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Operations Research, Systems Engineering and Industrial Engineering Commons™
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- Keyword
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- Adaptive large neighborhood search (3)
- Model Representation And Learning Domain Models For Planning (2)
- Multi-agent Planning And Learning (2)
- Reinforcement learning (2)
- Vehicle routing problem (2)
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- API call (1)
- Aircraft allocation (1)
- Alliances (1)
- Android fragmentation (1)
- App analysis (1)
- B2B Last Mile Logistics (1)
- Best Response Planning (1)
- Bus service deployment (1)
- COVID-19 (1)
- City logistics (1)
- Collaboration (1)
- Computational modeling (1)
- Control area network (CAN) (1)
- Corrosion (1)
- Covering location (1)
- Cross-docking (1)
- Cyclic inventory routing problem (1)
- Datasets (1)
- Decoding (1)
- Decomposition algorithm (1)
- Deep learning (1)
- Deep reinforcement learning (1)
- Driving behaviours (1)
- Electric vehicle routing problem (1)
- Electron tubes (1)
Articles 1 - 22 of 22
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan
Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …
Quantum-Inspired Algorithm For Vehicle Sharing Problem, Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau
Quantum-Inspired Algorithm For Vehicle Sharing Problem, Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Recent hardware developments in quantum technologies have inspired a myriad of special-purpose hardware devices tasked to solve optimization problems. In this paper, we explore the application of Fujitsu’s quantum-inspired CMOS-based Digital Annealer (DA) in solving constrained routing problems arising in transportation and logistics. More precisely in this paper, we study the vehicle sharing problem and show that the DA as a QUBO solver can potentially fill the gap between two common methods: exact solvers like Cplex and heuristics. We benchmark the scalability and quality of solutions obtained by DA with Cplex and with a greedy heuristic. Our results show that …
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
Research Collection School Of Computing and Information Systems
An empathetic car that is capable of reading the driver’s emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., …
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances, Jingfeng Yang, Hoong Chuin Lau
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances, Jingfeng Yang, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage …
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Research Collection School Of Computing and Information Systems
We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Research Collection School Of Computing and Information Systems
The Vehicle Routing Problem (VRP) was formally presented to the scientific literature in 1959 by Dantzig and Ramser (DOI:10.1287/mnsc.6.1.80). Sixty years on, the problem is still heavily researched, with hundreds of papers having been published addressing this problem and the many variants that now exist. Many datasets have been proposed to enable researchers to compare their algorithms using the same problem instances where either the best known solution is known or, in some cases, the optimal solution is known. In this survey paper, we provide a list of Vehicle Routing Problem datasets, categorized to enable researchers to have easy access …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
The Multi-Vehicle Cycle Inventory Routing Problem: Formulation And A Metaheuristic Approach, Vincent F. Yu, Audrey Tedja Widjaja, Aldy Gunawan, Pieter Vansteenwegen
The Multi-Vehicle Cycle Inventory Routing Problem: Formulation And A Metaheuristic Approach, Vincent F. Yu, Audrey Tedja Widjaja, Aldy Gunawan, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
This paper presents a new variant of the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) which aims to determine a subset of customers to be visited, the appropriate number of vehicles used, and the corresponding cycle time and route sequence, such that the total cost (e.g. transportation, inventory, and rewards) is minimized. The MV-CIRP is formulated as a mixed-integer nonlinear programming model. We propose a Simulated Annealing (SA) based algorithm to solve the problem. SA is first tested on the available benchmark Single-Vehicle CIRP (SV-CIRP) instances and compared to the state-of-the-art algorithms. SA is then tested on the benchmark MV-CIRP instances …
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Routing problems are very important in intelligent transportation systems. Recently, a number of deep learning-based methods are proposed to automatically learn construction heuristics for solving routing problems. However, these methods do not completely follow Bellman's Principle of Optimality since the visited nodes during construction are still included in the following subtasks, resulting in suboptimal policies. In this article, we propose a novel step-wise scheme which explicitly removes the visited nodes in each node selection step. We apply this scheme to two representative deep models for routing problems, pointer network and transformer attention model (TAM), and significantly improve the performance of …
An Adaptive Large Neighborhood Search For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiawan, Aldy Gunawan
An Adaptive Large Neighborhood Search For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiawan, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This study addresses a variant of the Electric Vehicle Routing Problem with Mixed Fleet, named as the Green Mixed Fleet Vehicle Routing Problem with Realistic Energy Consumption and Partial Recharges. This problem contains three important characteristics — realistic energy consumption, partial recharging policy, and carbon emissions. An adaptive Large Neighborhood Search heuristic is developed for the problem. Experimental results show that the proposed ALNS finds optimal solutions for most small-scale benchmark instances in a significantly faster computational time compared to the performance of CPLEX solver. Moreover, it obtains high quality solutions for all medium- and large-scale instances under a reasonable …
