Dynamic pickup and delivery problem with transshipments and LIFO constraints

Xiao-feng, XU; Wei, Zhifei

doi:10.1016/j.cie.2022.108835

Cited by 65 publications

(8 citation statements)

References 40 publications

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“…The EM algorithm was used to estimate the model’s parameters and failed to capture the irregularly shaped objects or groups of small objects. Conradsen and Nilsson (1987) , Wei et al (2015) and Xu and Wei (2023) proposed a hybrid method for multi-label object recognition that uses a transfer learning-based approach with four separate CNN models and feature fusion, resulting in higher accuracy than existing techniques. The HCP method struggles with scenes containing overlapping or closely positioned objects since it relies on single-shot detection without precise bounding box localization.…”

Section: Literature Reviewmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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Section: Literature Reviewmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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“…Constraints ( 8) and ( 9) mean the number of vehicles leaving and returning to the depot cannot exceed the number of vehicles owned. Constraint (10) and constraint (11) ensure that the pick-up demand or delivery demand of every vertex is met. Constraint (12) ensures that the load on any edge does not exceed the vehicle's capacity.…”

Section: Mathematical Modelmentioning

confidence: 99%

A green vehicle routing problem with pick-up and delivery based on an improved firefly algorithm

Zhou,

2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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Carbon emissions produced by logistics distribution have an obvious impact on the environment. A critical problem in logistics distribution research is the green vehicle routing problem. This paper investigates a green vehicle routing problem with pick-up and delivery and soft time windows for a single depot. The problem is developed as a single-objective mixed integer nonlinear programming model. The model aims to minimize fuel costs based on vehicle load and penalty costs for violating time windows while satisfying customer pick-up and delivery demands. This paper proposes an improved firefly algorithm based on chaotic initialization and differential evolution to solve the model. The results show that the proposed model minimizes fuel costs while minimizing the increase in penalty costs. Compared with a traditional heuristic algorithm, the improved firefly algorithm has higher solved precision.

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“…This analysis raises the critical questions of whether natural disasters have a considerable effect on agricultural production trade, whether the infrastructure like the transportation system substantially mediates this impact, whether the government and its international ties can fundamentally moderate these effects in China, and whether answering these questions depend on the type of natural disaster and the trade partner country of China (Xu and Wei, 2023).…”

Section: Introductionmentioning

confidence: 99%

Natural disasters and agricultural trade in China: analyzing the role of transportation, government and diplomacy

Zhao,

Cheng,

Chai

2024

CAER

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PurposeNatural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.Design/methodology/approachThis investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.FindingsThe results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.Originality/valueChina should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

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Dynamic pickup and delivery problem with transshipments and LIFO constraints

Cited by 65 publications

References 40 publications

Remote intelligent perception system for multi-object detection

Remote intelligent perception system for multi-object detection

A green vehicle routing problem with pick-up and delivery based on an improved firefly algorithm

Natural disasters and agricultural trade in China: analyzing the role of transportation, government and diplomacy

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