Informs Annual Meeting 2017

SA52

INFORMS Houston – 2017

SA53

4 - Fostering Innovation: Exploration is not Everybody’s Cup of Tea Vipul Aggarwal, University of Washington, 5263 University Avenue NE, Apt 103, Seattle, WA, 98105, United States, aggarv@uw.edu, Elina Hwang Innovation thrives on the creative application of existing and new ideas to create new products or concepts. The existing research on the ability of analogies to inspire innovative ideas treats the conceptual distance between the analogy and the target problem as a hammer. Our research aims to bring nuance to this hammer of problem-solving. We investigate the impact of analogical distance on the ability of users to ideate in an open innovation setting. We use state-of-the-art NLP techniques of paragraph vectors and structural topic models to construct our feature set. We find that the effects of analogy distance varies with user types and are also contingent upon the source of the analogies. 5 - Social Ties and the Quality of User-generated Content Jin Yue, School of Economics and Management, Tsinghua University, Beijing, 100084, China, jiny.13@sem.tsinghua.edu.cn The social networking mechanism is ubiquitous in content communities. However, the effect of social ties on user contribution behavior is unclear. This study explores how social ties influence the quality of user-generated content from the perspectives of social learning and motivation. Results from a social question-and-answer community reveal that the number of followings of a user enhances the quality of his/her future answers, whereas the number of followers diminishes the quality of such answers. Moreover, the influence of followings comes from their behaviors (i.e., number of answers) rather than their community status (i.e., number of followers and number of agrees). Hueon Lee, PhD Candidate, University of Arkansas, 4121 Bell Engineering Center, 1 University of Arkansas, Fayetteville, AR, 72701, United States, hueonlee@uark.edu, Kelly Sullivan, John A. White Block stacking is a commonly used storage method for palletized loads. As the name implies, unit loads are stacked on top of each other. Stacks are arranged in storage rows having different depths. Given a set of adopted row depths and inventory cycles of product lots, the floor space required for a block stacking storage system is determined by staggering replenishment schedules of multiple lots and assigning a row depth to a product lot. We employ integer programming to obtain the replenishment schedule and assignment of products to row depths to minimize required space. Numerical results are provided for the heuristic algorithm we developed. 2 - Design Challenges and Performance Analysis of the AGV-pick System Kaveh Azadeh, PhD Candidate, Rotterdam School of Management Erasmus University, Burgemeester Oudlaan 50, Mandeville Recently, an AGV-based pick system is developed to minimize the pickers travel time for filling large orders. In such systems, the AGV automatically go to the pick location to wait for the picker to arrive. Once the picker puts the item into a customer’s tote carried by the AGV, the AGV goes to the next location. When the order is complete, the AGV brings it to the depot. Due to a parallel movement among the pickers and the AGVs, analysis of such systems is complex. In this research, we attempt to develop queuing network models to capture the realistic movement of the AGVs and the pickers in the system and develop solution methods for performance evaluation. 3 - Modeling Service Priority at Landside Terminal Resources: Exact Analysis and Approximations Debjit Roy, Indian Institute of Management, Ahmedabad, India, debjit@iima.ac.in We develop an exact approach to model service priority of train over truck containers by one automated stacking crane at landside container terminals using an imbedded Markov chain analysis. We further develop an approximate analysis approach to model service priority of train over truck containers with multiple automated stack cranes. 4 - Inbound Storage Block Stacking Optimization for Vehicle Assembly Factories Yulian Zeng, Georgia Institute of Technology, 2618 Druid Oaks NE, Atlanta, GA, 30329, United States, yulian.zeng@gmail.com We focus on optimizing inbound parts storage center configuration exploiting rack-less block stacking in vehicle assembly factories. Given parts storage and throughput requirements and stacking constraints, the objective is to determine the center size (rows, row length & depth) and to assign parts to stack locations, so as to minimize induced costs. We report on large-scale experimentation for a major-brand carmaker factory. Building T09-41, Rotterdam, 3062PA, Netherlands, azadeh@rsm.nl, Debjit Roy, Debjit RoyB.M. De Koster SA52 361E Storage, Handling, and Picking Sponsored Session 1 - Sizing a Block Stacking Storage System

361F Autonomous Vehicles: From Planning to Vehicle Control Invited: TSL, Intelligent Transportation Systems (ITS) Invited Session

Chair: Michael Levin, The University of Texas at Austin, 7205 Hart Ln, Apt 1002, Austin, TX, 78731, United States, michaellevin@utexas.edu 1 - Optimal Design of Autonomous Vehicle Zones in Transportation Networks Zhibin Chen, University of Michigan, 2244 Saint Francis Drive, Apt 209, Ann Arbor, MI, 48104, United States, chipin@umich.edu, Fang He, Yafeng Yin, Yuchuab Du It is envisioned that in the future government agencies will dedicate certain areas of road networks to autonomous vehicles (AVs) to facilitate the formulation of vehicle platoons. We present a mathematical framework for the optimal design of AV zones in a general network. Since AVs may apply different routing principles outside of and within the AV zones, a novel mixed routing equilibrium model is proposed to capture such behaviors. A mixed-integer bi-level programming model is then formulated to optimize the deployment plan of AV zones. 2 - Auction-based Reservations of Network Trajectories for Autonomous Vehicles Michael Levin, The University of Texas at Austin, 7205 Hart Ln, Apt 1002, Austin, TX, 78731, United States, michaellevin@utexas.edu Auctions have been proposed to determine vehicle access to road infrastructures. Although high value-of-time vehicles may be willing to pay tolls or auction bids, many would rather make a single payment that guarantees a travel time for their entire trip. We present a combinatorial assignment algorithm for reserving space- time trajectories with priority determined by auction. Traffic flow follows the cell transmission model. Reservation-based intersection control is used to ensure that reserved trajectories are followed. Winning vehicles tend to have lower travel times, although variability is high. In addition, the trajectory reservation system reduced overall congestion in the network. 3 - Optimal Geometric Design of Parking Facilities for Autonomous Vehicles Mehdi Nourinejad, University of Toronto, Existing parking facilities are designed according to city guidelines without any accommodation for Autonomous vehicles (AV). With AVs, however, the geometric design of parking facilities will change for two reasons. First, AVs can be parked closer to each other and less space will be taken in total. Second, AVs can be relocated automatically within the parking lot. By optimally relocating the AVs, more vehicles can be packed into each parking facility. This paper addresses the following: (i) what is the optimal geometric design for AV parking facilities, (ii) how to relocate the vehicles within the facility, and (iii) how to stack the AVs in the facility to minimize the expected number of relocations. 4 - A Fuzzy Logic-based Approach for Determining Driving Mode under Partially or Highly Autonomous Vehicle Lin Liu, Chongqing University, Chongqing, China, liulin@cqupt.edu.cn, Yong Hoon Kim, Yongfu Li, Srinivas Peeta A partially or highly autonomous vehicle is capable of being operated in autonomous mode by an on-board self-driving controller, or in manual mode by a human driver. This study proposes a fuzzy logic-based approach to determine a competence level of the human driver and the on-board self-driving controller. The experimental validation shows the effectiveness of the proposed approach to determine the driving mode of autonomous vehicle. 193 Silverlinden Drive, Toronto, ON, L4B4G6, Canada, mehdi.nourinejad@mail.utoronto.ca, Sina Bahrami, Matthew Roorda

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