Efficient Neural Collaborative Search for Pickup and Delivery Problems.

In this paper, we introduce Neural Collaborative Search (NCS), a novel learning-based framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the collaboration between the latest prevalent neural construction and neural improvement models, establishing a collaborative framework where an improvement model iteratively refines solutions initiated by a construction model. Our NCS collaboratively trains the two models via reinforcement learning with an effective shared-critic mechanism.

Real-Time Evacuation Strategy Based on Cell-Inspired Simulation Model.

During the evacuation process in an emergency, the route conditions usually change instantaneously, which makes routing selection a challenging work. To describe dynamic changes in the evacuation environment, this work proposes a real-time evacuation strategy based on a comprehensive route constraint in the framework of a cell-inspired simulation model, intelligence decision P system (IDPS). In our model, the comprehensive route constraint is formed to describe more complex road condition information including the total distance, congestion state, and unreliability of the route.