Informs Annual Meeting 2017

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INFORMS Houston – 2017

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use - in the case of IBM’s Quantum Experience, even for free. So what is a quantum computer, and what can it do differently from classical computers? In this tutorial we will describe the paradigm underpinning the behavior of quantum computers, treating quantum computers as purely mathematical objects. We will use a simple gate model and show the basic ideas underlying quantum algorithms more closely related to operations research. This introduction does not require any prior knowledge of physics, and will, in fact, not discuss any quantum physics at all. However, it requires some familiarity with linear algebra on complex vector spaces

372A Transportation Network Analysis Sponsored: Optimization, Network Optimization Sponsored Session Chair: Bahar Cavdar, Middle East Technical University, Atlanta, GA, 30308, United States, bcavdar@metu.edu.tr 1 - A Traveling Repairman Problem with Job Dependencies Feng Qiu, fqiu@anl.gov, Bahar Cavdar Electricity service in power distribution network is often disrupted by various events (such as accidents and extreme weather). Many of the service disruptions are a result of physical damages of electrical components and require repair. We consider a power distribution network restoration problem with repair crew routing. We first present the description of the problem and its variations, and give a formulation in a general form. Then, we discuss the computational complexity of the problem. We present solution approaches and computational results. 2 - A Network-level Lane-based Mesoscopic Traffic State Estimation and Prediction Framework Kerem Demirtas, Arizona State University, 699 S. Mill Ave. Tempe, Brickyard Engineering 553, Tempe, AZ, 85281, United States, kdemirta@asu.edu Microscopic traffic models consider the interaction of individual vehicles with nearby vehicles as well as the transportation infrastructure in a very detailed resolution for a short-term horizon. On the other hand, in mesoscopic models, vehicles are considered either individually or in platoons, and how they move through the links of the network are governed by macroscopic traffic conditions. In this research, we propose a mesoscopic simulation framework that can enable traffic state estimation in a lane-level resolution for a network of connected links with multiple lanes. Preliminary results based on synthetic ground truth data generated by VISSIM are presented. 3 - Multiple Scenario Planning Approach for Two-stage Stochastic Team Orienteering Problems Yongjia Song, Virginia Commonwealth University, 1015 Floyd Avenue, P.O. Box 843083, Richmond, VA, 23284, United States, ysong3@vcu.edu, Marlin Wolf Ulmer, Barrett Thomas, Stein W. Wallace In this talk, we discuss study the multiple scenario planning approach in dynamic vehicle routing problems, from a stochastic programming perspective. In particular, we illustrate our preliminary findings using two-stage stochastic orienteering problems as test instances. 4 - A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows under Fairness and Customer Service Perception Considerations Bahar Cavdar, Middle East Technical Univeristy, Ankara, Turkey, bcavdar@metu.edu.tr, Joel Sokol For vehicle routing problems with time windows, soft time window assumptions provide more opportunity to study the tradeoff between the efficiency-based objectives such as number of vehicles, travel distance and the objectives reflecting the customers’ satisfaction such as total time window violation, number of violations. In this study, we develop a new tabu search heuristic for the vehicle routing problem with soft time windows. In addition to the traditional objectives, we also consider fairness and resulting service quality perception assuming that customers share information through word of mouth communication. 372B A Short Introduction to Quantum Computing for the OR Community Invited: Operations Research and the Future of Computing Invited Session Chair: Lior Horesh, IBM TJ Watson, Ossining, NY, 10562, United States, lhoresh@us.ibm.com Co-Chair: Giacomo Nannicini, IBM T.J. Watson, Yorktown Heights, NY, 10598, United States, giacomo.n@gmail.com 1 - A Short Introduction to Quantum Computing for the OR Community Giacomo Nannicini, IBM.T.J. Watson, 1101 Kitchawan Road, Yorktown Heights, NY, 10598, United States, giacomo.n@gmail.com Quantum computers have usually been considered unattainable devices, but quantum computing has come a long way since its inception in the 1980s. Indeed, universal quantum computers of small size are now available for general TB73

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372C New ICS Sponsored: Computing Sponsored Session

1 - Embedded Code in GAMS - using Python as an Example Michael R. Bussieck, GAMS, Washington, DC, United States, MBussieck@gams.com, Lutz Westermann This talk is about a recent extension of the General Algebraic Modeling System (GAMS): The “Embedded Code” facility. With the data model used by GAMS, code for parallel assignment and equation definition is compact, elegant, and efficient. However, traditional data structures (arrays, lists, ...) are not natively available. Though it is possible to represent such data structures, the GAMS code can become unwieldy or inefficient. To overcome this and other issues, the Embedded Code facility was introduced recently. It allows the use of external code (e.g. Python) to access GAMS symbols in memory, so that the user can concentrate on the task at hand and not the mechanics of moving data in and out of GAMS. 2 - Integrating Optimization Modeling with General-purpose Programming for Efficient and Reliable Application Deployment Robert Fourer, AMPL.Optimization Inc., 2521 Asbury Ave, Evanston, IL, 60201, United States, 4er@ampl.com, Filipe Brandao, Christian Valente We describe a new framework that combines the advantages of modeling in a specialized optimization environment and of coding in a general-purpose programming language. Models are formulated concisely and naturally in AMPL, promoting ease of development, reliability of maintenance, and experimentation with solvers and data sources. APIs for a variety of full-featured programming languages permit AMPL models and scripts to be embedded into complex applications, gaining access to extensive libraries for data management and interface development. We illustrate the possibilities with two roll-cutting examples using Python, and a collaborative optimization environment built in Java. 3 - Optimizing in the Cloud – Deploying Optimization Models on the Cloud with Web Services REST API’s Bjarni Kristjansson, Maximal Software Inc., 2111 Wilson Boulevard, Suite 700, Arlington, VA, 22201, United States, bjarni@maximalsoftware.com Over the past decade the IT has been moving steadfastly towards utilizing software on clouds using Web Services REST API’s. The old traditional way of deploying software on standalone computers is slowly but surely going away. In this presentation we will demonstrate the MPL REST Server, which allows optimization models to be easily deployed on the cloud. By delivering optimization through a standard REST API, which accepts data in JSON format, the optimization becomes purely data-driven. Client applications can now be implemented relatively easily on different client platforms such as mobile/tablets or web sites, using just standard HTML/CSS with JavaScript, or any other preferred programming language. 4 - Min Cost Optimization for an Epic Major League Baseball Road Trip Christopher Santos, United States Navy, OPNAV. N81 Assessment Division, 2000 Navy Pentagon, Rm 4D453, Washington DC, DC, 20350-2000, United States, christopher.p.santos@navy.mil Baseball is America’s Pastime and is the inspiration for many a childhood dream. What better way to be a fan of the game than to get a group of good friends together for an epic road trip to watch a game at all 30 Major League Baseball ballparks? While there exist online tools to minimize the number of days to accomplish such a trip, we wanted to optimize cost, but with our own custom constraints and requirements (read: make the trip fun and not grueling).The initial implementation of our optimization routine was performed using Python and GAMS with the CPLEX solver. We are currently porting over our model to Python and Pyomo with an open-source CBC solver.

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