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Operations Research, Systems Engineering and Industrial Engineering Commons™

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Singapore Management University

2014

Articles 1 - 25 of 25

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Second Order-Response Surface Model For The Automated Parameter Tuning Problem, Aldy Gunawan, Hoong Chuin Lau Dec 2014

Second Order-Response Surface Model For The Automated Parameter Tuning Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Several automated parameter tuning procedures/configurators have been proposed in order to find the best parameter setting for a target algorithm. These configurators can generally be classified into model-free and model-based approaches. We introduce a recent approach which is based on the hybridization of both approaches. It combines the Design of Experiments (DOE) and Response Surface Methodology (RSM) with prevailing model-free techniques. DOE is mainly used for determining the importance of parameters. A First Order-RSM is initially employed to define the promising region for the important parameters. A Second Order-RSM is then built to approximate the center point as well as …

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Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander Nov 2014

Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander

Research Collection School Of Computing and Information Systems

We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time …

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Survey On Wakeup Scheduling For Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Wee-Seng Soh, Hwee-Pink Tan Oct 2014

Survey On Wakeup Scheduling For Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Wee-Seng Soh, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

Advances in energy harvesting technologies and ultra low-power computing and communication devices are enabling the realization of environmentally-powered wireless sensor networks (EPWSNs). Because of limited and dynamic energy supply, EPWSNs are duty-cycled to achieve energy-neutrality, a condition where the energy demand does not exceed the energy supply. Duty cycling entails nodes to sleep and wakeup according to a wakeup scheduling scheme. In this paper, we survey the various wakeup scheduling schemes, with focus on their suitability for EPWSNs. A classification scheme is proposed to characterize existing wakeup scheduling schemes, with three main categories, namely, asynchronous, synchronous, and …

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Hippi Care Hospital: Towards Proactive Business Processes In Emergency Room Services, Kar Way Tan, Venky Shankaraman Oct 2014

Hippi Care Hospital: Towards Proactive Business Processes In Emergency Room Services, Kar Way Tan, Venky Shankaraman

Research Collection School Of Computing and Information Systems

It was 2.35 am on a Saturday morning. Wiki Lim, process specialist from the Process Innovation Centre (PIC) of Hippi Care Hospital (HCH), desperately doodling on her notepad for ideas to improve service delivery at HCH’s Emergency Department (ED). HCH has committed to the public that its ED would meet the service quality criterion of serving 90% of A3 and A4 patients, non-emergency patients with moderate to mild symptoms, within 90 minutes of their arrival at the ED. The ED was not able to meet this performance goal and Dr. Edward Kim, the head of the ED at HCH, had …

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Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau Oct 2014

Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at certain nodes simultaneously. As all nodes are capacitated, if a particular …

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Hybrid Metaheuristics For Solving The Quadratic Assignment Problem And The Generalized Quadratic Assignment Problem, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh, Hoong Chuin Lau Aug 2014

Hybrid Metaheuristics For Solving The Quadratic Assignment Problem And The Generalized Quadratic Assignment Problem, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper presents a hybrid metaheuristic for solving the Quadratic Assignment Problem (QAP). The proposed algorithm involves using the Greedy Randomized Adaptive Search Procedure (GRASP) to construct an initial solution, and then using a hybrid Simulated Annealing and Tabu Search (SA-TS) algorithm to further improve the solution. Experimental results show that the hybrid metaheuristic is able to obtain good quality solutions for QAPLIB test problems within reasonable computation time. The proposed algorithm is extended to solve the Generalized Quadratic Assignment Problem (GQAP), with an emphasis on modelling and solving a practical problem, namely an examination timetabling problem. We found that …

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A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau Aug 2014

A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper presents a generalization of the Orienteering Problem, the Time-Dependent Orienteering Problem (TDOP) which is based on the real-life application of providing automatic tour guidance to a large leisure facility such as a theme park. In this problem, the travel time between two nodes depends on the time when the trip starts. We formulate the problem as an integer linear programming (ILP) model. We then develop various heuristics in a step by step fashion: greedy construction, local search and variable neighborhood descent, and two versions of iterated local search. The proposed metaheuristics were tested on modified benchmark instances, randomly …

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Diversity-Oriented Bi-Objective Hyper-Heuristics For Patrol Scheduling, Mustafa Misir, Hoong Chuin Lau Aug 2014

Diversity-Oriented Bi-Objective Hyper-Heuristics For Patrol Scheduling, Mustafa Misir, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The patrol scheduling problem is concerned with assigning security teams to different stations for distinct time intervals while respecting a limited number of contractual constraints. The objective is to minimise the total distance travelled while maximising the coverage of the stations with respect to their security requirement levels. This paper introduces a hyper-heuristic strategy focusing on generating diverse solutions for a bi-objective patrol scheduling problem. While a variety of hyper-heuristics have been applied to a large suite of problem domains usually in the form of single-objective optimisation, we suggest an alternative approach for solving the patrol scheduling problem with two …

