Article
Agronomy
Jin-Wook Jang, Jong-Hee Lee, Gi-Pou Nam, Sung-Ho Lee
Summary: This article proposes an optimal system for determining the shipping schedule for pigs using a predictive model based on machine learning and big data. The system utilizes photographic and weight measurement data from cameras and weighing machines installed in pig pens to determine the presence of abdominal fat-forming pigs and predict the optimal shipping time based on the weight gain patterns. The proposed system provides a perspective on the body type and weight of pigs during the fattening period, allowing for appropriate shipment based on the fattening status.
Article
Economics
Xianhao Xu, Cheng Chen, Bipan Zou, Hongwei Wang, Zhiwen Li
Summary: This paper investigates the optimal shipping quantity and product pricing strategy of online retailers using the innovative logistics mode of shipping before order making. The study includes building a newsvendor model, exploring closed-form solutions, comparing optimal strategies under different risk attitudes, and analyzing the impact of key parameters. Numerical experiments were conducted to verify theoretical conclusions and analyze the effect of parameters on online retailers' optimal strategies and profits. The results show the importance of avoiding blind price wars and the significant influence of risk aversion and key parameters on optimal strategies.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Engineering, Industrial
Xinxin Ren, Yeming Gong, Yacine Rekik, Xianhao Xu
Summary: In this study, a forecasting-optimisation integrated approach is introduced for optimising multi-items' inventories in pickup points based on big data analysis. The results show that the proposed approach effectively increases profits, especially with the novel algorithm performing better. Additionally, it is found that emergency shipment has a more significant advantage when the pickup point is farther from the warehouse, but the mixture of anticipatory and emergency shipping can contribute to higher profits for online retailers.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Zhou Xu, Feng Li, Zhi-Long Chen
Summary: This article studies a commonly faced shipment consolidation problem for companies that outsource logistics operations. The problem involves delivering orders to their destinations by their committed due times using multiple shipping methods. The article investigates two shipping scenarios and develops analytical results and solution algorithms for each scenario.
MANAGEMENT SCIENCE
(2023)
Article
Management
Jianxiong Zhang, Jing Lu, Guowei Zhu
Summary: This study presents a multi-location inventory model for a firm operating multiple stores selling perishable items. The optimal dynamic pricing and shipment consolidation policies are explored, and it is found that both strategies can increase the firm's profitability, with substitutable effects.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Engineering, Marine
Ayesha Ubaid, Farookh Hussain, Muhammad Saqib
Summary: Demand forecasting plays a crucial role in informed decision-making by predicting future sales with historical data. This study compares three forecasting models for import demand forecasting in the Australian shipping industry, with Prophet identified as the best performer. The research highlights the importance of improved visibility into container shipment demand for industry stakeholders.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiaowen Zhao, Zhuo Sun
Summary: Cooperation between shipping liners and ports is an inevitable trend. The study finds that investments by shipping liners in ports may damage the market share of the ports, especially for larger ports. Additionally, discount strategies by flexible ports do not attract cooperation from shipping liners. The study uses a game model to characterize the decision-making changes in maritime supply chains under different strategies.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Nora Wang, Chieh-Ning Hung
Summary: The rise of the smartphone has led to a booming phone accessory market. Despite the market saturation caused by the high penetration rate of smartphones, the accessory market offers great business opportunities and potential. This study analyzes the optimal decisions of smartphone firms in joining the accessory market using game theory as a model. By considering competition situations and the impact on market demand, pricing, and profit, the study provides insights on the best decisions for firms. The research findings suggest that complementary effects may not always influence firms' willingness to enter the accessory market, and dominant firms are not necessarily more inclined to join than weaker firms when facing different market forces.
Article
Management
Huirong Fan, Moutaz Khouja, Jie Gao, Jing Zhou
Summary: The biggest drawback of online shopping is consumers' uncertainty about the product. Social learning plays a role in helping consumers infer whether a product meets their preferences through positive or negative signals. Retailers can reduce mismatch risk by offering money-back guarantees. This study analyzes retailer's pricing and return policy decisions in the presence of social learning.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Public, Environmental & Occupational Health
Xiwen Bai, Jasmine Siu Lee Lam
Summary: This article presents a study on risk estimation using the value-at-risk (VaR) method. It proposes a copula-based GARCH model to estimate the joint multivariate distribution, which can capture the VaR more successfully. The results have significant implications for shipowners in decision making and risk management.
Article
Engineering, Manufacturing
Yiming Li, Gang Li, Xiajun Amy Pan
Summary: We analyze return shipping insurance (RSI) policies on platforms like JD.com and Taobao.com, where retailers can offer RSI to consumers (RRSI) or allow consumers to purchase RSI themselves (CRSI). The decision to buy CRSI may be influenced by post-purchase regret. Our study investigates the optimal RSI policy for a monopolistic online retailer and insurer, finding that RRSI is offered when return handling costs are low and return shipping costs are moderate, while strong consumer propensity for regret drives the purchase of CRSI. Interestingly, under the optimal RRSI policy, the retailer charges higher prices but experiences expanded consumer demand, while the optimal CRSI policy results in lower prices but decreased demand. Counterintuitively, CRSI can be a win-win-win policy for retailers, insurers, and consumers.
PRODUCTION AND OPERATIONS MANAGEMENT
(2023)
Article
Economics
Yadong Wang, Qiang Meng
Summary: This paper focuses on determining the optimal freight rate for spot containers in order to maximize total profit in the container shipping industry. The study considers uncertainties such as uncertain spot demand volume and remaining ship capacity to improve profit from spot shipping demand.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Business
Casey J. Wichman, Brandon Cunningham
Summary: We estimate the value of time for cyclists by studying their behavior in response to a bikesharing program's pricing structure. Cyclists add time to their trip by stopping at an intermediate station to avoid incurring cost, revealing their value of saving time. The estimated value of time for cycling is close to the minimum wage or about 20% of median incomes. These estimates are smaller than those for cars, likely due to the leisure benefits of cycling.
JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Gongli Luo, Xiaoqing Liu, Felix T. S. Chan
Summary: This paper examines the optimal ordering decisions of a newsvendor in portfolio procurement involving long-term contracts and spot purchases. The study indicates that the newsvendor's optimal ordering quantity changes with market parameters and is significantly influenced by spot price fluctuation in portfolio procurement. The research proposes a method of using relative fluctuation of spot price and long-term contract price, which is more applicable in practice. Numerical experiments verify the results and provide management insights.
Article
Economics
Fatih Karanfil, Axel Pierru
Summary: The study develops a partial equilibrium framework to assess the opportunity cost of domestic oil consumption for an oil-exporting country, finding that the most efficient pricing policy is to set the domestic price equal to the opportunity cost, and evaluates the net welfare gains from reforming domestic oil pricing. A numerical illustration for Saudi Arabia is provided to demonstrate the practical value of the proposed framework.
Article
Engineering, Marine
Ayesha Ubaid, Farookh Hussain, Muhammad Saqib
Summary: Demand forecasting plays a crucial role in informed decision-making by predicting future sales with historical data. This study compares three forecasting models for import demand forecasting in the Australian shipping industry, with Prophet identified as the best performer. The research highlights the importance of improved visibility into container shipment demand for industry stakeholders.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Review
Computer Science, Artificial Intelligence
Hada A. Alsobhi, Rayed A. Alakhtar, Ayesha Ubaid, Omar K. Hussain, Farookh Khadeer Hussain
Summary: A micro-credential is a proof of a student's knowledge, skills, and experience that can be used to advance in a particular field of study in a short amount of time. It has gained popularity in higher education, especially during the Covid-19 pandemic, and many universities are offering courses based on micro-credentials. However, validating these micro-credentials is a complex process, and blockchain technology can provide an efficient solution. This study provides an overview of managing micro-credentials using blockchain technology, comparing existing research and identifying research gaps.
KNOWLEDGE-BASED SYSTEMS
(2023)
Proceedings Paper
Engineering, Electrical & Electronic
Ayesha Ubaid, Ubaid-UR-Rehman, Muhammad Ali Abidi
2012 IEEE ASIA-PACIFIC CONFERENCE ON APPLIED ELECTROMAGNETICS (APACE)
(2012)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)