This work investigates the uncertainty-aware joint pricing and matching problem for dynamic high-capacity ride-sharing services, where passengers are assumed to be price-elastic and decide whether to accept a ride-sharing offer based on the upfront prices provided by the platform. We formulate the studied problem as a...
In light of the escalating popularity of online shopping and the urgent need to reduce unnecessary driving to mitigate environmental impacts, it has become increasingly important to provide cost-effective solutions for attended home deliveries. Extensive research efforts have been dedicated to addressing challenges r...
Overnight rebalancing in dock-based bike-sharing systems requires routing a limited fleet of trucks before user activity begins. This article formulates static rebalancing problem under stochastic demand uncertainty as a bi-objective combinatorial optimization problem that selects truck routes and visited stations. The...
Demand response (DR) can relieve source-load imbalance in microgrids (MGs), but its practical scheduling value is limited by renewable uncertainty and uncertain user response behavior. This paper proposes a coordinated day-ahead/intraday scheduling framework that combines multi-scenario risk scheduling with fuzzy chanc...
Dynamic flexible job shop scheduling (DFJSP) faces compound uncertainty from job arrivals, machine breakdowns, labor volatility, and fluctuating carbon signals, and reactive or expectation-based methods cannot prevent costly tail events. This study proposes a five-stage risk-aware predictive–proactive framework that co...
Inter-hub feeder service is a promising application for electric vertical takeoff and landing aircraft (eVTOL), especially for passengers connecting to subsequent flights under tight time constraints. We develop an optimization framework that accounts for heterogeneous passenger behavior. Using stated-preference survey...
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