A review of robust operations management under model uncertainty
M Lu, ZJM Shen - Production and Operations Management, 2021 - journals.sagepub.com
Over the past two decades, there has been explosive growth in the application of robust
optimization in operations management (robust OM), fueled by both significant advances in …
optimization in operations management (robust OM), fueled by both significant advances in …
Robust stochastic optimization made easy with RSOME
We present a new distributionally robust optimization model called robust stochastic
optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization …
optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization …
Humanitarian transportation network design via two-stage distributionally robust optimization
Natural disasters are highly unpredictable, with varying degrees of magnitude, and thus
require a reliable and robust humanitarian relief network. Faced with the adverse effects of …
require a reliable and robust humanitarian relief network. Faced with the adverse effects of …
A two-stage robust approach to integrated station location and rebalancing vehicle service design in bike-sharing systems
A bike-sharing system is a shared mobility mechanism that provides an alternative
transportation mode for short trips with almost no added travel speed loss. However, this …
transportation mode for short trips with almost no added travel speed loss. However, this …
Data-driven distributionally robust electric vehicle balancing for autonomous mobility-on-demand systems under demand and supply uncertainties
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
A robust and constrained multi-agent reinforcement learning framework for electric vehicle amod systems
Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD)
systems, but their unique charging patterns increase the model uncertainties in AMoD …
systems, but their unique charging patterns increase the model uncertainties in AMoD …
Data-driven robust optimization for contextual vehicle rebalancing in on-demand ride services under demand uncertainty
Z Guo, B Yu, W Shan, B Yao - Transportation Research Part C: Emerging …, 2023 - Elsevier
The rebalancing of idle vehicles is critical to mitigating the supply–demand imbalance in on-
demand ride services. Motivated by a ride service platform, this paper investigates a short …
demand ride services. Motivated by a ride service platform, this paper investigates a short …
A target-based optimization model for bike-sharing systems: From the perspective of service efficiency and equity
The emergence of bike-sharing systems has considerably improved last-and first-mile
transportation systems. To ensure attractiveness to end users, operators aim to design …
transportation systems. To ensure attractiveness to end users, operators aim to design …
Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach
Bike-sharing systems are important components of urban transportation systems that
facilitate short-distance travel. In recent years, free-floating bike-sharing systems have …
facilitate short-distance travel. In recent years, free-floating bike-sharing systems have …
Distributionally robust facility location with uncertain facility capacity and customer demand
This study explores a capacitated facility location problem where facility capacity and
customer demand are subject to uncertainties simultaneously. This problem decides the …
customer demand are subject to uncertainties simultaneously. This problem decides the …