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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]

We introduce and study a challenging service network design problem arising within a city logistics company based in Beijing, China. To provide cost-effective same-day delivery services, the company divides the whole urban area into regions. Within each region, a fleet of small-capacity vehicles picks up and delivers packages. A fleet of line-haul vehicles (large-capacity vehicles) executes consistent multiple trips over the regions to transport consolidated packages, during which transshipment is allowed. In this study, we focus on designing the trips of line-haul vehicles, i.e., their region visiting sequences, as the performance of the logistics system depends significantly on the coordination of line-haul vehicles. We model the trip design problem as a service network design problem with consistent multiple trips. We develop a continuous-time MIP model to formulate the problem and optimally solve small-size instances. In addition, we propose an adaptive large neighborhood search heuristic to solve larger instances. Numerical experiments on randomly generated instances as well as real-world operational data demonstrate the effectiveness and efficiency of our solution approach. • The service network design problem with consistent multiple trips is introduced. • A continuous-time mixed integer programming model is formulated. • An adaptive large neighborhood search solution algorithm is developed. • The model and solution algorithm are demonstrated on real-world operational data. [ABSTRACT FROM AUTHOR]