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Emojis And Contract Formation: South West Terminal Ltd V Achter Land & Cattle Ltd [2023] Skkb 116, Kian Peng Soh Jan 2024

Emojis And Contract Formation: South West Terminal Ltd V Achter Land & Cattle Ltd [2023] Skkb 116, Kian Peng Soh

Research Collection Yong Pung How School Of Law

This case note analyses the decision in the Canadian case of South West Terminal Ltd v Achter Land & Cattle Ltd [2023] SKKB 116 and considers how Singapore law would be applied to a situation where emojis are used in the course of negotiations leading up to the formation of a contract.

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Expropriation Of Shares Via The Corporate Constitution, Stephen Bull Jan 2024

Expropriation Of Shares Via The Corporate Constitution, Stephen Bull

Research Collection Yong Pung How School Of Law

Company constitutions sometimes include powers to effect compulsory share acquisitions from members. Where these are introduced into the constitution after incorporation, the amendment, like all constitutional alterations, must be able to satisfy the common law “bona fide test” in order to be valid. The content of this test has been much debated since the first cases a century ago, and differences in view have emerged from the English and Australian courts. While there is no local case law on such expropriations per se, the High Court recently confirmed for the first time the applicability in Singapore of the common law …

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Neoliberal Shifts And Strategies Of Religious Adaptation In Post-War Sri Lanka, Orlando Woods Jan 2024

Neoliberal Shifts And Strategies Of Religious Adaptation In Post-War Sri Lanka, Orlando Woods

Research Collection School of Social Sciences

Neoliberal shifts have brought about a centring of market logics, and a new focus on the individual as consumer. Some religious groups are better able to adapt to these shifts than others, which reveals the changing role of religion in people’s daily lives. This paper explores how the adaptive strategies of religious groups in response to neoliberalisation can trigger a reimagination of the role and value of religion in/to society. I illustrate these ideas through a comparative exploration of how Buddhist elites and evangelical Christians engage with theology, tradition, and the market in Sri Lanka. Whilst Buddhist elites struggle to …

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Singapore’S Emerging Regulatory Approach To Stablecoins, Rachel Phang Jan 2024

Singapore’S Emerging Regulatory Approach To Stablecoins, Rachel Phang

Research Collection Yong Pung How School Of Law

The Monetary Authority of Singapore's vision to develop "an innovative and responsible digital asset ecosystem" has placed stablecoins firmly on the regulatory agenda. Stablecoins, as cryptoassets designed to maintain a stable value, are regarded as having the potential to serve as a tenable medium of exchange for the digital asset ecosystem - so long as they are well-regulated and securely backed. Both this vision of a flourishing digital asset ecosystem, as well as the specter of the instability wrought by the recent algorithmic stablecoin collapse, speak to the need for sound regulation of stablecoins. This article therefore seeks to critically …

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Innovation In Dynamic Knowledge Landscapes: Using Topic Modelling To Map Inventive Activity And Its Implications For Financial Performance, Simon J.D. Schillebeeckx, Yimin Lin, Gerard George Jan 2024

Innovation In Dynamic Knowledge Landscapes: Using Topic Modelling To Map Inventive Activity And Its Implications For Financial Performance, Simon J.D. Schillebeeckx, Yimin Lin, Gerard George

Research Collection Lee Kong Chian School Of Business

We examine the relationship between innovation and performance in the agricultural industry by studying how a firm's patent portfolio position in the knowledge landscape moderates the relationship between three firm search dimensions (scope, specialisation, and commitment) and the firm's financial performance. To represent dynamism in the knowledge landscape, we apply topic modelling to 67,120 patent texts of 571 firms and introduce two complementary perspectives on how knowledge can be dynamically categorised. This allows us to create two representations of the knowledge structure to identify contested (scarce) clusters of high (low) recent inventive activity and emergent clusters where knowledge ambiguity has …

