Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

Govindan, Kannan; Jafarian, Ahmad; Nourbakhsh, Vahid

doi:10.1016/j.cor.2018.11.013

Cited by 112 publications

(67 citation statements)

References 90 publications

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“…Related to SI, today is straightforward to find hybridization of SI meta-heuristic schemes with separated local improvement and individual learning mechanisms in the literature. Some examples of this research trend can be found in [34][35][36][37][38].…”

Section: Brief History Of Swarm Intelligencementioning

confidence: 96%

Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications

Osaba¹,

Villar²,

Ser³

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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Section: Brief History Of Swarm Intelligencementioning

confidence: 96%

Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications

Osaba¹,

Villar²,

Ser³

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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“…To address this dilemma, researchers traditionally deploy a metaheuristic procedure, which covers the use of swarmbased algorithms adopted by researchers to deal with various optimization problems. Govindan et al (2019), for example, developed a hybrid swarm-based algorithm using PSO, the electromagnetism-like mechanism algorithm, and artificial bee colony to optimize a biobjective sustainable distribution network. Yamada and Zukhruf (2015) proposed a new variant of PSO to deal with the multimodal supply chain transport supernetwork equilibrium problem.…”

Section: Solution Techniquesmentioning

confidence: 99%

A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design

Zukhruf¹,

Frazila²,

Widhiarso³

2020

IJTech

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This study compared swarm-based algorithms in terms of their effectiveness in improving the design of facilities in container terminals (CTs). The design was conducted within the framework of stochastic discrete optimization and involved determining the number of equipment needed in CTs by considering variations in demand and the productivity of facilities-issues that are rarely elaborated in CT design. Variations were identified via Monte Carlo simulation characterized by a particular distribution. The conflicting issue due to increments in equipment investment that possibly cause the distribution delays was also modeled, specifically in relation to the increasing number of trucks used in terminals. Given that the optimization problem is typified by numerous combinations of actions, the swarm-based algorithms were deployed to develop a feasible solution. A new variant of glowworm swarm optimization (GSO) was then proposed and compared with particle swarm optimization (PSO) algorithms. The numerical results showed that the performance of the proposed GSO is superior to that of PSO algorithms.

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“…Due to the rapid advance of technology and communication, these algorithms have drawn widespread attention and have been successfully applied to many real-world problems [32]. The machine learning approach can be applied to different optimization problems ranging from wind energy decision system [33], socially aware cognitive radio handovers [34], and truck scheduling at cross-docking terminals [35,36] to the sustainable supply chain network integrated with vehicle routing [37]. These studies proved that machine learning can adapt the environmental changes, creating its own knowledge base and adjusting its functionality to make dynamic data and network handover decisions.…”

Section: Image Classification Based On Multi-kernel Learningmentioning

confidence: 99%

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Zeng

2019

Information

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Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel method for road extraction from HRRS images. Multi-kernel learning is first utilized to integrate the spectral, texture, and linear features of images to classify the images into road and non-road groups. A precise extraction method for road elements is then designed by building road shaped indexes to automatically filter out the interference of non-road noises. A series of morphological operations are also carried out to smooth and repair the structure and shape of the road element. Finally, based on the prior knowledge and topological features of the road, a set of penalty factors and a penalty function are constructed to connect road elements to form a complete road network. Experiments are carried out with different sensors, different resolutions, and different scenes to verify the theoretical analysis. Quantitative results prove that the proposed method can optimize the weights of different features, eliminate non-road noises, effectively group road elements, and greatly improve the accuracy of road recognition.

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Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

Cited by 112 publications

References 90 publications

Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications

Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications

A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

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