A Hybrid Artificial Bee Colony Algorithm with Local Search for Flexible Job-shop Scheduling Problem

Thammano, Arit; Phu-ang, Ajchara

doi:10.1016/j.procs.2013.09.245

Cited by 30 publications

(10 citation statements)

References 7 publications

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“…is the flight position of particle i after the (t+1) th iteration; round is the rounded output [13][14][15][16][17][18][19][20][21].…”

Section: Icsamentioning

confidence: 99%

Job-Shop Scheduling Problem Based on Improved Cuckoo Search Algorithm

Hu¹,

Lei²,

Gao³

et al. 2018

Int. j. simul. model.

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This paper makes an in-depth exploration into the job-shop scheduling problem (JSP). After reviewing the related literature, the local search mechanism of the particle swarm algorithm (PSA) and the largespan search principle of standard cuckoo search algorithm (CSA) were combined into an improved cuckoo search algorithm (ICSA), which is capable of both local search and global search. Later, several simulation experiments were carried out on the LA type typical library proposed by Lawrence, and the stability and accuracy of the ICSA was contrasted with those of the PSA and the genetic algorithm (GA) based on the means and variances in multiple iterations. After the comparison, a convergence analysis of the ICSA was specially designed for our model. The results demonstrate that the ICSA provides a better tool for solving the JSP than other algorithms. The research findings lay a solid theoretical basis for the JSP in the actual production process.

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“…is the flight position of particle i after the (t+1) th iteration; round is the rounded output [13][14][15][16][17][18][19][20][21].…”

Section: Icsamentioning

confidence: 99%

Job-Shop Scheduling Problem Based on Improved Cuckoo Search Algorithm

Hu¹,

Lei²,

Gao³

et al. 2018

Int. j. simul. model.

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“…A. Thammano and A. Phu-ang [11] solved FJSP problem using hybrid Artificial Bee Colony algorithm with local search for makespan. The initial solution was created using several dispatching rule and harmony search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Two Improved Cuckoo Search Algorithms for Solving The Flexible Job-Shop Scheduling Problem

Al-Obaidi

Hussein²

2016

IJPCC

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The Cuckoo Search (CS) is heuristic search algorithm which inspired from cuckoo bird behavior. In this paper, we proposed two improvements for the cuckoo search algorithm of solving Flexible Job-Shop Scheduling problem (FJSP); the first one depends on Best Neighbors Generation (CS-BNG) and the second one based on Iterative Levy Flight (CS-ILF). Some adaptation for the key points of CS algorithm has been done to enhance searching in the discrete state space. The proposed algorithms have increased solutions quality and convergence rate. The improved algorithms have been tested on some FJSP benchmark instances for performance examination. The experimental results demonstrate the effectiveness of the improved algorithms in comparison to the basic cuckoo search algorithm.

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“…Intelligent based FJSSP solving methods are bee colony algorithm [7,8], hybrid genetic algorithms and Tabu search [9], particle swarm optimization [10], gradual freezing algorithm and Tabu search algorithm [11] which they uses optimization methods to resolve scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

Pareto- Front Generation by Classical and Meta-heuristic Methods in Flexible Job Shop Scheduling with Multiple Objectives

Ghasemi¹,

Farzan²

2017

IJCA

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Planning and scheduling are as decision making processes which they have important roles in production systems and industries. According that, job shop scheduling is one of NPhard problems to solve multi-objective decision making approaches. So, the problem is known as uncertain with many variables in optimal solution view. Finding optimal solutions are essential task in scheduling of jobs between machines in the industries. In this paper, we present classical sum weighted (WS) method and non-dominated sorting genetic algorithm II (NSGA-II) to solve flexible job shop scheduling problem (FJSSP) with multiple objectives and find Paretofronts: minimizing completion time of jobs and maximizing machine employment. To generate Pareto-fronts, a search algorithm uses mechanism of variable weights and random selection to change directions in search spaces. The experiment results indicate that NSGA-II solve the problem more acceptable than WS method with considering computing time and consuming memory.

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A Hybrid Artificial Bee Colony Algorithm with Local Search for Flexible Job-shop Scheduling Problem

Cited by 30 publications

References 7 publications

Job-Shop Scheduling Problem Based on Improved Cuckoo Search Algorithm

Job-Shop Scheduling Problem Based on Improved Cuckoo Search Algorithm

Two Improved Cuckoo Search Algorithms for Solving The Flexible Job-Shop Scheduling Problem

Pareto- Front Generation by Classical and Meta-heuristic Methods in Flexible Job Shop Scheduling with Multiple Objectives

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