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Heterogeneous Adaptability: Learning, Cash Resources, And The Fine-Grained Adjustment Of Misaligned Governance, Xavier Martin, Ilya Cuypers Jan 2024

Heterogeneous Adaptability: Learning, Cash Resources, And The Fine-Grained Adjustment Of Misaligned Governance, Xavier Martin, Ilya Cuypers

Research Collection Lee Kong Chian School Of Business

Research Summary: When can a firm make fine-grained adjustments to misaligned subsidiary governance? We examine whether and under what conditions a firm will adapt the equity stake it owns in a subsidiary, enabling improved alignment of the stake with the uncertainty in the local environment. We predict that the rate of adaptation of misaligned equity stakes depends on the experiential and vicarious learning from which the firm can draw, and that these learning effects are contingent on possessing fungible slack resources, specifically cash. Using a sample of 726 Japanese-foreign subsidiaries established in 38 host countries over a 21-year period, we …

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Derivatives And Market (Il)Liquidity, Shiyang Huang, Bart Zhou Yueshen, Cheng Zhang Jan 2024

Derivatives And Market (Il)Liquidity, Shiyang Huang, Bart Zhou Yueshen, Cheng Zhang

Research Collection Lee Kong Chian School Of Business

We study how derivatives (with nonlinear payoffs) affect the underlying assets liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors relative delta hedging sensitivity, i.e., the gamma of the derivatives. The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading.

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Language-Related Misunderstanding At Work: What It Is, Why It Occurs And What Organizations Can Do About It, John Fiset, Devasheesh P. Bhave Jan 2024

Language-Related Misunderstanding At Work: What It Is, Why It Occurs And What Organizations Can Do About It, John Fiset, Devasheesh P. Bhave

Research Collection Lee Kong Chian School Of Business

Language is the foundation of human interaction. It plays a central role in facilitating effective communication by allowing people to express their thoughts, share essential information and establish connections with one another.

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Introducing Business School Research And Positive Impact, Howard Thomas Jan 2024

Introducing Business School Research And Positive Impact, Howard Thomas

Research Collection Lee Kong Chian School Of Business

Typically, there are three main priorities, and dimensions, which interact with each other as business schools frame their visions and missions of enhancing management knowledge and producing distinctive management theories and insights. First, the processes of knowledge generation and development to produce high quality, often multi-disciplinary research outputs involving academic faculty, doctoral students and ‘tri-sector’ participants. Second, knowledge dissemination in teaching and learning activities enabling the growth of quality education at undergraduate and postgraduate levels and thus contributing to student intellectual growth and societal socio-economic development and advancement. Third, knowledge transfer through ‘tri-sector’ collaboration, engagement and practice enhancements that is …

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Market For Manipulable Information, Hui Chen, Jian Sun Jan 2024

Market For Manipulable Information, Hui Chen, Jian Sun

Research Collection Lee Kong Chian School Of Business

We study how investors, firms, and information sellers interact in a market with manipulable information. To better predict the firm characteristics they care about, investors can buy a score from a monopolistic information seller, which aggregates signals that are subject to firm manipulation. The average degree of signal manipulability has no effect on the equilibrium, while the uncertainty about manipulability becomes a new source of noise. Its contribution depends on firms' incentive to manipulate the signals, which in turn depends on the equilibrium price sensitivity to the score. The optimal design of the score weighs signal precision against the endogenous …

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Female Ceos And Investment Efficiency In The Vietnamese Market, Jun Myung Song, Chune Young Chung Dec 2023

Female Ceos And Investment Efficiency In The Vietnamese Market, Jun Myung Song, Chune Young Chung

Sim Kee Boon Institute for Financial Economics

This paper proposes female CEOs’ overconfidence and risky behavior stem from gender stereotype threats. Using two subsamples from Vietnam—firms in the Northern and Southern regions—we empirically show that female CEOs in the North, where there is less gender stereotyping, tend to overinvest relative to male CEOs. However, in the South, they are indifferent. Additional analysis reinforces the main finding that female CEOs in the North tend to take more risks even when dealing with market volatility and uncertainty (e.g., the COVID-19 pandemic). Such risky behaviors do not deteriorate firm value but, instead, possibly improve firm performance.