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Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan Aug 2014

Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan

Research Collection School Of Computing and Information Systems

Human’s impact on earth through global warming is more or less an accepted fact. Ocean freight is estimated to contribute 4-5% of global carbon emissions and manufacturing companies can aid in reducing this amount. Many companies that ship goods through full container loads do not have the capabilities to ensure the containers they are using minimizes their carbon footprint. One of the reasons is the choice of non-ideal container sizes for their shipments. This paper provides a mathematical model to minimize companies’ shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes. Using data from a …

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An Empirical Study Of Off-Line Configuration And On-Line Adaptation In Operator Selection, Zhi Yuan, Stephanus Daniel Handoko, Duc Thien Nguyen, Hoong Chuin Lau Aug 2014

An Empirical Study Of Off-Line Configuration And On-Line Adaptation In Operator Selection, Zhi Yuan, Stephanus Daniel Handoko, Duc Thien Nguyen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line …

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Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe Jul 2014

Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …

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Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang Jul 2014

Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang

Research Collection School Of Computing and Information Systems

Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments …

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Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet Jul 2014

Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only …

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Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau Jul 2014

Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.

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Robust Two-Stage Stochastic Linear Programs With Moment Constraints, Sarah Yini Gao, Lingchen Kong, Jie Sun Jun 2014

Robust Two-Stage Stochastic Linear Programs With Moment Constraints, Sarah Yini Gao, Lingchen Kong, Jie Sun

Research Collection Lee Kong Chian School Of Business

We consider the two-stage stochastic linear programming model, in which the recourse function is a worst case expected value over a set of probabilistic distributions. These distributions share the same first- and second-order moments. By using duality of semi-infinite programming and assuming knowledge on extreme points of the dual polyhedron of the constraints, we show that a deterministic equivalence of the two-stage problem is a second-order cone optimization problem. Numerical examples are presented to show non-conservativeness and computational advantage of this approach.

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Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham Jun 2014

Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham

Research Collection School Of Computing and Information Systems

While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, theobjective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RSMDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate …

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A Quantitative Analysis Of Decision Process In Social Groups Using Human Trajectories, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya Krishnan May 2014

A Quantitative Analysis Of Decision Process In Social Groups Using Human Trajectories, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya Krishnan

Research Collection School Of Computing and Information Systems

A group's collective action is an outcome of the group's decision-making process, which may be reached by either averaging of the individual preferences or following the choices of certain members in the group. Our problem here is to decide which decision process the group has adopted given the data of the collective actions. We propose a generic statistical framework to infer the group's decision process from the spatio-temporal data of group trajectories, where each "trajectory" is a sequence of group actions. This is achieved by systematically comparing each agent type's influence on the group actions based on an array of …

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On Understanding Diffusion Dynamics Of Patrons At A Theme Park, Jiali Du, Akshat Kumar, Pradeep Reddy Varakantham May 2014

On Understanding Diffusion Dynamics Of Patrons At A Theme Park, Jiali Du, Akshat Kumar, Pradeep Reddy Varakantham

Research Collection School Of Computing and Information Systems

In this work, we focus on the novel application of learning the diffusion dynamics of visitors among attractions at a large theme park using only aggregate information about waiting times at attractions. Main contributions include formulating optimisation models to compute diffusion dynamics. We also developed algorithm capable of dealing with noise in the data to populate parameters in the optimization model. We validated our approach using cross validation on a real theme park data set. Our approach provides an accuracy of about 80$% for popular attractions, providing solid empirical support for our diffusion models.

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Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein May 2014

Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments …

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Predictive Analytics For Outpatient Appointments, Nang Laik Ma, Khataniar Seemanta, Dan Wu, Serene Seng Ying Ng Apr 2014

Predictive Analytics For Outpatient Appointments, Nang Laik Ma, Khataniar Seemanta, Dan Wu, Serene Seng Ying Ng

Research Collection School Of Computing and Information Systems

Healthcare is a very important industry where analytics has been applied successfully to generate insights about patients, identify bottleneck and to improve the business efficiency. In this paper, we aim to look at the patient appointment process as the hospital is experiencing high volume of ?no shows. ?No shows have a high impact on longer appointment lead time for patients, poor patient satisfaction and loss of revenue for hospital. We use data analytics to identify pattern of ?no shows, develop a statistical model to predict the probability of ?no shows and finally operationalizing the model to embed the analytics solution …

