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Articles 1441 - 1470 of 1897

Full-Text Articles in Artificial Intelligence and Robotics

Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau Aug 2018

Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is able to perform at humans and even surpass human’s performances in some tasks. Recent cases about self-driving cars, cashier-free supermarket Amazon Go, and virtual assistants such as Apple’s Siri and Google Assistant have illustrated the current and future potential of AI. AI and its applications have infiltrated human’s work and daily life. It is inevitable that humans need to build a working relationship with AI and its applications. On one hand, humans can benefit from this new technology, for instance, a home robot can release housewife from mundane and monotonous tasks (Siau 2017, Siau 2018). On …

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Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau Aug 2018

Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

AI-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data security, data privacy, and ethical problems of AI-based technology also pose significant risks for users, developers, and governments. As the AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. This study will focus on the ethics and morality issues that may be caused by AI, andmay arise because of AI. This research uses …

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Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah Aug 2018

Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Mining is an important industrial and economic sector that plays a major role in the economic development of a country and provides many employment opportunities. Implementation of Artificial Intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with the first application to autonomous trucks. The autonomous technologies provide many economic benefits to the mining industry through cost reduction, productivity improvement, reduction in exposure of workers to hazardous conditions, continuous production, and improved safety. However, implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This paper discusses the current status …

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Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang Jul 2018

Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang

Research Collection School Of Computing and Information Systems

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence …

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Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram Jul 2018

Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram

Research Collection School Of Computing and Information Systems

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …

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The Price Of Usability: Designing Operationalizable Strategies For Security Games, Sara Marie Mccarthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe Jul 2018

The Price Of Usability: Designing Operationalizable Strategies For Security Games, Sara Marie Mccarthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe

Research Collection School Of Computing and Information Systems

We consider the problem of allocating scarce security resources among heterogeneous targets to thwart a possible attack. It is well known that deterministic solutions to this problem being highly predictable are severely suboptimal. To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary. Unfortunately, such mixed strategies typically involve randomizing over a large number of strategies, requiring security personnel to be familiar with numerous protocols, making them hard to operationalize. Motivated by these practical considerations, we propose an easy to use approach for computing strategies that are easy …

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Reserved Optimisation: Handling Incident Priorities In Emergency Response Systems, Muralidhar Konda, Supriyo Ghosh, Pradeep Varakantham Jun 2018

Reserved Optimisation: Handling Incident Priorities In Emergency Response Systems, Muralidhar Konda, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency (medical, fire or criminal) Management Systems(EMSs) are crucial for ensuring public safety and security. Typically in many cities, less than 20% of the cases received by EMSs belong to the extremely serious category and require immediate help. Rest of the incidents typically are less serious and thereby allow more flexibility in response time. Therefore, for efficient management of EMS requests, several EMSs now categorise an incoming emergency request into apriority level based on well studied “triaging” methods. Leading research on optimising emergency response has either focussed on data-driven models for settings with homogenous incidents or on generic heuristics (that …

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Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan Jun 2018

Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Solving combinatorial optimization problems using a fixed set of operators has been known to produce poor quality solutions. Thus, adaptive operator selection (AOS) methods have been proposed. But, despite such effort, challenges such as the choice of suitable AOS method and configuring it correctly for given specific problem instances remain. To overcome these challenges, this work proposes a novel approach known as I-AOS-DOE to perform Instance-specific selection of AOS methods prior to evolutionary search. Furthermore, to configure the AOS methods for the respective problem instances, we apply a Design of Experiment (DOE) technique to determine promising regions of parameter values …

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Analysing Multi-Point Multi-Frequency Machine Vibrations Using Optical Sampling, Dibyendu Roy, Avik Ghose, Tapas Chakravarty, Sushovan Mukherjee, Arpan Pal, Archan Misra Jun 2018

Analysing Multi-Point Multi-Frequency Machine Vibrations Using Optical Sampling, Dibyendu Roy, Avik Ghose, Tapas Chakravarty, Sushovan Mukherjee, Arpan Pal, Archan Misra

