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Simulation Model To Evaluate Effectiveness Of Queue Management Tool In Supermarket Retail Chain, Michelle L. F. Cheong, Yong Qing Chia
Simulation Model To Evaluate Effectiveness Of Queue Management Tool In Supermarket Retail Chain, Michelle L. F. Cheong, Yong Qing Chia
Research Collection School Of Computing and Information Systems
As part of its continuous process to improve operational excellence and productivity, a retail company in South East Asia trialed the usage of a commercial market retail tool, known as Queue Buster, to improve queue management processes in its physical stores. We employed Queuing Theory to determine the effectiveness of implementing the Queue Buster. Specifically, we constructed a queuing simulation model based on input parameters derived from queuing data collected at a pilot store. Three main performance metrics - Wait Time, System Time and System Length were measured fortwo different queue systems, with and without the implementation of Queue Buster. …
Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng
Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng
Research Collection School Of Computing and Information Systems
Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models …
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. …
Influence, Information And Team Outcomes In Large Scale Software Development, Subhajit Datta
Influence, Information And Team Outcomes In Large Scale Software Development, Subhajit Datta
Research Collection School Of Computing and Information Systems
It is widely perceived that the egalitarian ecosystems of large scale open source software development foster effective team outcomes. In this study, we question this conventional wisdom by examining whether and how the centralization of information and influence in a software development team relate to the quality of the team's work products. Analyzing data from more than a hundred real world projects that include development activities over close to a decade, involving 2000+ developers, who collectively resolve more than two hundred thousand defects through discussions covering more than six hundred thousand comments, we arrive at statistically significant evidence indicating that …
An Iot-Driven Smart Cafe Solution For Human Traffic Management, Maruthi Prithivirajan, Kyong Jin Shim
An Iot-Driven Smart Cafe Solution For Human Traffic Management, Maruthi Prithivirajan, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this study, we present an IoT-driven solution for human traffic management in a corporate cafe. Using IoT sensors, our system monitors human traffic in a physical cafe located at a large international corporation located in Singapore. The backend system analyzes the streaming data from the sensors and provides insights useful to the cafe visitors as well as the cafe manager.
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within …
Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
Research Collection School Of Computing and Information Systems
Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for …
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …
An Empirical Study Of Sms One-Time Password Authentication In Android Apps, Siqi Ma, Runhan Feng, Juanru Li, Yang Liu, Surya Nepal, Elisa Bertino, Robert H. Deng, Zhuo Ma, Sanjay Jha
An Empirical Study Of Sms One-Time Password Authentication In Android Apps, Siqi Ma, Runhan Feng, Juanru Li, Yang Liu, Surya Nepal, Elisa Bertino, Robert H. Deng, Zhuo Ma, Sanjay Jha
Research Collection School Of Computing and Information Systems
A great quantity of user passwords nowadays has been leaked through security breaches of user accounts. To enhance the security of the Password Authentication Protocol (PAP) in such circumstance, Android app developers often implement a complementary One-Time Password (OTP) authentication by utilizing the short message service (SMS). Unfortunately, SMS is not specially designed as a secure service and thus an SMS One-Time Password is vulnerable to many attacks. To check whether a wide variety of currently used SMS OTP authentication protocols in Android apps are properly implemented, this paper presents an empirical study against them. We first derive a set …
Do My Students Understand? Automated Identification Of Doubts From Informal Reflections, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Do My Students Understand? Automated Identification Of Doubts From Informal Reflections, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
Traditionally teaching is usually one directional where the instructor imparts knowledge and there is minimal interaction between learners and instructor. With the focus on learner-centred pedagogy, it can be a challenge to provide timely and relevant guidance to individual learners according to their levels of understanding. One of the options available is to collect reflections from learners after each lesson to extract relevant and high-value feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived an approach to automate the identification of doubts from the informal reflections through features analysis and machine …
