metaheuristics inspired from the hunting mechanism and leadership hierarchy of grey wolves in nature and has been successfully applied for solving optimizing key values in the cryptography algorithms [15], feature subset selection [16], time forecasting [17], optimal power flow problem [18], economic dispatch problems [19], flow shop scheduling problem [20], and optimal design of double layer grids [21]. Several algorithms have also been developed to improve the convergence performance of Grey Wolf Optimizer that includes parallelized GWO [22,23], binary GWO [24], integration of DE with GWO [25], hybrid GWO with Genetic Algorithm (GA) [26], hybrid DE with GWO [27], and hybrid Grey Wolf Optimizer using Elite Opposition Based Learning Strategy and Simplex Method [28].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Singh

2017

Journal of Applied Mathematics

220

143

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A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants' strength. Some unimodal, multimodal, and fixeddimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

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Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Singh

2017

Journal of Applied Mathematics

220

143

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show abstract

“…Yusof and Mustaffa [80] developed GWO to forecast daily crude oil prices. Komaki and Kayvanfar [81] employed GWO to solve scheduling optimization problems. El-Fergany and Hasanien [82] integrated GWO and DE to handle single and complex power flow problems.…”

Section: Grey Wolf Optimizer Methodsmentioning

confidence: 99%

“…Mittal et al [85] proposed a modified grey wolf optimizer (MGWO) to improve the exploration and exploitation capability of the GWO that led to optimal efficiency of the method. GWO implementation steps are referenced in [18,81].…”

Section: Grey Wolf Optimizer Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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“…Authors observed that integrated scheduling of jobs, machine centers, and AGVs scheduling, in the FMS can yield impressive utilization. Komaki and Kayvanfar (2015) applied a grey wolf optimization algorithm for two-stage assembly flow shop scheduling problem considering the release time of fabrication jobs and assembly jobs. Similarly, Lu et al (2017) solved a multi-objective dynamic scheduling problem by application of grey wolf optimization algorithm for the welding operations.…”

Section: Literature Reviewmentioning

confidence: 99%

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

Chawla¹,

Chanda²,

Angra³

2019

10.5267/j.jpm

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The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the efficient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced work-load for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

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Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time

Cited by 245 publications

References 17 publications

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

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