Solving The Winner Determination Problem For Online B2b Transportation Matching Platforms, Hoong Chuin Lau, Baoxiang Li
Solving The Winner Determination Problem For Online B2b Transportation Matching Platforms, Hoong Chuin Lau, Baoxiang Li
Research Collection School Of Computing and Information Systems
We consider the problem of matching multiple shippers and transporters participating in an online B2B last-mile logistics platform in an emerging market. Each shipper places a bid that is made up of multiple jobs, where each job comprises key information like the weight, volume, pickup and delivery locations, and time windows. Each transporter specifies its vehicle capacity, available time periods, and a cost structure. We formulate the mathematical model and provide a Branch-and-Cut approach to solve small-scale problem instances exactly and larger scale instances heuristically using an Adaptive Large Neighbourhood Search approach. To increase the win percentage of both shippers …
Set Team Orienteering Problem With Time Windows, Aldy Gunawan, Vincent F. Yu, Andros Nicas Sutanto, Panca Jodiawan
Set Team Orienteering Problem With Time Windows, Aldy Gunawan, Vincent F. Yu, Andros Nicas Sutanto, Panca Jodiawan
Research Collection School Of Computing and Information Systems
This research introduces an extension of the Orienteering Problem (OP), known as Set Team Orienteering Problem with Time Windows (STOPTW), in which customers are first grouped into clusters. Each cluster is associated with a profit that will be collected if at least one customer within the cluster is visited. The objective is to find the best route that maximizes the total collected profit without violating time windows and time budget constraints. We propose an adaptive large neighborhood search algorithm to solve newly introduced benchmark instances. The preliminary results show the capability of the proposed algorithm to obtain good solutions within …
First Train Timetabling And Bus Service Bridging In Intermodal Bus-And-Train Transit Networks, Liujiang Kang, Hao Li, Huijun Sun, Jianjun Wu, Zhiguang Cao, Nsabimana Buhigiro
First Train Timetabling And Bus Service Bridging In Intermodal Bus-And-Train Transit Networks, Liujiang Kang, Hao Li, Huijun Sun, Jianjun Wu, Zhiguang Cao, Nsabimana Buhigiro
Research Collection School Of Computing and Information Systems
Subway system is the main mode of transportation for city dwellers and is a quite signif-icant backbone to a city's operations. One of the challenges of subway network operation is the scheduling of the first trains each morning and its impact on transfers. To deal with this challenge, some cities (e.g. Beijing) use bus 'bridging' services, temporarily substitut -ing segments of the subway network. The present paper optimally identifies when to start each train and bus bridging service in an intermodal transit network. Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and …
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative Logistics Service Providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a Vehicle Routing Problem with Pickup and Delivery, Time Windows and Location Congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient …
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …
Waste Collection Routing Problem: A Mini-Review Of Recent Heuristic Approaches And Applications, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan
Waste Collection Routing Problem: A Mini-Review Of Recent Heuristic Approaches And Applications, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan
Research Collection School Of Computing and Information Systems
The waste collection routing problem (WCRP) can be defined as a problem of designing a route to serve all of the customers (represented as nodes) with the least total traveling time or distance, served by the least number of vehicles under specific constraints, such as vehicle capacity. The relevance of WCRP is rising due to its increased waste generation and all the challenges involved in its efficient disposal. This research provides a mini-review of the latest approaches and its application in the collection and routing of waste. Several metaheuristic algorithms are reviewed, such as ant colony optimization, simulated annealing, genetic …
Singapore Airlines: Profit Recovery And Aircraft Allocation Models During The Covid-19 Pandemic, Michelle L. F. Cheong, Ulysses M. Z. Chong, Anne N. T. A. Nguyen, Su Yiin Ang, Gabriella P. Djojosaputro, Gordy Adiprasetyo, Kendra L. B. Gadong
Singapore Airlines: Profit Recovery And Aircraft Allocation Models During The Covid-19 Pandemic, Michelle L. F. Cheong, Ulysses M. Z. Chong, Anne N. T. A. Nguyen, Su Yiin Ang, Gabriella P. Djojosaputro, Gordy Adiprasetyo, Kendra L. B. Gadong
Research Collection School Of Computing and Information Systems
COVID-19 has severely impacted the global aviation industry, causing many airlines to downsize or exit the industry. For airlines which attempt to sustain their operations, they will need to respond to the increase in passenger and cargo demand, as countries recover slowly from the crisis due to the availability of vaccines. We built a series of spreadsheet models to first project the COVID-19 recovery rates by countries from 2021 to 2025, then forecast the passenger and cargo demand, using historical data as base figures. Using the financial and operation data, the revenue, expense, and profit can be projected, then an …
Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as …
A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.
A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many …
Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo
Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular apps (with an …