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid
Research Collection School Of Computing and Information Systems
Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based …
Quantum Consensus, Jorden Seet, Paul Griffin
Quantum Consensus, Jorden Seet, Paul Griffin
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is …
Objective Sleep Quality As A Predictor Of Mild Cognitive Impairment In Seniors Living Alone, Brian Chen, Hwee-Pink Tan, Irus Rawtaer, Hwee Xian Tan
Objective Sleep Quality As A Predictor Of Mild Cognitive Impairment In Seniors Living Alone, Brian Chen, Hwee-Pink Tan, Irus Rawtaer, Hwee Xian Tan
Research Collection School Of Computing and Information Systems
Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction …
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Research Collection School Of Computing and Information Systems
Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
Research Collection School Of Computing and Information Systems
A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …
Computing Maximum And Minimum With Privacy Preservation And Flexible Access Control, Wenxiu Ding, Zheng Yan, Xinren Qian, Robert H. Deng
Computing Maximum And Minimum With Privacy Preservation And Flexible Access Control, Wenxiu Ding, Zheng Yan, Xinren Qian, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the fast development of Internet of Things, huge volume of data is being collected from various sensors and devices, aggregated at gateways, and processed in the cloud. Due to privacy concern, data are usually encrypted before being outsourced to the cloud. However, encryption seriously impedes both computation over the data and sharing of the computation results. Computing maximum and minimum among a data set are two of the most basic operations in machine learning and data mining algorithms. In this paper, we study how to compute maximum and minimum over encrypted data and control the access to the computation …
Ssrgd: Simple Stochastic Recursive Gradient Descent For Escaping Saddle Points, Zhize Li
Ssrgd: Simple Stochastic Recursive Gradient Descent For Escaping Saddle Points, Zhize Li
Research Collection School Of Computing and Information Systems
We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points. We show that a simple perturbed version of stochastic recursive gradient descent algorithm (called SSRGD) can find an $(\epsilon,\delta)$-second-order stationary point with $\widetilde{O}(\sqrt{n}/\epsilon^2 + \sqrt{n}/\delta^4 + n/\delta^3)$ stochastic gradient complexity for nonconvex finite-sum problems. As a by-product, SSRGD finds an $\epsilon$-first-order stationary point with $O(n+\sqrt{n}/\epsilon^2)$ stochastic gradients. These results are almost optimal since Fang et al. [2018] provided a lower bound $\Omega(\sqrt{n}/\epsilon^2)$ for finding …
Appmod: Helping Older Adults Manage Mobile Security With Online Social Help, Zhiyuan Wan, Lingfeng Bao, Debin Gao, Eran Toch, Xin Xia, Tamir Mendel, David Lo
Appmod: Helping Older Adults Manage Mobile Security With Online Social Help, Zhiyuan Wan, Lingfeng Bao, Debin Gao, Eran Toch, Xin Xia, Tamir Mendel, David Lo
Research Collection School Of Computing and Information Systems
The rapid adoption of Smartphone devices has caused increasing security and privacy risks and breaches. Catching up with ever-evolving contemporary smartphone technology challenges leads older adults (aged 50+) to reduce or to abandon their use of mobile technology. To tackle this problem, we present AppMoD, a community-based approach that allows delegation of security and privacy decisions a trusted social connection, such as a family member or a close friend. The trusted social connection can assist in the appropriate decision or make it on behalf of the user. We implement the approach as an Android app and describe the results of …
Tracking Political Events In Social Media: A Case Study Of Hong Kong Protests, Haodi Qi, Hanyu Jiang, Wende Bu, Chengzi Zhang, Kyong Jin Shim
Tracking Political Events In Social Media: A Case Study Of Hong Kong Protests, Haodi Qi, Hanyu Jiang, Wende Bu, Chengzi Zhang, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this study, we analyze social conversations about Hong Kong Protests, a series of events that were widely seen and debated in social media in 2019. Our system collects data from Twitter and Reddit using their APIs. It performs sentiment analysis, and the analysis results show changes in public sentiment around major events. Social network analysis reveals influencers - users that are in the center of social conversations. Our interactive Tableau dashboard allows the user to easily monitor live social conversations about Hong Kong protests.