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Partisanship In Loan Pricing, Ramona Dagostino, Janet Gao, Pengfei Ma Dec 2023

Partisanship In Loan Pricing, Ramona Dagostino, Janet Gao, Pengfei Ma

Research Collection Lee Kong Chian School Of Business

Does partisanship influence the way investors price financial assets? Using voter registration data of bankers originating large corporate loans, we show that bankers whose party differs from that of the U.S. President charge 7% higher loan spreads than other bankers. This effect holds regardless of borrowers’ partisanship, and becomes stronger for politically active bankers and when partisan media exhibit greater disagreement. Bankers do not match disproportionately with co-partisan borrowers but they lead syndicates more frequently with co-partisan bankers. Our results are not driven by bank or borrower fundamentals, but suggest that investor optimism, driven by political alignment, shapes asset prices.

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How Commonality Persists? (Through Investors' Sentiment And Attention), Chyng Wen Tee, Raja Velu, Zhaoque Zhou Dec 2023

How Commonality Persists? (Through Investors' Sentiment And Attention), Chyng Wen Tee, Raja Velu, Zhaoque Zhou

Research Collection Lee Kong Chian School Of Business

Studies on commonality generally attribute the variation in asset returns to the variation in order flows. In this research study, we show that order flows do not predict asset returns, rather their relationship have been static over time. Thus we model both returns and the order flows as endogenous variables, and use investors' sentiment and attention as exogenous factors via a reduced-rank regression. We provide empirical evidence to demonstrate that cross-sectional commonality in attention (sentiment) is linearly (nonlinearly) associated with both returns and order flows at the intraday level, while the sentiment and attention measures themselvesexhibit a nonlinear mutual relationship, …

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Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou Dec 2023

Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover, they generate sizable economic values, and their predictability is not spanned by the yield curve. We also observe that the forecasted bond returns are countercyclical, and the magnitude of predictability is stronger during …

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The Persuasive Effect Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee Dec 2023

The Persuasive Effect Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee

Research Collection Lee Kong Chian School Of Business

Artificial intelligence (AI) technology seeks to emulate humans. One aspect is AI-synthesized voices, used in voice assistants (such as Amazon Alexa, Apple Siri, and Google Assistant) to assistive technologies (such as voiceover narration in product videos). For example, there are currently more than 3.25 billion voice assistants; a number that is expected to touch about 8 billion by next year (i.e., 2023) (Statista 2022). With the extensive availability and enhanced accuracy of AI-synthesized voices, consumer research is starting to examine the impact of AI-synthesized voices on consumer information processing and decision making. The extant literature, however, is relatively limited because …

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In Search Of Cryptocurrency Failure, Donglian Ma, Jun Tu, Zhaobo Zhu Dec 2023

In Search Of Cryptocurrency Failure, Donglian Ma, Jun Tu, Zhaobo Zhu

Research Collection Lee Kong Chian School Of Business

This paper explores the determinants of cryptocurrency failure and the pricing of crypto failure risk. We document different significant market- and characteristic-based predictors for coin and token failures. The introduction of Bitcoin futures and the outbreak of COVID19 affect the importance of many predictors. Investors require extra return for bearing high failure risk of crypto assets. The return difference across high and low failure risk crypto assets is not explained by the market, size and momentum factors in the cryptocurrency market. Finally, investors benefit from diversifying into high failure risk crypto assets that is little correlated with the stock market.

