Hybridization of Artificial Bee Colony algorithm with Particle Swarm Optimization algorithm for flexible Job Shop Scheduling

Muthiah, A.; Rajkumar, A.; Rajkumar, Ragunathan

doi:10.1109/iceets.2016.7583875

Cited by 12 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Muthiah et al (2016) [100] proposed the hybridization of the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) optimization techniques to minimize the makespan of the shops. In the same vein, Nouiri et al (2017) [101] proposed a two-stage particle swarm optimization (2S-PSO) to solve the problem assuming that there is only one breakdown.…”

Section: (8) Combination Algorithms and Recent Studies Based On Psomentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

View full text Add to dashboard Cite

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

show abstract

Section: (8) Combination Algorithms and Recent Studies Based On Psomentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Singh and Mahapatra [122] proposed a quantum-behaved PSO for solving the FJSP, which overcomes the drawback of PSO easily getting trapped at a local optimum. Muthiah et al [123] proposed a hybridisation of PSO and the artificial bee colony (ABC) to solve the FJSP to minimise makespan. Yi et al [124] proposed an effective MA, which is a combination of TS and GA for the FJSP to minimise the makespan.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

156

View full text Add to dashboard Cite

Flexible job shop scheduling problem (FJSP) is an NP‐hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta‐heuristics, which are reviewed comprehensively. Moreover, the real‐world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.

show abstract

“…Sha and Lin (2010) proposed, a multi-objective PSO, to increase the search quality and efficiency of searching for optimal scheduling time and jobs [15]. Muthiah et al, (2016) [16] proposed the JSSP became flexible when using the Hybridization of ABC and PSO Algorithm for to effectively reduce time and cost for multi-assigned jobs. Motivated by this concept, we propose a novel intelligent algorithm for the cost problems based on the Hybridization of ABC and PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

2019

IJRTE

View full text Add to dashboard Cite

The Job Shop Scheduling Problem (JSSP) is one of the major inconvenience models because it is the hardest combinatorial change in nature. So far number of algorithms developed to solve JSSP and still there is scope to develop an efficient algorithm. Hence, this paper considered a unique hybrid algorithm which is the combination of the Grey Wolf and Chicken Swarm Optimization algorithm with the objective of minimization of makespan time and cost. The most extraordinary compilation (makespan), and the tardiness is considered in the meantime. The hybrid algorithm count relies upon the reenactment of the swarming behavior of wolves’ individuals. The base objectives of each wolf and swarms from most bewildering thicknesses of the group are considered as the objective work for the improvement. The results show that the proposed hybrid Grey Wolf and Chicken Swarm Optimization algorithm can obtain better results in terms of various iteration levels. The accuracy and robustness are also applied in real industrial conditions and for large size problems. The proposed method results show that very less time duration when compared with the Genetic Algorithm (GA) and Ants Colony Optimization (ACO) algorithm.

show abstract

Hybridization of Artificial Bee Colony algorithm with Particle Swarm Optimization algorithm for flexible Job Shop Scheduling

Cited by 12 publications

References 21 publications

Review of job shop scheduling research and its new perspectives under Industry 4.0

Review of job shop scheduling research and its new perspectives under Industry 4.0

Review on flexible job shop scheduling

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

Contact Info

Product

Resources

About