Programme

Schedule

The final programme will be announced in mid-May 2018. A provisional technical programme schedule is as follows:

  • Wed 13 June: 9:00 - 18:15
  • Thu 14 June: 9:00 - 17:30
  • Fri 15 June: 9:00 - 16:45

The schedule of the meeting will provide plenty of opportunities for interaction which we hope everyone will find enriching.

Sessions Schedule

The final programme will be announced in mid-May 2018. A provisional sessions schedule is as follows:

WedThuFri
9:00 Tutorial (Warren B. Powell)9:00 Approximation, Heuristic and Asymptotic Methods 1
9:00 Logistics and Transportation
9:00 Markov Decision Processes 1
9:00 Simulation
10:30 Break10:30 Break10:30 Break
11:00 Queueing Theory 1
11:00 Finance and Risk
11:00 Stochastic Processes
11:00 Logistics and Transportation
11:00 Markov Decision Processes 2
11:00 Computing and Communications
12:30 Lunch12:30 Lunch12:30 Lunch
13:30 Keynote (Margaret Brandeau)13:30 Keynote (Kevin Glazebrook)13:30 Keynote (Kalyan Talluri)
14:30 Break14:30 Break14:30 Open Discussion about Teaching (chaired by Ger Koole)
14:45 Queueing Theory 2
14:45 Machine Learning and Data Science
14:45 Approximation, Heuristic and Asymptotic Methods 2
14:45 Bayesian Models and Approaches
16:15 Break15:30 Break15:45 Break
16:45 Queueing Theory 3
16:45 Healthcare
16:00 Approximation, Heuristic and Asymptotic Methods 3
16:00 Game Theory and Behavior Theory
16:00 End
18:15 End17:30 End 
 18:45 Bus to social dinner

Open Discussion about Teaching

An open discussion themed "Teaching of stochastic modelling in the era of business analytics and data science" chaired by Prof. Ger Koole (VU Amsterdam) will be part of the technical programme, provisionally scheduled on Friday afternoon.

Invited Plenary Talks

There will be three invited plenary talks.

Speaker: Prof. Margaret Brandeau (Stanford University, Department of Management Science and Engineering & Department of Medicine, US)

Title: How Much Detail is Enough? Examining Stochastic Elements in Models to Support Disease Control Policy

Abstract: Many potential public health policies for disease control are evaluated using epidemic models, instantiated using the best available data. Such models attempt to capture, in a stylized way, the complex stochastic interactions of individuals in a population that lead to the spread of communicable diseases. Because of data uncertainty, typical policy studies perform extensive sensitivity analysis on input parameter values. However, structural assumptions in such models, such as the choice of model type and the determination of which stochastic elements to include, might affect model predictions as much as or more than the choice of input parameters. This talk explores the potential implications of structural assumptions on epidemic model predictions and policy conclusions. We present a case study of the effects of a hypothetical HIV vaccine in multiple population subgroups over eight related transmission models, which we sequentially modify to vary over two dimensions: parameter complexity (e.g., the inclusion of age and hepatitis C virus comorbidity) and contact/simulation complexity (e.g., aggregated compartmental vs. individual/disaggregated compartmental vs. network models). We describe the findings of the case study and suggest some guidelines for future model selection. Our qualitative findings are illustrative of broader phenomena and can provide insight for modelers as they consider the appropriate balance of simplicity versus complexity in model structure.

Speaker: Prof. Kevin Glazebrook (Lancaster University Management School, Department of Management Science, UK)

Title: On Radical Extensions to Multi-armed Bandits and to Notions of Indexation

Abstract: It is nearly 50 years since Gittins (and Jones) elucidated solutions to important classes of multi-armed bandit problems (MABs) in the form of index policies. Such policies assign a calibrating index function to each option available at each decision stage and choose the option with maximal current index. There is now a huge literature related to this work and interest in MABs grows apace. The talk will discuss recent work seeking to develop appropriate notions of indexation for radical extensions to MABs. These include

  1. General models for the dynamic allocation of a single resource to a set of stochastic projects which are in competition for it. Here indices emerge as measures of the cost effectiveness of increasing the resource available to a project from a given level when in a given state;

  2. Models for optimal search in which an object is hidden in one of several locations according to a known probability distribution and the goal is to discover the object in minimum expected time by successive searches of individual locations. The work extends a classical result of Blackwell by allowing two search modes- slow and fast- to look for the object;

  3. A model for the effective sourcing of intelligence data when analytical capability is in short supply takes the form of a MAB with finite horizon in which only a small (pre-assigned) number of the bandit rewards observed may be claimed. The goal is to maximise the aggregate expected reward claimed.