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Sampled Fictitious Play For Multi-Action Stochastic Dynamic Programs, Archis Ghate, Shih-Fen Cheng, Stephen Baumert, Daniel Reaume, Dushyant Sharma, Robert L. Smith Mar 2014

Sampled Fictitious Play For Multi-Action Stochastic Dynamic Programs, Archis Ghate, Shih-Fen Cheng, Stephen Baumert, Daniel Reaume, Dushyant Sharma, Robert L. Smith

Research Collection School Of Computing and Information Systems

We introduce a class of finite-horizon dynamic optimization problems that we call multi-action stochastic dynamic programs (DPs). Their distinguishing feature is that the decision in each state is a multi-dimensional vector. These problems can in principle be solved using Bellman's backward recursion. However, complexity of this procedure grows exponentially in the dimension of the decision vectors. This is called the curse of action-space dimensionality. To overcome this computational challenge, we propose an approximation algorithm rooted in the game theoretic paradigm of Sampled Fictitious Play (SFP). SFP solves a sequence of DPs with a one-dimensional action-space, which are exponentially smaller than …

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Building Thinc: User Incentivization And Meeting Rescheduling For Energy Savings, Jun Young Kwak, Debarun Kar, William Haskell, Pradeep Reddy Varakantham, Milind Tambe Feb 2014

Building Thinc: User Incentivization And Meeting Rescheduling For Energy Savings, Jun Young Kwak, Debarun Kar, William Haskell, Pradeep Reddy Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

This paper presents THINC, an agent developed for saving energy in real-world commercial buildings. While previous work has presented techniques for computing energy-efficient schedules, it fails to address two issues, centered on human users, that are essential in real-world agent deployments: (i) incentivizing users for their energy saving activities and (ii) interacting with users to reschedule key “energy-consuming” meetings in a timely fashion, while handling the uncertainty in such interactions. THINC addresses these shortcomings by providing four new major contributions. First, THINC computes fair division of credits from energy savings. For this fair division, THINC provides novel algorithmic advances for …

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Generic Instance-Specific Automated Parameter Tuning Framework, Linda Lindawati Jan 2014

Generic Instance-Specific Automated Parameter Tuning Framework, Linda Lindawati

Dissertations and Theses Collection (Open Access)

Meta-heuristic algorithms play an important role in solving combinatorial optimization problems (COP) in many practical applications. The caveat is that the performance of these meta-heuristic algorithms is highly dependent on their parameter configuration which controls the algorithm behaviour. Selecting the best parameter configuration is often a difficult, tedious and unsatisfying task. This thesis studies the problem of automating the selection of good parameter configurations. Existing approaches to address the challenges of parameter configuration can be classified into one-size-fits-all and instance-specific approaches. One-size-fits-all approaches focus on finding a single best parameter configuration for a set of problem instances, while instance-specific approaches …

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Risk Minimization Of Disjunctive Temporal Problem With Uncertainty, Hoong Chuin Lau, Tuan Anh Hoang Jan 2014

Risk Minimization Of Disjunctive Temporal Problem With Uncertainty, Hoong Chuin Lau, Tuan Anh Hoang

Research Collection School Of Computing and Information Systems

The Disjunctive Temporal Problem with Uncertainty (DTPU) is a fundamental problem that expresses temporal reasoning with both disjunctive constraints and contingency. A recent work (Peintner et al, 2007) develops a complete algorithm for determining Strong Controlla- bility of a DTPU. Such a notion that guarantees 100% confidence of execution may be too conservative in practice. In this paper, following the idea of (Tsamardinos 2002), we are interested to find a schedule that minimizes the risk (i.e. probability of failure) of executing a DTPU. We present a problem decomposition scheme that enables us to compute the probability of failure efficiently, followed …

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Strategic Decision Support System Using Heuristic Algorithm For Practical Outlet Zones Allocation To Dealers In A Beer Supply Distribution Network, Michelle Lee Fong Cheong Jan 2014

Strategic Decision Support System Using Heuristic Algorithm For Practical Outlet Zones Allocation To Dealers In A Beer Supply Distribution Network, Michelle Lee Fong Cheong

Research Collection School Of Computing and Information Systems

We consider a two-echelon beer supply distribution network with the brewer replenishing the dealers and the dealers serving the outlet zones directly, for multiple product types. The allocation of the outlet zones to the dealers will determine the quantity of products the brewer replenishes each dealer, which will in turn impact the total warehousing and transportation costs. The non-linear optimization model formulated is difficult to solve to optimality, and the model itself does not include practical business considerations in the distribution business. A heuristics algorithm is designed and easily implemented using spreadsheets with Visual Basic programming to effectively and efficiently …

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