An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

Jiang, Enda; Wang, Ling

doi:10.1080/00207543.2018.1504251

Cited by 101 publications

(35 citation statements)

References 29 publications

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“…The main aspects of energy consumption, e.g., processing energy, idle, and setup energy, in a production process are modeled and evaluated. Some researchers have started to consider other energy consumption in a production process, for instance, energy for component transportation [80,88].…”

Section: Extending Of Energy Consumption Aspectsmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

Self Cite

183

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Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

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Section: Extending Of Energy Consumption Aspectsmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

Self Cite

183

View full text Add to dashboard Cite

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“…In order to solve the problem, they used hybrid multi-objective backtracking search algorithm. The turn on /off framework was used in a same problem in the work of Jiang and Wang (2018). They presented a bi-objective mathematical model to minimise makespan and energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There exists a little attention to the flowshop scheduling problem with sequence dependent setup times considering energy consumption. As far as we know, only the works of Lu et al (2017) and Jiang and Wang (2018) have considered energy-efficient m-machine SDST flowshop scheduling problem which both of them used turn on/off framework. There is still a need for discussion on energy-efficient m-machine SDST PFS, which is addressed in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study

Ramezanian

Vali-Siar

Jalalian

2019

International Journal of Production Research

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Increasing global energy consumption, large variations in its cost and the environmental degradation effects are good reasons for the manufacturing industries to become greener. Green shop floor scheduling is increasingly becoming a vital factor in the sustainable manufacturing. In this paper, a green permutation flowshop scheduling problem with sequence-dependent setup times is studied. Two objectives are considered including minimisation of makespan as a measure of service level and minimisation of total energy consumption as a measure of environmental sustainability. We extend a bi-objective mixedinteger linear programming model to formulate the stated problem. We develop a constructive heuristic algorithm to solve the model. The constructive heuristic algorithm includes iterated greedy (CHIG) and local search (CHLS) algorithms. We develop an efficient energy-saving method which decreases energy consumption, on average, by about 15%. To evaluate the effectiveness of the constructive heuristic algorithm, we compare it with the famous augmented ε-constraint method using various small-sized and large-sized problems. The results confirm that the heuristic algorithm obtains high-quality nondominated solutions in comparison with the augmented ε-constraint method. Also, they show that the CHIG outperforms the CHLS. Finally, this paper follows a case-study, with in-depth analysis of the model and the constructive heuristic algorithm.

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“…A genetic-SA has been adopted by Dai et al [28] to achieve a significant advance in this field. The improved multi-objective evolutionary algorithm based on decomposition has also been considered [29]. A Pareto-based chemical-reaction optimization algorithm has been proposed by Li et al [30].…”

Section: Introductionmentioning

confidence: 99%

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

Tao

Huang

et al. 2020

Complex Intell. Syst.

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The resource-constrained hybrid flowshop problem (RCHFS) has been investigated thoroughly in recent years. However, the practical case that considers both resource-constrained and energy consumption still has rare research. To address this issue, a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption. In the proposed algorithm, first, each solution was represented by a two-dimensional vector, where one vector represented the scheduling sequence and another one showed the machine assignment. Then, a decoding method considering the resource allocation was designed. Finally, we combined DICA and the simulated annealing algorithm (SA) to improve the performance of the proposed approach. Furthermore, we tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances and compared with the existing heuristic algorithms. The results confirmed that the proposed algorithm can solve the RCHFS with high efficiency.

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An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

Cited by 101 publications

References 29 publications

A review of energy-efficient scheduling in intelligent production systems

A review of energy-efficient scheduling in intelligent production systems

Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study

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

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