In all cases an appropriate indexation emerges from a Lagrangian relaxation of the original problem.

Speaker: Prof. Kalyan Talluri (Imperial College Business School, UK)

Title: Traffic Issues for Rational Drivers

Abstract: Traffic problems and their resolution were an early preoccupation for many Operations Researchers. However, the topic has fallen on the wayside of top OR journals over the last couple of decades. The research in the area now is driven primarily by physicists and civil and traffic engineers where the modelling either has a physics flavour (to take an extreme example, the kinetic gas traffic model) or relies on discrete-event simulations to test out policies.

The advent of driverless cars and vehicle-to-vehicle communications however ought to revive interest in this problem as it has great relevance to practice and requires considerable modelling skill. In this talk we present our recent research on a simple traffic situation---a two-lane highway has one of its lanes blocked, say due to an accident. The traffic on the blocked lane has to merge to the free lane. For each car, this is akin to the classic parking problem but with a velocity decision variable, in addition to the merge decision. This can be formulated as a dynamic program and the optimal policy shown to be of a bi-threshold type. Now, however incentive compatibility comes into play. Drivers are rational and minimize their travel time. Even simple situations with just two cars on the blocked lane can result in a traffic jam (a subgame-perfect equilibrium) because of the dynamics and instantaneous best-response functions. We devise simple policies for central planner based on our insights and compare them with the optimal solutions.

(Joint work with Mihalis Markakis and Dmitrii Tikhonenko (UPF).)

Invited Tutorial

Speaker: Prof. Warren B. Powell (Princeton University, Department of Operations Research and Financial Engineering, US)

Title: Tutorial: A Unified Framework for Optimization under Uncertainty

Abstract: Stochastic optimization is a fragmented field comprised of multiple communities from within operations research (stochastic programming, Markov decision processes, simulation optimization, decision analysis), computer science (reinforcement learning, multiarmed bandit problems), engineering and economics (stochastic optimal control, optimal stopping), statistics (ranking and selection), probability (multiarmed bandit problems), and applied mathematics (stochastic search). In this talk, I will begin by presenting a much-needed canonical framework for stochastic optimization that matches the widely used setting for math programming. I will then identify the major dimensions of this rich class of problems, spanning static to fully sequential problems, offline and online learning, derivative-free and derivative-based algorithms, with special attention given to problems with expensive function evaluations. We divide solution strategies for sequential problems ("dynamic programs") between policy search (searching within a class of functions) and policies based on approximating the impact of a decision now on the future. We further divide each of these two fundamental solution approaches into two subclasses, producing four classes of policies for approaching sequential stochastic optimization problems that covers all the solution strategies that have been used in any of the fields (including whatever is currently being used in practice). We demonstrate that each of these four classes may work best, as well as opening the door to a range of hybrid policies. The goal is to create a single, elegant framework for modeling optimization problems under uncertainty, and a general tool box for designing and testing effective policies in both offline (simulated) and online (real world) settings. Every problem class, as well as the solution strategies, will be illustrated using actual applications.

Contributed Talks

72 abstracts have been accepted for presentation as contributed talks; these will be split into two parallel sessions (as listed on easychair.org, ordered by abstract ID):