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Leveraging In-Store Technology And Ai: Increasing Customer And Employee Efficiency And Enhancing Their Experiences, Dhruv Grewal, Sabine Benoit, Stephanie Noble, Abhijit Guha, Carl-Philip Ahlbom, Jens Nordfalt Dec 2023

Leveraging In-Store Technology And Ai: Increasing Customer And Employee Efficiency And Enhancing Their Experiences, Dhruv Grewal, Sabine Benoit, Stephanie Noble, Abhijit Guha, Carl-Philip Ahlbom, Jens Nordfalt

Research Collection Lee Kong Chian School Of Business

Due to digital innovations, retailing is undergoing radical changes. Scholars have proposed frameworks to address outcomes of implementing technology e.g., an increased customer experience, efficiency gains, consumer or employee acceptance. Existing frameworks concentrate primarily on the consumer perspective, focus on specific technologies (e.g., AI) and covering the customer journey. In contrast, this paper also focuses on the employee perspective, and how technology influences the employee journey. Since the convenience offered by online retailers puts offline retailers under pressure, this research focuses on in-store technology. Based on a comprehensive review of managerial and academic literature and expert interviews, we propose a …

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Effect Of The Announcement Of Human-To-Human Transmission On Teleconsultation Services In China During Covid-19, Mairehaba Maimaitiming, Jingui Xie, Zhichao Zheng, Yongjian Zhu Dec 2023

Effect Of The Announcement Of Human-To-Human Transmission On Teleconsultation Services In China During Covid-19, Mairehaba Maimaitiming, Jingui Xie, Zhichao Zheng, Yongjian Zhu

Research Collection Lee Kong Chian School Of Business

Objectives: Telemedicine enables patients to communicate with physicians effectively, especially during the coronavirus disease (COVID-19) pandemic. However, few studies have explored the use of online health care platforms for a comprehensive range of specialties during the COVID-19 pandemic. This study aimed to investigate how telemedicine services were affected by the announcement of human-to-human transmission in China. Methods: Telemedicine data from haodf.com in China were collected. A difference-in-differences analysis compared the number of telemedicine use and the number of active online physicians for different specialties in 2020 with the numbers in 2019, before and after the announcement of human-to-human transmission. Results: …

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Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon Dec 2023

Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trained language models, have been widely explored for the code search task. However, studies mainly focus on proposing new architectures for ever-better performance on designed test sets but ignore the performance on unseen test data where only natural language queries are available. The same problem in other domains, e.g., CV and NLP, is usually solved by test input selection that …

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Estimating Propensity For Causality-Based Recommendation Without Exposure Data, Zhongzhou Liu, Yuan Fang, Min Wu Dec 2023

Estimating Propensity For Causality-Based Recommendation Without Exposure Data, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Causality-based recommendation systems focus on the causal effects of user-item interactions resulting from item exposure (i.e., which items are recommended or exposed to the user), as opposed to conventional correlation-based recommendation. They are gaining popularity due to their multi-sided benefits to users, sellers and platforms alike. However, existing causality-based recommendation methods require additional input in the form of exposure data and/or propensity scores (i.e., the probability of exposure) for training. Such data, crucial for modeling causality in recommendation, are often not available in real-world situations due to technical or privacy constraints. In this paper, we bridge the gap by proposing …

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Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He Dec 2023

Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We …

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Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo Dec 2023

Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance …

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Benchmarking Foundation Models With Language-Model-As-An-Examiner, Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, Lei Hou Dec 2023

Benchmarking Foundation Models With Language-Model-As-An-Examiner, Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, Lei Hou

Research Collection School Of Computing and Information Systems

Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model’s ability to understand and generate language in a manner similar to humans. Most of these works focus on proposing new datasets, however, we see two main issues within previous benchmarking pipelines, namely testing leakage and evaluation automation. In this paper, we propose a novel benchmarking framework, Language-Model-as-an-Examiner, where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner. Our framework allows for effortless extensibility …

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Efficient Meta Neural Heuristic For Multi-Objective Combinatorial Optimization, Jinbiao Chen, Zizhen Zhang, Te Ye, Zhiguang Cao, Siyuan Chen, Jiahai Wang Dec 2023

