INFORMS 2021 Program Book
INFORMS Anaheim 2021
MD37
Kenneth Currie A robotic mobile fulfillment (RMF) system is proven to be a solution for the fast- growing, rapid, and dynamic demand of e-commerce. With many advantages on pick time, efficiency, and accuracy, an RMF system is often limited to a one- dimension operation on the floor coupling with heavy utilization of robots. We propose implementing an overhead lifting system to take advantage of the vertical space, increase warehouse density, improve robot utilization, and save space costs. We simulate restricted environments and analyze pick time, picker utilization, and throughput times. MD36 CC Room 210B In Person: Sustainable Urban Logistics/ Urban Freight Operations General Session Chair: Vikrant Vaze, Dartmouth College, Hanover, NH, 03755-3560, United States 1 - Prohibiting Cherry-picking: Regulating Ride-hailing Services Who Choose Service Region, Availability and Fleet Size Layla Martin, Eindhoven University of Technology, Arcisstr. 21, Eindhoven, 80333, Netherlands, Wu Hao Ridehailing is frequently positively attributed, but may also increase congestion and emissions, may negatively impact existing markets, and the service quality of ridehailing may differ within a city. We study different regulations on fleet size, service region, service availability, and rebalancing. We formulate the problem of setting fleet size, service region, and availability jointly with the operational rebalancing decision as a Mixed Integer Second-Order Cone Program. Numerical experiments on artificial and case study instances point towards surprising interdependencies. For example in the case study, requiring equal availability at all locations decreases the fleet size by up to 15.8%, and also decreases the rebalancing activities. 2 - Presenter Ricardo Giesen, Pontificia Universidad Catolica de Chile, 486 Av Vicuna Mackenna, Casilla 306 Cod 105, Trans, Santiago, CP 7820436, Chile Abstract not available MD37 CC Room 210C In Person: New Business Models for Sustainable Operations General Session Chair: Somya Singhvi, USC Marshall School of Business, Los Angeles, CA, 90007, United States 1 - The Impact of Animal Welfare Regulations on Firms Yen-Ting (Daniel) Lin, University of San Diego, Olin Hall, School Of Business Administration Univ, San Diego, CA, 92110, United States, Yingping Mu, Wenli Xiao, Zhiping Lin Factory farming generates pollution and animals are raised in an inhumane environment. Many countries introduce animal welfare regulations to prohibit factory farming. Under these regulations, companies can choose to offer a humane product or an organic product that vary in animal living conditions. We study how such regulations affect firms’ product offering and pricing decisions under competition. We also study the effectiveness of various subsidy policies to support regulatory compliance. 2 - Improving Cash Constrained Smallholder Farmer Welfare: Role Of Government Interventions Somya Singhvi, University of Southern California, Los Angeles, CA, 02139-4230, United States, Kenneth Pay, Yanchong Zheng The need for immediate cash inhibits smallholder farmers from maximizing their revenue by forcing them to sell their produce at suboptimal times. This paper develops a model to examine how cash constraints influence farmers’ sales decisions, as well as to analyze the efficacy of loan programs in improving revenue outcomes. 3 - Detours in Shared Rides Sebastien Martin, Northwestern University, Evanston, IL, United States, Ilan Lobel Detours are considered key for the efficient operation of a shared rides service, but are also the major pain point for consumers of such services. This paper studies the relationship between the value generated by shared rides and the detours they create for riders. We establish a fundamental limit on the sum of value and detour, and prove this leads to a tight bound on the Pareto frontier of
how policy makers should set more appropriate tax rates. 2 - Dynamic Batch Learning with High-Dimensional Covariates: Theory, Algorithm and Application Zhimei Ren, United States We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits. We characterize the fundamental learning limit in this problem and provide a simple, exploration-free algorithm that uses the LASSO estimator and achieves the minimax optimal performance (up to log factors). To our best knowledge, our work provides the first inroad into a rigorous understanding of dynamic batch learning with high-dimensional covariates. We also demonstrate the efficacy of our algorithm on both synthetic data and the Warfarin medical dosing data. 3 - The Important Role of Time Limits when Consumers Choose their Time in Service Pnina Feldman, Boston University, Boston, MA, 02215, United States We examine ways to manage congestion in services where customers choose their service time. Time limits that restrict time spent in service are very attractive levers to regulate congestion. When combined with simple pricing schemes (e.g., per-use fees and price rates), they maximize revenue and social welfare. To maximize consumer surplus, service should be provided for free, but time limits should be set to regulate congestion. Time limits don’t only work well when combined with simple price mechanisms, but they are in fact optimal when congestion is high. Service providers can achieve the first-best outcome and extract all customer surplus by coupling a time limit with an optimal price mechanism. MD35 CC Room 210A In Person: Emerging Topics in Facility Logistics General Session Chair: Leily Farrokhvar, California State University Northridge, Porter Ranch, CA, 91326, United States 1 - Impact Of Route Planning On Workforce Scheduling In Distribution Centers Arpan Rijal, University of Groningen, Groningen, Netherlands, Marco Bijvank, René De Koster When the distribution of ordered items from a warehouse to customers is scheduled, the transportation planning is generally done first and this serves as input for the planning of warehouse operations. However, when the deliveries to customers have time window restrictions and the availability of order picking or staging capacities at the warehouse is limited, the sequential approach of transportation-first-warehousing-second is not only sub-optimal but the routes can also be infeasible for warehouse managers to implement. This paper studies the routing decisions of vehicles to customer locations with hard time windows while considering scheduling of order pickers determining batch size of orders and their sequencing with limited staging. We propose a mathematical model for the integrated problem and propose solution approaches. 2 - Maximum-stability Dispatch Policy for Shared Autonomous Vehicles Based on Zone-based Dynamic Queueing Models TE XU, University of Minnesota, Minneapolis, MN, United States Shared autonomous vehicles (SAVs) are a fleet of autonomous taxis that provide point-to-point services for travelers. But the number of waiting passengers could become arbitrarily large when the fleet size is too small for travel demand, which causes an unstable network. To overcome this, we design a zone-based dynamic queueing model for waiting passengers and a maximum stability dispatch policy for SAVs that when the average number of waiting passengers is bounded in expectation, which is proven by the Lyapunov drift techniques. Then we expand the proof to the existence of exiting passengers. Simulation results show that our dispatch policy can ensure the waiting queues are bounded in expectation. 3 - Maximal Coverage Problem for a Naval Task Group with Random Threat Zeyu Wang, George Washington University, Washington, DC, United States, Miguel Lejeune We present a stochastic programming model with decision-dependent probabilities for air defense coverage and the formation of a naval task group. The problem maximizes the probabilistic coverage level of a naval group. We introduce a load-discounted function to describe the impact of allocation decisions on coverage efficiency. The joint chance constraint optimizes defense coverage under uncertainty of missile attack directions. We use the Boolean framework to obtain a deterministic reformulation of the chance constraint. We derive several valid inequalities to improve computational efficiency. 4 - Increasing Shelving Density in a Robotic Mobile Fulfillment Warehouse Using Overhead Lifting System Leily Farrokhvar, California State University Northridge, 16322 Benjamin Ct, Granada Hills, CA, 91344, United States, Vy Nguyen,
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