 
Kees Kooiman, Frank Phillipson and Alex Sangers. A Classification Framework for Time stamp Stochastic Assignment Problems
Yanlu Zhao and Felix Papier. Heuristics for Call-Center-Based Scheduling of Field Visits
Ger Koole. An LP-based forecasting method for times series with seasonality and trend
Bismark Singh. Approximating Two-Stage Chance Constrained Programs using Bonferroni Inequalities
Benjamin Legros. M/G/1 queue with event-dependent arrival rates
Ronald Reagan Moussitou, Erika Hausenblas and Pani W. Fernando. Rates of convergence for a particle approximation to the solution of the Zakai equation with jump noise
Esmail Amiri. Forecasting nonlinear time series using hybrid Manifold learning based PSR
Esmail Amiri. Forecasting asymmetric discrete and continuous time GARCH processes
Alessandro Balata and Jan Palczewski. Regress-Later Monte Carlo for optimal control of Markov processes
Gabor Lugosi, Mihalis Markakis and Gergely Neu. On the Hardness of Inventory Management with Censored Demand Data
Dong Li, Zhan Pang and Dali Zhang. Dynamic Bid Price Control for Car Rental Network Revenue Management
Rakesh Kumar. An M/M/1/N queuing model with self-regulatory servers and retention of impatient customers
Oktay Karabağ and Barış Tan. Purchasing, production, and sales strategies for a production system with limited capacity and fluctuating sales and purchasing prices
Mustafa Demircioglu, Herwig Bruneel and Sabine Wittevrongel. Discrete-time queues with disasters
Refael Hassin and Adam Nathaniel. A Cyclic Queueing Game
Yonit Barron and Opher Baron. Queueing and Markov Chain Decomposition Approach for Perishability Models: The (S,s) Control Policy with Lead Time
Dong Li, Li Ding and Stephen Stephen Connor. When to Switch? An Index Policy Approach to Resource Scheduling in Emergency Response
Xin Fei, Juergen Branke and Nalan Gulpinar. Efficient Rollout Algorithms for the Pharmaceutical R&D Pipeline Scheduling Problem
Baris Balcioglu and Odysseas Kanavetas. The Call-back Option in the "Sensitive" Markovian Queueing System
Irit Nowik, Refael Hassin and Yair Shaki. On the price of anarchy in a single-server queue with heterogeneous service valuations induced by travel costs
Abhishek Abhishek, Michel Mandjes and Marko Boon. Generalized gap acceptance models for unsignalized intersections
Omololu Stephen Aluko and Henry Mwambi. Statistical methodologies for handling ordinal longitudinal responses with monotone dropout patterns using multiple imputation
Navid Izady. A Clustered Overflow Configuration of Inpatient Beds in Hospitals
Xinan Yang and Arne Strauss. An Approximate Dynamic Programming Approach to Attended Home Delivery Management
Riccardo Mogre and Luca Bertazzi. Dynamic resource allocation for a project with uncertain progress
Ran Snitkovsky and Refael Hassin. Self, Social and Monopoly Optimization in Observable Queues
Luke Rhodes-Leader, Bhakti Stephan Onggo, David J. Worthington and Barry L Nelson. Airline Disruption Management using Symbiotic Simulation and Multi-fidelity Modelling
Ioannis Dimitriou. Queueing models with state-dependent parameters: New results and applications to next generation communication networks
Ad Ridder, Anne van den Broek d'Obrenan, Dennis Roubos and Leen Stougie. Minimizing Bed Occupancy Variance by Scheduling Patients under Uncertainty
Ioannis Dimitriou and Konstantina Katsanou. Stationary analysis of an adaptive two-class retrial system under the join the shortest orbit queue policy
Oualid Jouini, Benjamin Legros, Zeynep Aksin and Ger Koole. Front-office multitasking between service encounters and back-office tasks
Apoorv Saxena, Dieter Claeys and Joris Walraevens. Analysis of maximum age of data in data backup services
Philippe Chevalier, Gilles Merckx, Wenli Peng and Aadhaar Chaturvedi. Group Purchasing with Demand and Technology Level Uncertainty
Afshin Mansouri and Özlem Ergun. Stochastic Scheduling of Vessels' Arrivals at Ports to Reduce Emissions in Maritime Shipping
Lulai Zhu, Giuliano Casale and Iker Perez. Fluid Analysis of Closed Queueing Networks with Discriminatory Processor Sharing
Jacky Li and Sandjai Bhulai. Dynamic Vehicle Routing Problem with New York City Taxi Data
Luk Knapen, Niels Wardenier, Thomas Koch and Elenna Dugundji. Using Observed Route Complexity to Validate Choice Sets for Simulations
Jullian van Kampen, Rob van der Mei and Elenna Dugundji. Predicting bicycle parking behavior using a discrete modelling approach
Enver Yucesan, Roberto Szechtman and Moshe Kress. Efficient Employment of Adaptive Sensors