Efficient Meta Neural Heuristic For Multi-Objective Combinatorial Optimization, Jinbiao Chen, Zizhen Zhang, Te Ye, Zhiguang Cao, Siyuan Chen, Jiahai Wang

Research Collection School Of Computing and Information Systems

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the …

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Truncated Affinity Maximization: One-Class Homophily Modeling For Graph Anomaly Detection, Hezhe Qiao, Guansong Pang Dec 2023

Truncated Affinity Maximization: One-Class Homophily Modeling For Graph Anomaly Detection, Hezhe Qiao, Guansong Pang

Research Collection School Of Computing and Information Systems

We reveal a one-class homophily phenomenon, which is one prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets, i.e., normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. However, this anomaly-discriminative property is ignored by existing GAD methods that are typically built using a conventional anomaly detection objective, such as data reconstruction. In this work, we explore this property to introduce a novel unsupervised anomaly scoring measure for GAD – local node affinity – that assigns a larger anomaly score to nodes that are …

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A Closer Look At The Security Risks In The Rust Ecosystem, Xiaoye Zheng, Zhiyuan Wan, Yun Zhang, Rui Chang, David Lo Dec 2023

A Closer Look At The Security Risks In The Rust Ecosystem, Xiaoye Zheng, Zhiyuan Wan, Yun Zhang, Rui Chang, David Lo

Research Collection School Of Computing and Information Systems

Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io, as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs …

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Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath …

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Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong Dec 2023

Assessing The Effectiveness Of A Chatbot Workshop As Experiential Teaching And Learning Tool To Engage Undergraduate Students, Kyong Jin Shim, Thomas Menkhoff, Ying Qian Teo, Clement Shi Qi Ong

Research Collection School Of Computing and Information Systems

In this paper, we empirically examine and assess the effectiveness of a chatbot workshop as experiential teaching and learning tool to engage undergraduate students enrolled in an elective course “Doing Business with A.I.” in the Lee Kong Chian School of Business (LKCSB) at Singapore Management University. The chatbot workshop provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity are designed to impart conversation and user-centric design know how and know why to students. A key didactical aspect which informs the design and flow …

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Video Sentiment Analysis For Child Safety, Yee Sen Tan, Nicole Anne Huiying Teo, Ezekiel En Zhe Ghe, Jolie Zhi Yi Fong, Zhaoxia Wang Dec 2023

Video Sentiment Analysis For Child Safety, Yee Sen Tan, Nicole Anne Huiying Teo, Ezekiel En Zhe Ghe, Jolie Zhi Yi Fong, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The proliferation of online video content underscores the critical need for effective sentiment analysis, particularly in safeguarding children from potentially harmful material. This research addresses this concern by presenting a multimodal analysis method for assessing video sentiment, categorizing it as either positive (child-friendly) or negative (potentially harmful). This method leverages three key components: text analysis, facial expression analysis, and audio analysis, including music mood analysis, resulting in a comprehensive sentiment assessment. Our evaluation results validate the effectiveness of this approach, making significant contributions to the field of video sentiment analysis and bolstering child safety measures. This research serves as a …

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Designing Large-Scale Intelligent Collaborative Platform For Freight Forwarders, Pang Jin Tan, Shih-Fen Cheng, Richard Chen Dec 2023

Designing Large-Scale Intelligent Collaborative Platform For Freight Forwarders, Pang Jin Tan, Shih-Fen Cheng, Richard Chen

Research Collection School Of Computing and Information Systems

In this paper, we propose to design a large-scale intelligent collaborative platform for freight forwarders. This platform is based on a mathematical programming formulation and an efficient solution approach. Forwarders are middlemen who procure container capacities from carriers and sell them to shippers to serve their transport requests. However, due to demand uncertainty, they often either over-procure or under-procure capacities. We address this with our proposed platform where forwarders can collaborate and share capacities, allowing one's transport requests to be potentially shipped on another forwarder's container. The result is lower total costs for all participating forwarders. The collaboration can be …

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