2015 Informs Annual Meeting

MB70

INFORMS Philadelphia – 2015

MB69 69-Room 201C, CC Facility Logistics IV Sponsor: TSL/Facility Logistics Sponsored Session Chair: Debjit Roy, Associate Professor, Indian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, 380015, India, debjit@iimahd.ernet.in 1 - Robust Supply Chain Design and Operation under Uncertainty Deniz Tursun, Postdoctoral Research Associate, University of Illinois Urbana Champaign, 3308 Sharp Drive, Champaign, IL, 61822, United States of America, utursu2@illinois.edu Robust supply chain design and operation under uncertainty problems lead to confluence of integer and continuous variables, which call for Mixed-Integer Nonlinear Programming (MINLP) algorithms. We consider a comprehensive random projection algorithm for a subclass of MINLP’s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. 2 - Batching Decisions in Stock-to-Picker Order Picking Debjit Roy, Associate Professor, Indian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, 380015, India, debjit@iimahd.ernet.in, Vibhuti Dhingra, Jennifer Pazour We develop new analytical models to analyze performance of static vs. dynamic batching policies in stock-to-picker order pick systems. In particular, we analyze the effect of batch size, variability in item inter-arrival times, and item commonality on order pick performance. 3 - Branch-and-Price for the Capacitated Mobile Facility Location Problem S. Raghavan, Professor, Smith School of Business & Institute for Systems Research, University of Maryland, College Park, MD, 20742, United States of America, raghavan@umd.edu, Mustafa Sahin, Sibel Salman The Capacitated Mobile Facility Location Problem (CMFLP) is a combinatorial optimization problem with applications in supply chain operations and distribution of medical services. We propose two Mixed Integer Programming formulations for the CMFLP and discuss a branch-and-price algorithm for a set partitioning formulation, where the linear programming relaxation is solved with a column generation procedure. We demonstrate the quality of the algorithm on instances adapted from the literature. 4 - Store Fulfillment for Online Orders: A Planning Model in a Collaborative Store Environment Ming Ni, SUNY Buffalo, 326 Bell Hall, University at Buffalo, Amherst, NY, 14260, United States of America, mingni@buffalo.edu, Arun Hampapur, Qing He, Xuan Liu This study on online order fulfillment aims to identify the seasonal planning dimensions from local retailing outlets perspective. It develops optimization models and heuristic algorithms which solve order assignment and fleet sizing problems to construct the supply chain plan. The numerical examples are derived from same day delivery from a real-world retailer store network. MB70 70-Room 202A, CC Joint Session RAS/TSL/AAS: Real-Time Decision Support Practice Sponsor: Railway Applications Sponsored Session Chair: Ravindra Ahuja, President, Optym, 7600 NW 5th Place, Gainesville, FL, 32607, United States of America, ravindra.ahuja@optym.com 1 - Simulation-guided Optimization Algorithms for Real-time Train Scheduling Pedram Sahba, Senior Systems Engineer, Optym, 7600 NW 5th Place, Gainesville, FL, 32607, United States of America, pedram.sahba@optym.com, Ravindra Ahuja, Abbas Bozorgirad In this presentation, we will describe several algorithms for real-time train scheduling (also known as meet-pass planning) using simulation, mixed integer programming and network optimization techniques including their computational results. These algorithms are in production at a railroad in Australia and we will give a demonstration of the system using these algorithms. We will also share our lessons about how these algorithms evolved, what worked, and what did not work.

5 - Data Mining for Problem-Specific State Space Design in Routing Applications Dirk Mattfeld, Technische Universität Braunschweig, Braunschweig, 38106, Germany, d.mattfeld@tu-braunschweig.de, Ninja Soeffker, Marlin Ulmer We consider a dynamic routing problem, where a vehicle serves customer requests arriving stochastically over time. Due to a time limit, not every request can be served. Rejections are possible to maximize the overall number of served requests. For anticipation of future requests, we apply value function approximation (VFA). For improvement of the approximation process, we combine VFA with data mining operations to derive a problem specific VFA-state space based on the observed problem states.

MB68 68-Room 201B, CC Green Vehicle Routing Sponsor: Transportation, Science and Logistics Sponsored Session

Chair: Mesut Yavuz, Associate Professor, University of Alabama, Alston Hall Box 870226, Tuscaloosa, AL, 35487, United States of America, myavuz@cba.ua.edu 1 - Pathways to Green Logistics: Past, Present and Future of the Green Vehicle Routing Problem (GVRP) Sevgi Erdogan, Faculty Research Associate, University of Maryland-NCSG, 1112 J Preinkert Field House, College Park, MD, 20742, United States of America, serdogan@umd.edu This talk will give a brief background to the GVRP and a review of the variants and extensions to the problem as well as techniques to their modeling and solution. A close analysis of the state of literature will be given. Pathways for future research will be discussed. 2 - The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations Richard Hartl, Professor, University of Vienna, Oskar- Morgenstern-Platz 1, Vienna, Austria, richard.hartl@univie.ac.at, Gerhard Hierman, Jakob Puchinger, Stefan Ropke When routing electrical vehicles, limited battery capacity makes detours to recharging stations necessary. We introduce the E-FSMVRPTW to model decisions regarding the fleet composition and the actual routing including the choice of recharging times and locations. We propose a branch-and-price method as well as an ALNS with an embedded local search and labelling procedure. The effectiveness of the proposed approach is shown on a newly created set of benchmark instances and existing benchmarks. 3 - An Iterated Beam Search Algorithm for the Green Vehicle Routing Problem in Service Fleets Mesut Yavuz, Associate Professor, University of Alabama, external refueling and aims to minimize the total travel distance of a homogeneous fleet. Two mathematical formulations (vehicle flows and set partitioning) are built and a lower bound is obtained for each. Two heuristics (savings and insertion) are adopted for upper bounding. We also present preliminary results of a computational experiment. 4 - Fleet Sizing and Scheduling for Mixed Fleets with Alternative Fuel Vehicles Ismail Capar, Texas A&M University, College Station, TX, United States of America, capar@tamu.edu We present a mixed-integer formulation for a new type of fleet sizing and scheduling problem. The formulation consider special need for refueling of alternative fuel vehicles due to their limited range and/or limited refueling infrastructure, such as availability of charging stations for electric vehicles. We provide results of numerical analysis together with managerial insights. Alston Hall Box 870226, Tuscaloosa, AL, 35487, United States of America, myavuz@cba.ua.edu We present a novel Iterated Beam Search (IBS) solution to the Green Vehicle Routing Problem (GVRP) in Service Fleets. The problem allows internal and

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