Athanasia Manou, Pelin Canbolat and Fikri Karaesmen. Heterogeneous strategic customers in a transportation station
Christopher Kirkbride, Kevin Glazebrook, Roberto Szechtman and Jak Marshall. Effective heuristic policies for time-critical intelligence gathering operations
Thomas Koch, Rob van der Mei and Elenna Dugundji. The optimization of traffic count locations in multi-modal networks
Justus Arne Schwarz and Raik Stolletz. Structural properties of time-dependent flow production systems
Kishor Patil, Mohsin Jafri, Dieter Fiems and Andrea Marin. Performance Evaluation of Depth Based Routing in Underwater Sensor Networks
Thomas Lowbridge and David Hodge. A graph patrol problem with locally-observable random attackers
James Grant, David Leslie, Kevin Glazebrook and Roberto Szechtman. Combinatorial Bandits for Multi-Searcher Surveillance Problems
Lee Benson. Direct transmission models for indirectly transmitted environmental pathogens
Bernard Zweers, Sandjai Bhulai and Rob van der Mei. The benefits of preprocessing the stochastic container relocation problem
Dave Worthington and Martin Utley. Stochastic footprints for infinite-server queueing models
Mohamed El-Beltagy and Amnah Al-Johani. Hybrid Spectral Technique for solving SDEs with Combined Uncertainties
Sven van der Kooij, Pia Kempker, Hans van den Berg and Sandjai Bhulai. Optimal battery charging in smart grids with price forecasts
Feray Tunçalp and Lerzan Örmeci. MDP Model for the Preference-Based Appointment Scheduling Problem with Multi-priority Patients
Andrew Clare, Chul Jang and Iqbal Owadally. Optimal Investment and Deferred Annuity Choice with Inflation and Labour Income Risks
James Kim and Mohan Chaudhry. A Novel Way of Treating the Finite-Buffer Queue GI/M/c/N Using Roots
Odysseas Kanavetas, Apostolos Burnetas and Michael Katehakis. Asymptotically Optimal Multi-Armed Bandit Policies under Side Constraints.
Mehdi Amiri-Aref and Jingxin Dong. Dynamic Location-Inventory Optimization for Inland Container Fleet Management with Uncertain Demand
Faruk Akin and E. Lerzan Örmeci. Admission Control In An Intensive Care Unit With Readmissions
Esha Saha and Pradip Kumar Ray. Patient Condition-based Healthcare Inventory Management System: A Markov Decision Process Approach
Emiliano Heyns. Early detection of highway congestion from probe car data
Zimian Zhang and Christopher Kirkbride. Dynamic cash management models with loan opportunities
Javiera Barrera and Guido Lagos. Sharp bounds for the reliability of a k-out-of-n system under dependent failures using cut-off phenomenon techniques.
Alexandros Pasiouras, Apostolos Burnetas and Athanasios Yannacopoulos. Bayesian Estimation of Initial Conditions in an Infinite Dimensional Gaussian Process with Unknown Covariance Operator
Apostolos Burnetas and Odysseas Kanavetas. Adaptive ordering policies for two products with demand substitution
Dieter Fiems, Matthias Deceuninck and Stijn De Vuyst. An appointment game with unobservable schedules
Maria Rieders, Michael Levy and Patrick Emedom-Nnamdi. A Load Dependent Queueing Model for Epidemic Disease Control
Lerzan Ormeci, Evrim Didem Gunes, Odysseas Kanavetas and Christos Vasilakis. Modelling the use of Patient Activation Measure (PAM) in Complex Patient Care
Rob van der Mei, Asparuh Hristov, Joost Bosman and Sandjai Bhulai. A new method for obtaining closed-form approximations for threshold-based optimal policies for Markov Decision Processes using Symbolic Regression
Yoel Yera, Rosa E. Lillo and Pepa Ramirez-Cobo. Simultaneous and Correlated Events Modeled by the Batch Markov-Modulated Poisson Process
Floske Spieksma, Sandjai Bhulai and Dwi Ertiningsih. Monotonicity properties of the single server queue with abandonments and retrials by coupling
Stephen Ford, Kevin Glazebrook and Peter Jacko. Dynamic Allocation of Assets Subject to Failure and Replenishment
Ruben van de Geer, Qingchen Wang and Sandjai Bhulai. Data-driven Consumer Debt Collection via Machine Learning and Approximate Dynamic Programming
Jannik Vogel and Raik Stolletz. Does the future matter? Optimization of time-dependent service systems

Social Dinner

A social dinner will be held on Thursday approx. between 19:00 - 22:00, and will take place in Ashton Hall - a 14th-century mansion recorded in the National Heritage List for England and now the Club House of Lancaster Golf Club - located 3 miles from the campus. There will be a coach service from the campus.

Ashton Hall