Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time

Marichelvam, M.K.; Tosun, Ömür; Mani, Geetha

doi:10.1016/j.asoc.2017.02.003

Cited by 47 publications

(24 citation statements)

References 37 publications

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“…Marichelvam et al [3] incorporated NEH with the cuckoo search algorithm to constitute an improved cuckoo search algorithm (ICS). Lately, Marichelvam et al [4] proposed a hybrid monkey search algorithm (MSA) which was integrated with the dispatching rules, the shortest processing time, the longest processing time, and NEH heuristics to improve MSA performance. The author showed that MSA gives competitive results when it is compared with NEH.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

Abdel‐Basset

Manogaran

El-Shahat

et al. 2018

Future Generation Computer Systems

248

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The flow shop scheduling problem is one of the most important types of scheduling with a large number of real-world applications. In this paper, we propose a new algorithm that integrates the Whale Optimization Algorithm (WOA) with a local search strategy for tackling the permutation flow shop scheduling problem. The Largest Rank Value (LRV) requires the algorithm to deal with the discrete search space of the problem. The diversity of candidate schedules is improved using a swap mutation operation as well. In addition to the insert-reversed block operation is adopted to escape from the local optima. The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz-Enscore-Ham (NEH) to improve the performance of the algorithm. It is observed that HWA gives competitive results compared to the existing algorithms.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

Abdel‐Basset

Manogaran

El-Shahat

et al. 2018

Future Generation Computer Systems

248

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“…In the paper, the authors have provided the mathematical formulation and a hybrid metaheuristic algorithm combined ant colony optimization and simulated annealing algorithm. Marichelvam et al [7] address a flow-shop scheduling problem with minimization of the makespan and total flow time, which is a biobjective flow-shop scheduling problem. In order to solve the proposed problem, a hybrid monkey search algorithm based on a subpopulation has been studied.…”

Section: Introductionmentioning

confidence: 99%

A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

Sun

Qi²

2020

Scientific Programming

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In this paper, we address a hybrid flow-shop scheduling problem with the objective of minimizing the makespan and the cost of delay. The concerned problem considers the diversity of the customers’ requirements, which influences the procedures of the productions and increases the complexity of the problem. The features of the problem are inspired by the real-world situations, and the problem is formulated as a mixed-integer programming model in the paper. In order to tackle the concerned problem, a hybrid metaheuristic algorithm with Differential Evolution (DE) and Local Search (LS) (denoted by DE-LS) has been proposed in the paper. The differential evolution is a state-of-the-art metaheuristic algorithm which can solve complex optimization problem in an efficient way and has been applied in many fields, especially in flow-shop scheduling problem. Moreover, the study not only combines the DE and LS, but also modifies the mutation process and provides the novel initialization process and correction strategy of the approach. The proposed DE-LS has been compared with four variants of algorithms in order to justify the improvements of the proposed algorithm. Experimental results show that the superiority and robustness of the proposed algorithm have been verified.

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“…This type of research is typically inspired by natural behavior, and has yielded algorithms such as the artificial bee colony (ABC) [24], ant colony optimization (ACO) [25], cuckoo search (CS) [26], differential evolution (DE) [27], firefly algorithm (FA) [28], gravitational search algorithm (GSA) [29], particle swarm optimization (PSO) [30], and whale optimization (WA) [31]. Recently, the combinatorial optimization community has attempted to solve the PFSP by using metaheuristic approaches, such as those in References [32][33][34][35][36][37]. One of the recently proposed swarm-based intelligence algorithms is the crow search algorithm (CSA) [38].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

Huang

Girsang

et al. 2019

Applied Sciences

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The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

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Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time

Cited by 47 publications

References 37 publications

RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

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