Punctuation Prediction For Vietnamese Texts Using Conditional Random Fields, Hong Quang Pham, Binh T. Nguyen, Nguyen Viet Cuong
Punctuation Prediction For Vietnamese Texts Using Conditional Random Fields, Hong Quang Pham, Binh T. Nguyen, Nguyen Viet Cuong
Research Collection School Of Computing and Information Systems
We investigate the punctuation prediction for the Vietnamese language. This problem is crucial as it can be used to add suitable punctuation marks to machine-transcribed speeches, which usually do not have such information. Similar to previous works for English and Chinese languages, we formulate this task as a sequence labeling problem. After that, we apply the conditional random field model for solving the problem and propose a set of appropriate features that are useful for prediction. Moreover, we build two corpora from Vietnamese online news and movie subtitles and perform extensive experiments on these data. Finally, we ask four volunteers …
Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa
Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models …
Strongly Leakage Resilient Authenticated Key Exchange, Revisited, Guomin Yang, Rongmao Chen, Yi Mu, Willy Susilo, Guo Fuchun, Jie Li
Strongly Leakage Resilient Authenticated Key Exchange, Revisited, Guomin Yang, Rongmao Chen, Yi Mu, Willy Susilo, Guo Fuchun, Jie Li
Research Collection School Of Computing and Information Systems
Authenticated Key Exchange (AKE) protocols allow two (or multiple) parties to authenticate each other and agree on a common secret key, which is essential for establishing a secure communication channel over a public network. AKE protocols form a central component in many network security standards such as IPSec, TLS/SSL, and SSH. However, it has been demonstrated that many standardized AKE protocols are vulnerable to side-channel and key leakage attacks. In order to defend against such attacks, leakage resilient (LR-) AKE protocols have been proposed in the literature. Nevertheless, most of the existing LR-AKE protocols only focused on the resistance to …
Automating Change-Level Self-Admitted Technical Debt Determination, Meng Yan, Xin Xia, Emad Shihab, David Lo, Jianwei Yin, Xiaohu Yang
Automating Change-Level Self-Admitted Technical Debt Determination, Meng Yan, Xin Xia, Emad Shihab, David Lo, Jianwei Yin, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Self-Admitted Technical Debt (SATD) refers to technical debt that is introduced intentionally. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is more beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, and identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. In this paper, we propose a change-level SATD Determination mode by extracting 25 features from software changes that are …
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), …
Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data, Xiancai Tian, Baihua Zheng
Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data, Xiancai Tian, Baihua Zheng
Research Collection School Of Computing and Information Systems
In this paper, we aim at studying the group travel behaviour (GTB) patterns from large-scale auto fare collection (AFC) data. GTB is defined as two or more commuters intentionally and regularly traveling together from an origin to a destination. We propose a method to identify GTB accurately and efficiently and apply our method to the Singapore AFC dataset to reveal the GTB patterns of Singapore commuters. The case study proves that our method is able to identify GTB patterns more accurately and efficiently than the state-of-the-art.
Compositional Verification Of Heap-Manipulating Programs Through Property-Guided Learning, Long H. Pham, Jun Sun, Quang Loc Le
Compositional Verification Of Heap-Manipulating Programs Through Property-Guided Learning, Long H. Pham, Jun Sun, Quang Loc Le
Research Collection School Of Computing and Information Systems
Analyzing and verifying heap-manipulating programs automatically is challenging. A key for fighting the complexity is to develop compositional methods. For instance, many existing verifiers for heap-manipulating programs require user-provided specification for each function in the program in order to decompose the verification problem. The requirement, however, often hinders the users from applying such tools. To overcome the issue, we propose to automatically learn heap-related program invariants in a property-guided way for each function call. The invariants are learned based on the memory graphs observed during test execution and improved through memory graph mutation. We implemented a prototype of our approach …
Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma
Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma
Research Collection School Of Computing and Information Systems
In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, …
Smu Teaching Bank: Case Study Of A Multiyear Development Project Utilizing Student Resources, Alan Megargel, Terence P. C. Fan, Venky Shankararaman
Smu Teaching Bank: Case Study Of A Multiyear Development Project Utilizing Student Resources, Alan Megargel, Terence P. C. Fan, Venky Shankararaman
Research Collection School Of Computing and Information Systems
A domain refers to a business sector such as banking, healthcare, insurance, manufacturing etc. For an IS student, it is imperative that the domain knowledge includes a comprehension and understanding of business processes, technology and data related to the chosen domain. For example, when learning the retail banking domain, an IS student must have an understanding of the transactions concerned with retail banking such as fund transfers and loan repayments. The student must also gain a strong foothold in transaction fulfilment processes, the various application services that are used, the data that is transferred, etc. Teaching domain knowledge is very …
Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo
Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo
Research Collection School Of Computing and Information Systems
Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through …
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen
Research Collection School Of Computing and Information Systems
Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …