Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

Sun, Lei; Shi, Wei‐Min; Wang, Junru; Mao, Huimin; Jiajia, Tu; Wang, Luojun

doi:10.3390/app13095701

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…. taking a longer amount of time to arrive at the best possible answer (Meng et al, 2023;Sun et al, 2023). Besides that, the IPSO-based meta-heuristic algorithms provide superior outcomes in a shorter amount of time when compared to the other methods.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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“…It not only needs to consider the selection of multiskill resources, but it must also adjust the resource allocation of each time period for each activity on the basis of the MSRCPSP. In recent years, many meta-heuristics algorithms have been applied to solve RCPSP-like optimization problems [22][23][24][25][26][27][28][29][30][31]. Among them, the multiobjective fruit fly optimization algorithm (MOFOA), as an effective optimization algorithm, has shown promising potential for solving complex engineering optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

Luo,

Guo,

et al. 2024

Applied Sciences

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This paper addresses a novel multi-skill resource-constrained project scheduling problem with flexible resource profiles (F-MSRCPSP), in which the resource allocation of each activity consists of a certain number of discrete resources and is allowed to be adjusted over its duration. The F-MSRCPSP aims, therefore, to determine the flexible resource profile of each activity to minimize the make-span and total cost simultaneously. Then, a hybrid multi-objective fruit fly optimization algorithm is proposed to handle the concerned problem. In the proposed algorithm, two flexible parallel and serial schedule generation schemes are introduced, aiming to schedule activities and adjust allocated resource combinations. Additionally, two heuristic strategies are proposed to effectively select suitable resource combinations for activities. Moreover, a series of operators has been developed, including the rejoining operator, empirical re-arrangement operator, and empirical re-selection operator. These operators aim to accelerate the convergence speed and enhance the exploration of the proposed algorithm. Finally, the orthogonal test is used to select the optimal parameter combination, and comparative experiments based on tests with different scales are conducted, along with a t-test. The experimental results demonstrate that MOFOA-HS is effective in solving the F-MSRCPSP.

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“…The PFSP is one of the permutation-based problems that has gained more attention in the community due to its wide range of applications, including manufacturing, production planning, textiles, and logistics [20,21]. The PFSP involves scheduling a set of jobs on a set of machines to minimize a specific performance measure.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Lemtenneche,

Bensayah,

Cheriet

2023

Systems

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Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely used in various optimization problems, known for their favorable results. Each generation of EDAs builds a probabilistic model to represent the most promising individuals, and the next generation is created by sampling from this model. The primary challenge in designing such algorithms lies in effectively constructing the probabilistic model. The mutual exclusivity constraint imposes an additional challenge for EDAs to approach permutation-based problems. In this study, we propose a new EDA called Position-Guided Sampling Estimation of Distribution Algorithm (PGS-EDA) specifically designed for permutation-based problems. Unlike conventional approaches, our algorithm focuses on the positions rather than the elements during the sampling phase. We evaluate the performance of our algorithm on the Permutation Flow-shop Scheduling Problem (PFSP). The experiments conducted on various sizes of Taillard instances provide evidence of the effectiveness of our algorithm in addressing the PFSP, particularly for small and medium-sized problems. The comparison results with other EDAs designed to handle permutation problems demonstrate that our PSG-EDA algorithm consistently achieves the lowest Average Relative Percentage Deviation (ARPD) values in 19 out of the 30 instances of sizes 20 and 50 used in the study. These findings validate the superior performance of our algorithm in terms of minimizing the makespan criterion of the PFSP.

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Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

Cited by 5 publications

References 31 publications

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

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