Research Collection School Of Computing and Information Systems

Vibration analysis is a key troubleshooting methodology for assessing the health of factory machinery. We propose an unobtrusive framework for at-a-distance visual estimation of such (possibly high frequency) vibrations, using a low fps (frames-per-second) camera that may, for example, be mounted on a worker's smart-glass. Our key innovation is to use an external stroboscopic light source (that, for example, may be provided by an assistive robot), to illuminate the machine with multiple mutually-prime strobing frequencies, and use the resulting aliased signals to efficiently estimate the different vibration frequencies via an enhanced version of the Chinese Remainder Theorem. Experimental results show …

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Disentangled Person Image Generation, Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, Mario Fritz Jun 2018

Disentangled Person Image Generation, Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an …

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Natural And Effective Obfuscation By Head Inpainting, Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz Jun 2018

Natural And Effective Obfuscation By Head Inpainting, Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. …

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Ai: Augmentation, More So Than Automation, Steven M. Miller May 2018

Ai: Augmentation, More So Than Automation, Steven M. Miller

Asian Management Insights

The take-up of Artificial Intelligence (AI)-enabled systems in organisations is expanding rapidly. Integrating AI-enabled automation with people into workplace processes and societal systems is a complex and evolving challenge. The articles takes a managerial perspective on how firms can effectively deploy human minds and intelligent machines in the workplace.

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Assessing Classical And Expressive Aesthetics Of Web Pages Using Machine Learning, Ang Chen, Fiona Fui-Hoon Nah, Langtao Chen May 2018

Assessing Classical And Expressive Aesthetics Of Web Pages Using Machine Learning, Ang Chen, Fiona Fui-Hoon Nah, Langtao Chen

Research Collection School Of Computing and Information Systems

Aesthetics plays a key role in web design. However, most websites are developed based on designers’ "inspirations" or "educated guesses" (Liu, 2003). While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In this research, we propose using machine learning techniques to explore and more fully understand the patterns and underlying principles of aesthetics. We propose using machine learning techniques to develop predictive models for two aesthetic dimensions – classical aesthetics and expressive aesthetics – as well as for overall aesthetics of web pages in order to evaluate the aesthetic quality …

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Artificial Intelligence: A Study On Governance, Policies, And Regulations, Weiyu Wang, Keng Siau May 2018

Artificial Intelligence: A Study On Governance, Policies, And Regulations, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is displacing jobs and creating an upheaval in the world. It will change the way we work and the way we live. What should be the AI governance, policies, and regulations? How can AI governance, policies, and regulations mitigate and alleviate the negative aspects of AI advancement? How will AI governance, policies, and regulations impact the future of work and the future of humanity? This longitudinal multiple case studies research will study the evolution and revolution of AI governance, policies, and regulations, and how governance, policies and regulations impact AI advancement and are impacted by AI advancement. …

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A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau May 2018

A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau

Research Collection School Of Computing and Information Systems

The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …

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Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu Apr 2018

Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve battery-less wearable system. Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts, which suggests that energy harvesting and sensing can be performed concurrently. However, realization of simultaneous energy harvesting and sensing (SEHS) is challenging as the energy harvesting process distorts the sensing signal. In this demo, we propose a novel SEHS architecture prototyped in the form factor of an insole, which combines …

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Sehs: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohan Lan, Weitao Xu, Mahbub Hassan, Wen Hu Apr 2018

Sehs: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohan Lan, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts …

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Findings Of A User Study Of Automatically Generated Personas, Joni Salminen, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen Apr 2018

Findings Of A User Study Of Automatically Generated Personas, Joni Salminen, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We report findings and implications from a semi-naturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organization's social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the system in a use case scenario. We employed a variety of data collection methods, including mouse tracking and survey data, analyzing the data with a mixed method approach. Results show that having an interactive system may aid in keeping personas at the forefront while making customer-centric decisions and indicate that …

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Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak Mar 2018

Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak

Research Collection School Of Computing and Information Systems

To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed …

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Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen Mar 2018

Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen

Research Collection School Of Computing and Information Systems

We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such …

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Hiddencode: Hidden Acoustic Signal Capture With Vibration Energy Harvesting, Guohao Lan, Dong Ma, Mahbub Hassan, Wen Hu Mar 2018

Hiddencode: Hidden Acoustic Signal Capture With Vibration Energy Harvesting, Guohao Lan, Dong Ma, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

