More Like this

1. Supplier Menus for Dynamic Matching in Peer-to-Peer Transportation Platforms

   https://doi.org/10.1287/trsc.2022.1133  

   Ausseil, Rosemonde; Pazour, Jennifer A.; Ulmer, Marlin W. (March 2022, Transportation Science)

   Peer-to-peer transportation platforms dynamically match requests (e.g., a ride, a delivery) to independent suppliers who are not employed nor controlled by the platform. Thus, the platform cannot be certain that a supplier will accept an offered request. To mitigate this selection uncertainty, a platform can offer each supplier a menu of requests to choose from. Such menus need to be created carefully because there is a trade-off between selection probability and duplicate selections. In addition to a complex decision space, supplier selection decisions are vast and have systematic implications, impacting the platform’s revenue, other suppliers’ experiences (in the form of duplicate selections), and the request waiting times. Thus, we present a multiple scenario approach, repeatedly sampling potential supplier selections, solving the corresponding two-stage decision problems, and combining the multiple different solutions through a consensus algorithm. Extensive computational results using the Chicago Region as a case study illustrate that our method outperforms a set of benchmark policies. We quantify the value of anticipating supplier selection, offering menus to suppliers, offering requests to multiple suppliers at once, and holistically generating menus with the entire system in mind. Our method leads to more balanced assignments by sacrificing some “easy wins” toward better system performance over time and for all stakeholders involved, including increased revenue for the platform, and decreased match waiting times for suppliers and requests. 

   more » « less
2. Online acceptance probability approximation in peer-to-peer transportation

   https://doi.org/10.1016/j.omega.2023.102993  

   Ausseil, Rosemonde; Ulmer, Marlin W; Pazour, Jennifer A (February 2024, Omega)

   Crowdsourced transportation by independent suppliers (or drivers) is central to urban delivery and mobility platforms. While utilizing crowdsourced resources has several advantages, it comes with the challenge that suppliers are not bound to assignments made by the platforms. In practice, suppliers often decline offered service requests, e.g., due to the required travel detour, the expected tip, or the area a request is located. This leads to inconveniences for the platform (ineffective assignments), the corresponding customer (delayed service), and also the suppliers themselves (non-fitting assignment, less revenue). Therefore, the objective of this work is to analyze the impact of a platform approximating and incorporating individual suppliers’ acceptance behavior into the order dispatching process and to quantify its impact on all stakeholders (platform, customers, suppliers). To this end, we propose a dynamic matching problem where suppliers’ acceptances or rejections of offers are uncertain. Suppliers who accept an offered request are assigned and reenter the system after service looking for another offer. Suppliers declining an offer stay idle to wait for another offer, but leave after a limited time if no acceptable offer is made. Every supplier decision reveals only their acceptance or rejection information to the platform, and in this paper, we present a corresponding mathematical model and an approximation method that translates supplier responses into updated approximations of the likelihood of a specific supplier to accept a specific future offer and use this information to optimize subsequent offering decisions. We show via a computational study based on crowdsourced food delivery that online approximation and incorporating individual supplier acceptance estimates into order dispatching leads to overall more successful assignments, more revenue for the platform and most of the suppliers, and less waiting for the customers to be served. We also show that considering individual supplier behavior can lead to unfair treatment of more agreeable suppliers. 

   more » « less
3. Simulating Counterfeit Personal Protective Equipment (PPE) Supply Chains During Covid-19

   https://doi.org/10.1109/WSC57314.2022.10015398  

   Hashemi, Layla; Jeng, Chu Chuan; Mohiuddin, Ahna; Huang, Edward; Shelley, Louise (December 2022, Proceedings of the 2022 Winter Simulation Conference)

   B. Feng, G. Pedrielli (Ed.)

   Increased demand for medical supplies, and specifically respirators and face masks, during the Covid-19 pandemic along with the inability of legitimate suppliers to meet these needs created a window of opportunity for counterfeiters to capitalize on the supply chain disruptions caused by a global health crisis. Both legitimate and illicit businesses began shifting their scope from sectors such as textiles to producing and distributing personal protective equipment (PPE), many of which were counterfeit or unauthentic products and thus unable to properly protect users. To study cost-effective disruption strategies, this study proposes a simulation-optimization framework. The framework is used to model counterfeiters’ behavior and analyze the effectiveness of different disruption strategies for counterfeit PPE supply chains during the Covid-19 pandemic. 

   more » « less
4. Community-Connect: COVID-19 Small Business Marketplace with Automated Regulation Matching and Company Lead Retrieval

   https://doi.org/10.1109/IRI51335.2021.00014  

   Coltey, Erik; Vassigh, Shahin; Chen, Shu-Ching (August 2021, IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI))

   Abstract—Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer’s Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatory landscape. Index Terms—COVID-19, Disaster, Natural Language Processing, Data Acquisition, Data Retrieval, User Interfaces 

   more » « less
5. Effects of the COVID-19 crisis on survey fieldwork: Experience and lessons from two major supplements to the U.S. Panel Study of Income Dynamics

   https://doi.org/10.18148/srm/2020.v14i2.7752  

   Sastry, N.; McGonagle, K.A.; Fomby, P. (January 2020, Survey research methods)

   Two major supplements to the Panel Study of Income Dynamics (PSID) were in the field during the COVID-19 outbreak in the United States: the 2019 waves of the PSID Child Development Supplement (CDS-19) and the PSID Transition into Adulthood Supplement (TAS-19). Both CDS-19 and TAS-19 abruptly terminated all face-to-face fieldwork and, for TAS-19, shifted interviewers from working in a centralized call center to working from their homes. Overall, COVID-19 had a net negative effect on response rates in CDS-19 and terminated all home visits that represented an important study component. For TAS-19, the overall effect of Covid-19 was uncertain, but negative. The costs were high of adapting to COVID-19 and providing paid time-off benefits to staff affected by the pandemic. Longitudinal surveys, such as CDS, TAS, and PSID, that span the pandemic will provide valuable information on its life course and intergenerational consequences, making ongoing data collection of vital importance. 

   more » « less