Variable neighborhood-based Cuckoo Search for production routing with time window and setup times

Wu, Gen-Han; Cheng, Chen-Yang; Pourhejazy, Pourya; Fang, Bai-Lyn

doi:10.1016/j.asoc.2022.109191

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…In other words, there are no clear guidelines for designing OBL strategies for different optimization problems [ 126 , 127 ]. Secondly, if the VNS method is implemented too frequently, the population of solutions could be spread over a larger area than necessary [ 128 ].…”

Section: Discussionmentioning

confidence: 99%

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

et al. 2022

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This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.

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Section: Discussionmentioning

confidence: 99%

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

et al. 2022

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“…Its core principles encompass the parasitic breeding style of cuckoos and the Lévy flight mechanism. CS has demonstrated successful applications in various domains, including the flow shop scheduling problem [57,58], production routing problem [59], and production-inventoryrouting problem [37]. For a more comprehensive understanding of the CS, researchers are encouraged to consult the works of [56,59].…”

Section: Hcs-vns Algorithmmentioning

confidence: 99%

“…CS has demonstrated successful applications in various domains, including the flow shop scheduling problem [57,58], production routing problem [59], and production-inventoryrouting problem [37]. For a more comprehensive understanding of the CS, researchers are encouraged to consult the works of [56,59]. To further enhance the efficiency of the local search procedure in CS, we have integrated it with VNS to address our specific problem.…”

Section: Hcs-vns Algorithmmentioning

confidence: 99%

Optimization of Sustainable Supply Chain Network for Perishable Products

Pan,

Shan

2024

Sustainability

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In today’s perishable products industry, the importance of sustainability as a critical consideration has significantly increased. This study focuses on the design of a sustainable perishable product supply chain network (SPPSCN), considering the factors of economics cost, environmental impacts, and social responsibility. The proposed model is a comprehensive production–location–inventory problem optimization framework that addresses multiple objectives, echelons, products, and periods. To solve this complex problem, we introduce three hybrid metaheuristic algorithms: bat algorithm (BA), shuffled frog leaping algorithm (SFLA), and cuckoo search (CS) algorithm, all hybrid with variable neighbourhood search (VNS). Sensitivity to input parameters is accounted for using the Taguchi method to tune these parameters. Additionally, we evaluate and compare these approaches among themselves and benchmark their results against a reference method, a hybrid genetic algorithm (GA) with VNS. The quality of the Pareto frontier is evaluated by six metrics for test problems. The results highlight the superior performance of the bat algorithm with variable neighbourhood search. Furthermore, a sensitivity analysis is conducted to evaluate the impact of key model parameters on the optimal objectives. It is observed that an increase in demand has a nearly linear effect on the corresponding objectives. Moreover, the impact of extending raw material shelf life and product shelf life on these objectives is limited to a certain range. Beyond a certain threshold, the influence becomes insignificant.

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Operational Integration of Supply Chain Activities with Earliness and Tardiness Considerations

Yağmur,

Kesen

2023

Lecture Notes in Computer Science

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Variable neighborhood-based Cuckoo Search for production routing with time window and setup times

Cited by 10 publications

References 31 publications

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

Optimization of Sustainable Supply Chain Network for Perishable Products

Operational Integration of Supply Chain Activities with Earliness and Tardiness Considerations

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