A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows

Zhu, Ningning; Zhao, Fuqing; Ding, Ruiqing; Xu, Tianpeng; Jonrinaldi,

doi:10.1016/j.eswa.2022.116921

Cited by 22 publications

(2 citation statements)

References 40 publications

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“…Zhu et al [46] proposed a distributed no-wait flow shop scheduling problem with due windows, with an efficient discrete knowledge-guided learning fruit fly optimization algorithm. Similarly, Guo et al [49] proposed a discrete fruit fly optimization algorithm based on a differential flight strategy to solve a DPFSP.…”

Section: Green Scheduling Problems Application Areasmentioning

confidence: 99%

A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

et al. 2023

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The concept of sustainability is defined as composed of three pillars: social, environmental, and economic. Social sustainability implies a commitment to equity in terms of several “interrelated and mutually supportive” principles of a “sustainable society”; this concept includes attitude change, the Earth’s vitality and diversity conservation, and a global alliance to achieve sustainability. The social and environmental aspects of sustainability are related in the way sustainability indicators are related to “quality of life” and “ecological sustainability”. The increasing interest in green and sustainable products and production has influenced research interests regarding sustainable scheduling problems in manufacturing systems. This study is aimed both at reducing pollutant emissions and increasing production efficiency: this topic is known as Green Scheduling. Existing literature research reviews on Green Scheduling Problems have pointed out both theoretical and practical aspects of this topic. The proposed work is a critical review of the scientific literature with a three-pronged approach based on keywords, taxonomy analysis, and research mapping. Specific research questions have been proposed to highlight the benefits and related objectives of this review: to discover the most widely used methodologies for solving SPGs in manufacturing and identify interesting development models, as well as the least studied domains and algorithms. The literature was analysed in order to define a map of the main research fields on SPG, highlight mainstream SPG research, propose an efficient view of emerging research areas, propose a taxonomy of SPG by collecting multiple keywords into semantic clusters, and analyse the literature according to a semantic knowledge approach. At the same time, GSP researchers are provided with an efficient view of emerging research areas, allowing them to avoid missing key research areas and focus on emerging ones.

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Section: Green Scheduling Problems Application Areasmentioning

confidence: 99%

A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

et al. 2023

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“…Based on the independent variational mode decomposition, Li and Xu [17] established an effective FOA algorithm to deal with the laser cladding operations. Zhu et al [18] proposed a discrete knowledge-guided learning FOA algorithm to deal with the distributed no-wait flow shop scheduling problem with the due windows. Other relevant research results can be found in references [19]- [29].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

Zhang,

Xiong,

Liu

2023

JAMP

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In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed; in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

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Probability Learning Based Multi-objective Evolutionary Algorithm for Distributed No-Wait Flow-Shop and Vehicle Transportation Integrated Optimization Problem

Ding

Qian

et al. 2023

Lecture Notes in Computer Science

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A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows

Cited by 22 publications

References 40 publications

A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

Probability Learning Based Multi-objective Evolutionary Algorithm for Distributed No-Wait Flow-Shop and Vehicle Transportation Integrated Optimization Problem

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