A Novel Multi-Population Artificial Bee Colony Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Problem

Zuo, Yandi; Fan, Zhun; Zou, Tierui; Wang, Pan

doi:10.3390/sym13122421

Cited by 27 publications

(28 citation statements)

References 68 publications

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“…ABC has become a very active research area and many modifications [28] and enhancements [71] of the original algorithm were introduced. For PFSP, ABC was applied for different versions of the problem, as a standalone method [72][73][74][75][76][77], hybridized with other approaches [46,78], to optimize single or multiple objective functions [79,80].…”

Section: Related Workmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Ladj,

Benbouzid-Si Tayeb,

Dahamni

et al. 2024

Technologies

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This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.

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Section: Related Workmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Ladj,

Benbouzid-Si Tayeb,

Dahamni

et al. 2024

Technologies

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“…As seen from Figure 2a, the sigmoid function is a strictly monotonically increasing, continuous, and smooth threshold function. Perform telescopic translation on Equation (12) and introduce amplitude, telescopic, and translational factors to obtain:…”

Section: Improvement Of Parameter Amentioning

confidence: 99%

“…A natural phenomenon algorithm is a kind of algorithm based on the physical laws of nature, such as the sine cosine algorithm (SCA) [ 9 ] and biogeography-based optimization (BBO) [ 10 ]. The inspiration for the population life habit algorithm comes from the relationship between population individuals, including particle swarm optimization (PSO) [ 11 ], artificial bee colony (ABC) algorithm [ 12 ], cuckoo search (CS) algorithm [ 13 ], bat algorithm (BA) [ 14 ], and ant colony optimization (ACO) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

Xia

Ding

Zhang

et al. 2022

Entropy

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This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.

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“…This implies that the manufacturing companies urgently need to implement an effective production scheduling [7]- [9] and an efficient energy-saving system [10], in order to reduce energy consumption [11]- [13]. Scheduling and sequencing refer to a resource allocation system that requires efficient management [14], and one of the issues encountered during the process is the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP) [15]. The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis.…”

Section: Introductionmentioning

confidence: 99%

“…Constraint (14) ensures that the job has been processed in the previous stage before being processed at stage 𝑗 + 1. Constraint (15) illustrates that the job completion time in the previous stage is considered, as well as the initial setup time for each job, processing time, and removal time. Furthermore, constraint (16) shows that when the machine completes a job, it is positioned to do the next, and constraint (17) models that each job is only processed on 1 machine in each phase, where variable 𝑌 𝑖𝑗𝑘 represents a binary number 0 and 1.…”

mentioning

confidence: 99%

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

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

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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.

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A Novel Multi-Population Artificial Bee Colony Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Problem

Cited by 27 publications

References 68 publications

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

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

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