Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

Perakis, Georgia; Tang, Qinshen; Xiong, Peng

doi:10.2139/ssrn.3305039

Cited by 1 publication

(1 citation statement)

References 108 publications

(57 reference statements)

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“…Shared mobility, where shared passenger cars are deployed to provide ride and logistics services, has been examined from various perspectives such as pricing (Taylor 2018, Bai et al 2018, Guda and Subramanian 2019, admission control (Afeche et al 2018), repositioning (He et al 2020) and last-mile delivery (Qi et al 2018), among others. In retailing, recent development of IT and big data tools have enabled innovative omni-channel strategies (Gao andSu 2016, Harsha et al 2019) and data-driven pricing and logistics policies (Perakis et al 2018, Glaeser et al 2019, Liu et al 2019. We refer readers to Mak (2018) and Qi and Shen (2019) for thorough reviews of papers in smart city operations.…”

Section: Related Literaturementioning

confidence: 99%

Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study

Liu¹,

Shen

2018

SSRN Journal

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We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists' route choices. The proposed framework drives the data-driven urban planning scheme in smart city operations management.

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