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This study introduces a challenging service network design problem with stochastic demands and fixed routes. In this problem, the routes of some service vehicles are fixed during the whole planning horizon, while the remaining vehicles are flexible in that their routes can adapt to different realizations of customer demands each day. The problem is to determine which vehicles should be set as fixed and how their fixed routes should be designed. To solve this problem, we formulate a two-stage stochastic mixed-integer linear program. The fixed routes are designed in the first stage before customer demands are realized, and the routes for flexible vehicles are designed in the second stage after customer demands are observed. A learning-based multiple scenario approach is developed as the solution method. Numerical experiments on real-world operational data from a logistics company show that fixing the routes of some service vehicles may reduce the operational cost by an average of approximately 6.37% compared with that in the case of no fixed routes. • The service network design problem with stochastic demands and fixed routes is introduced. • A two-stage stochastic mixed-integer linear programming model is formulated. • A learning-based multiple scenario approach is developed. • The model and solution algorithm are demonstrated on real-world operational data. [ABSTRACT FROM AUTHOR]

Motivated by the recent boom of the sharing economy, this paper presents a scheme of sharing demand-side energy resources (DERs) among multiple prosumers. Successful sharing must achieve enhanced utilization efficiency of DERs and, in the mean time, ensure voluntary participation of prosumers and a sharing-enabling aggregator. It is also desirable to incentivize the adoption of DERs. To fulfill these goals, we formulate a mathematical program with equilibrium constraints (MPEC) for DER valuation within a sharing community. The aggregator coordinates DER operations in real-time; then it solve this MPEC problem after each billing period. In doing so, the aggregator evaluates two operating costs for each prosumer: the actual cost under coordination and the counter-factual cost if the prosumer independently traded power with the aggregator. We define the difference in these two costs as the coordination surplus, which the aggregator and the prosumer split. Simulation results demonstrate that this sharing procedure effectively achieves the aforementioned goals. [ABSTRACT FROM AUTHOR]