INFORMS 2021 Program Book

INFORMS Anaheim 2021

TC02

TB45 CC Room 213C In Person: Using Drones in Smart Cities General Session Chair: Nima Molavi, Elizabeth City State University, Toledo, OH, 43623, United States 1 - An Exact Algorithm for Drone-truck Cooperative Routing Problem with Deadlines under Travel Time Uncertainty Jaegwan Joo, Hankuk University of Foreign Studies, Yongin-si, Korea, Republic of, Hyunwoo Park, Chungmok Lee We consider a drone-truck cooperative delivery problem with deadlines. The truck carries multiple drones which are deployed to serve customers. Because multiple drones can be used simultaneously, the truck waits until all drones finish the delivery at the spot. Due to the deadlines, we also should determine the visiting sequence of drones for the customers. The travel times of truck is assumed uncertain, for which we employ the robust optimization to protect the solution from late deliveries. An exact branch-and-price approach is developed to tackle the problem. The computational efficiency of the proposed algorithm is compared to the state-of-the-art MIP solvers. 2 - Sustainable Location and Routing Problem of Commercial Hybrid Drones with Stochastic Customer Locations Nima Molavi, Assistant Professor, Elizabeth City State University, Elizabeth City, NC, United States, Yue Zhang, Marcelo J. Alvarado Vargas Commercial drones’ operation often includes uncertainty in customer locations. This research aims to design a sustainable hybrid commercial drone system with stochastic customer locations by minimizing economic, environmental, and social costs. A two-stage approach is used to assist decision-making at strategic, tactical, and operational levels. First, a scenario-based mathematical model is developed to find the optimal station locations, and then a simulation-optimization approach is used to optimize the total number of drones and the routing to each customer. Tuesday Plenary 01 CC Ballroom E /Virtual Theater 1 Plenary: Role of Optimization in Managing Amazon’s Supply Chain Plenary Session 1 - Plenary: Role of Optimization in Managing Amazon’s Supply Chain Huseyin Topaloglu, Cornell Tech, New York, NY, 10044-1501, United States Amazon runs a complex supply chain to manage the journey of each unit of inventory from the warehouses of the vendors to the hands of the customers, as the inventory passes through cross-docks, fulfillment centers, and delivery stations. At each step of this journey, optimization models play a critical role. In this talk, I will give an overview of these optimization models in a way that is biased towards my personal experience. The models operate on different scales in terms of granularity of time, geography, and product groups, which make them particularly difficult to coordinate. Thus, coordination will be a prevalent theme throughout the talk. I will conclude with a more technical discussion based on models that have been abstracted from my work at Amazon. Tuesday, 9:45AM-10:45AM

Tuesday, 11:00AM-12:30PM

TC01 CC Ballroom A / Virtual Theater 1 Hybrid Dynamic Data Driven Application Systems Sponsored: Simulation Society Sponsored Session Chair: Jie Xu, George Mason University, Fairfax, VA, 22030, United States 1 - Dynamic Data Driven Application Systems Jie Xu, George Mason University, Fairfax, VA, 22030, United States The Dynamic Data Driven Applications Systems (DDDAS) presents a paradigm whereby instrumentation data are dynamically integrated into an executing application simulation and in reverse, the executing model controls the instrumentation. DDDAS plays a key role in advancing capabilities in many application areas ranging from aerospace, materials sciences, biosciences, geosciences and space sciences, resilient security, and cyber systems for critical infrastructures. In addition, DDDAS is also driving advances in foundational methods, through system-level (as well as subsystems-level) representations, that include comprehensive principle- and physics-based-models and instrumentation, uncertainty quantification, estimation, observation, sampling, planning and control. This panel brings together a diverse group of leading DDDAS researchers to discuss significance advances that have been made and highlight crosscutting research opportunities. 2 - Panelist Nurcin Celik, University of Miami, Coral Gables, FL, 33146-2509, United States 3 - Panelist Salim Hariri, AZ, United States 4 - Panelist Kevin Jin, University of Arkansas, Fayetteville, AR, 72701, United States 5 - Panelist Chun-Hung Chen, George Mason University, Fairfax, VA, 22030-4422, United States TC02 CC Ballroom B / Virtual Theater 2 Hybrid Location and logistics Sponsored: Location Analysis Sponsored Session Chair: Sibel Alumur Alev, University of Waterloo, Waterloo, ON, N2L 3G1, Canada 1 - Server Positioning and Response Strategies for Spatially Arriving Jobs with Degradation: Light and Medium Traffic Cases Rajan Batta, University at Buffalo (SUNY), NY, 14214-3001, United States, Fatemeh Aarabi This talk studies server positioning and response strategies for spatially arriving jobs with degradation, for situations of light and medium traffic. For the light traffic case it is shown that the p-median solution provides the optimum server positioning, and the optimum response strategy involves no server cooperation. To analyze the medium traffic case, an extended Hypercube queuing model tailored to handle spatially distributed jobs with degradation rate is formulated. The main findings for the medium traffic case are that the degree of server cooperation is strongly related to the rate of job degradation and to the cost of assigning jobs that find all servers busy to a backup server. 2 - Nested-Solution Facility Location Models Ronald McGarvey, University of Missouri, IMSE and TSPA, Columbia, MO, 65211, United States, Andreas Holger Thorsen Classical facility location models can generate solutions that do not maintain consistency in the set of utilized facilities as the number of utilized facilities is varied. We introduce the concept of nested facility locations, in which the solution utilizing p facilities is a subset of the solution utilizing q facilities, for all i ≤ p < q ≤ j, given some lower limit i and upper limit j on r, the number of facilities that will be utilized in the future. This approach is demonstrated with application to the p-median model, with computational testing showing these new models achieve reductions in both average regret and worst-case regret when r <> p facilities are actually utilized.

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