Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

Tao, Xin-Rui; Li, Junqing; Huang, Tihao; Duan, Ping

doi:10.1007/s40747-020-00193-w

Cited by 24 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have previously discussed this problem under two stages, including Schulz [16] which proposed a Genetic Algorithm (GA), and Du, et al [17] which introduced Ant Colony Optimization (ACO) algorithm. Tao, et al [18] also presented the Discrete Imperialist Competitive Algorithm in the two stages of EEHFSP. Furthermore, it has also been performed under three stages such as Zeng, et al [19] which formulated Particle Swarm Optimization (PSO), and Liu, et al [20] which utilizeda mixed integer linear programming approach.…”

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms.

show abstract

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…However, there is still work to be considered in the future, we could (1) consider combining the flexible job shop scheduling problem with other complicated constraints that are typically ignored in this field to deploy our research to actual production, for example, fuzzy processing time, 46 setup time, 47 and distributed multi-factories 36 ; (2) consider factors that we have not considered thoroughly enough, for example, the energy consumption generated by cranes in the process of handling and transferring, the energy consumption generated by machines in idle time 48 ; (3) consider the normalization of multiple objectives 49 ; (4) consider combining algorithms with neural model 50,51 ; and (5) consider applying the proposed algorithms to other scheduling fields. 52–54…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Improved artificial immune algorithm for the flexible job shop problem with transportation time

Chen

Han

et al. 2020

Measurement and Control

Self Cite

View full text Add to dashboard Cite

The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

show abstract

“…In [13], the IPSO algorithm is proposed to improve the efficiency of resource scheduling while facing a large amount of tasks. In [14], a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption of the resource-constrained hybrid flowshop problem (RCHFS). In [15], efficient exact algorithms are devised for the multitasking scheduling problems with batch distribution and due date assignment (DDA).…”

Section: Introductionmentioning

confidence: 99%

EHEFT-R: multi-objective task scheduling scheme in cloud computing

Zhang

Sun

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

show abstract

Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

Cited by 24 publications

References 59 publications

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Improved artificial immune algorithm for the flexible job shop problem with transportation time

EHEFT-R: multi-objective task scheduling scheme in cloud computing

Contact Info

Product

Resources

About