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19 pages, 605 KiB

Open AccessArticle

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

by Jingbo Huang, Jiting Li, Yonghao Du, Yanjie Song, Jian Wu, Feng Yao and Pei Wang

Mathematics 2023, 11(23), 4813; https://doi.org/10.3390/math11234813 - 28 Nov 2023

Viewed by 1306

Abstract

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations. Full article

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14 pages, 410 KiB

Open AccessArticle

Type-1 Robotic Assembly Line Balancing Problem That Considers Energy Consumption and Cross-Station Design

by Yuanying Chi, Zhaoxuan Qiao, Yuchen Li, Mingyu Li and Yang Zou

Systems 2022, 10(6), 218; https://doi.org/10.3390/systems10060218 - 15 Nov 2022

Cited by 6 | Viewed by 3165

Abstract

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The “cross-station task” design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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18 pages, 4562 KiB

Open AccessArticle

A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals

by Farrukh Hassan, Lukman Ab. Rahim, Ahmad Kamil Mahmood and Saad Adnan Abed

Symmetry 2022, 14(6), 1253; https://doi.org/10.3390/sym14061253 - 16 Jun 2022

Cited by 16 | Viewed by 3350

Abstract

Acoustic emission (AE) as a non-destructive monitoring method is used to identify small damage in various materials effectively. However, AE signals acquired during the monitoring of oil and gas steel pipelines are always contaminated with noise. A noisy signal can be a threat to the reliability and accuracy of the findings. To address these shortcomings, this study offers a technique based on discrete wavelet transform to eliminate noise in these signals. The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. To obtain the global best solution, a threshold selection approach is proposed by integrating particle swarm optimization and the late acceptance hill-climbing heuristic algorithms. By comparing five common approaches, the superiority of the suggested technique was validated by simulation results. The enhanced thresholding solution based on particle swarm optimization algorithm outperformed others in terms of signal-to-noise ratio and root-mean-square error of denoised AE signals, implying that it is more effective for the detection of AE sources in oil and gas steel pipelines. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Damage Detection, Wavelet Transformation and Applied Mechanics)

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25 pages, 8472 KiB

Open AccessArticle

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

by Ahmed AL-Saffar, Suryanti Awang, Wafaa AL-Saiagh, Ahmed Salih AL-Khaleefa and Saad Adnan Abed

Sensors 2021, 21(21), 7306; https://doi.org/10.3390/s21217306 - 2 Nov 2021

Cited by 13 | Viewed by 4379

Abstract

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods. Full article

(This article belongs to the Section Sensor Networks)

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24 pages, 541 KiB

Open AccessArticle

Extending and Solving the Refrigerated Routing Problem

by Sara Ceschia, Luca Di Gaspero and Antonella Meneghetti

Energies 2020, 13(23), 6214; https://doi.org/10.3390/en13236214 - 26 Nov 2020

Cited by 4 | Viewed by 2558

Abstract

In recent years, cold food chains have shown an impressive growth, mainly due to customers life style changes. Consequently, the transportation of refrigerated food is becoming a crucial aspect of the chain, aiming at ensuring efficiency and sustainability of the process while keeping a high level of product quality. The recently defined Refrigerated Routing Problem (RRP) consists of finding the optimal delivery tour that minimises the fuel consumption for both the traction and the refrigeration components. The total fuel consumption is related, in a complex way, to the distance travelled, the vehicle load and speed, and the outdoor temperature. All these factors depend, in turn, on the traffic and the climate conditions of the region where deliveries take place and they change during the day and the year. The original RRP has been extended to take into account also the total driving cost and to add the possibility to slow down the deliveries by allowing arbitrarily long waiting times when this is beneficial for the objective function. The new RRP is formulated and solved as both a Mixed Integer Programming and a novel Constraint Programming model. Moreover, a Local Search metaheuristic technique (namely Late Acceptance Hill Climbing), based on a combination of different neighborhood structures, is also proposed. The results obtained by the different solution methods on a set of benchmarks scenarios are compared and discussed. Full article

(This article belongs to the Special Issue Refrigeration Systems and Applications 2020)

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26 pages, 388 KiB

Open AccessEditor’s ChoiceArticle

Late Acceptance Hill-Climbing Matheuristic for the General Lot Sizing and Scheduling Problem with Rich Constraints

by Andreas Goerler, Eduardo Lalla-Ruiz and Stefan Voß

Algorithms 2020, 13(6), 138; https://doi.org/10.3390/a13060138 - 9 Jun 2020

Cited by 16 | Viewed by 5815

Abstract

This paper considers the general lot sizing and scheduling problem with rich constraints exemplified by means of rework and lifetime constraints for defective items (GLSP-RP), which finds numerous applications in industrial settings, for example, the food processing industry and the pharmaceutical industry. To address this problem, we propose the Late Acceptance Hill-climbing Matheuristic (LAHCM) as a novel solution framework that exploits and integrates the late acceptance hill climbing algorithm and exact approaches for speeding up the solution process in comparison to solving the problem by means of a general solver. The computational results show the benefits of incorporating exact approaches within the LAHCM template leading to high-quality solutions within short computational times. Full article

(This article belongs to the Special Issue Optimization Algorithms for Allocation Problems)

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