The feasibility of using vibration energy harvesting (VEH) as an energy-efficient receiver for short-range acoustic data communication has been investigated recently. When data was encoded in acoustic signal within the energy harvesting frequency band and transmitted through a speaker, a VEH receiver was capable of decoding the data by processing the harvested energy signal. Although previous work created new opportunities for simultaneous energy harvesting and communication using the same hardware, the communication makes annoying sounds as the energy harvesting frequency band lies within the sensitive region of human auditory system. In this work, we present a novel modulation scheme to …

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An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang Mar 2018

An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our first model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we …

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The Way You Move: The Effect Of A Robot Surrogate Movement In Remote Collaboration, Martin Feick, Lora Oehlberg, Anthony Tang, André Miede, Ehud Sharlin Mar 2018

The Way You Move: The Effect Of A Robot Surrogate Movement In Remote Collaboration, Martin Feick, Lora Oehlberg, Anthony Tang, André Miede, Ehud Sharlin

Research Collection School Of Computing and Information Systems

In this paper, we discuss the role of the movement trajectory and velocity enabled by our tele-robotic system (ReMa) for remote collaboration on physical tasks. Our system reproduces changes in object orientation and position at a remote location using a humanoid robotic arm. However, even minor kinematics differences between robot and human arm can result in awkward or exaggerated robot movements. As a result, user communication with the robotic system can become less efficient, less fluent and more time intensive.

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Education In The Age Of Artificial Intelligence: How Will Technology Shape Learning?, Keng Siau Mar 2018

Education In The Age Of Artificial Intelligence: How Will Technology Shape Learning?, Keng Siau

Research Collection School Of Computing and Information Systems

The age of Artificial Intelligence (AI) is here! Higher education needs to prepare students for a world in which AI plays an increasingly dominant role. What are the jobs that can be replaced easily? How would higher education be affected in the age of AI, robotics, machine learning, and automation? How can higher education excel and flourish in the age of AI?

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Building Trust In Artificial Intelligence, Machine Learning, And Robotics, Keng Siau, Weiyu Wang Mar 2018

Building Trust In Artificial Intelligence, Machine Learning, And Robotics, Keng Siau, Weiyu Wang

Research Collection School Of Computing and Information Systems

In this article, we look at trust in artificial intelligence, machine learning (ML), and robotics. We first review the concept of trust in AI and examine how trust in AI may be different from trust in other technologies. We then discuss the differences between interpersonal trust and trust in technology and suggest factors that are crucial in building initial trust and developing continuous trust in artificial intelligence.

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Building Deep Networks On Grassmann Manifolds, Zhiwu Huang, J. Wu, Gool L. Van Feb 2018

Building Deep Networks On Grassmann Manifolds, Zhiwu Huang, J. Wu, Gool L. Van

Research Collection School Of Computing and Information Systems

Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds. In particular, we design full rank mapping layers to transform input Grassmannian data to more desirable ones, exploit re-orthonormalization layers to normalize the resulting matrices, study projection pooling layers to reduce the model complexity in the Grassmannian context, and devise projection mapping layers to respect Grassmannian geometry and meanwhile achieve Euclidean forms for regular output layers. To train the Grassmann networks, …

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Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Desmond Koh Feb 2018

Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Desmond Koh

Research Collection School Of Computing and Information Systems

In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures …

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Risk-Sensitive Stochastic Orienteering Problems For Trip Optimization In Urban Environments, Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh Feb 2018

Risk-Sensitive Stochastic Orienteering Problems For Trip Optimization In Urban Environments, Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh

Research Collection School Of Computing and Information Systems

Orienteering Problems (OPs) are used to model many routing and trip planning problems. OPs are a variantof the well-known traveling salesman problem where the goal is to compute the highest reward path thatincludes a subset of vertices and has an overall travel time less than a specified deadline. However, the applicabilityof OPs is limited due to the assumption of deterministic and static travel times. To that end, Campbellet al. extended OPs to Stochastic OPs (SOPs) to represent uncertain travel times (Campbell et al. 2011). Inthis article, we make the following key contributions: (1) We extend SOPs to Dynamic SOPs (DSOPs), …

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Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein Feb 2018

Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …

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Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau Feb 2018

Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of mitigating congestion and preventing hotspots in busy water areas such as Singapore Straits and port waters. Increasing maritime traffic coupled with narrow waterways makes vessel schedule coordination for just-in-time arrival critical for navigational safety. Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than …

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