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Recent studies and industry advancements indicate that modular vehicles (MVs) have the potential to enhance transportation systems through their ability to dock and split during a trip. Although various applications of MVs have been explored across different domains, their application in logistics remains underexplored. This study examines the use of MVs in cargo delivery to reduce total delivery costs. We model the delivery problem for MVs as a variant of the Vehicle Routing Problem, referred to as the Modular Vehicle Routing Problem (MVRP). In the MVRP, MVs can either serve customers independently or dock with other MVs to form a platoon, thereby reducing the average cost per unit. In this study, we mainly focus on two fundamental types of MVRPs, namely the capacitated MVRP (CMVRP) and the MVRP with time windows (MVRPTW). To address these problems, we first developed mixed-integer linear programming (MILP) models, which can be solved using commercial optimization solvers. Given the NP-hardness of this problem, we also designed a Tabu Search (TS) algorithm with a solution representation based on Gantt charts and a neighborhood structure tailored for the MVRP. Multi-start and shaking strategies were incorporated into the TS algorithm to escape local optima. Additionally, we explored other potential applications in logistics and discussed problem settings for three MVRP variants. Results from numerical experiments indicate that the proposed algorithm successfully identifies nearly all optimal solutions found by the MILP model in small-size benchmark instances, while also demonstrating good convergence speed in large-size benchmark instances. Comparative experiments show that the MVRP approach can reduce costs by approximately 5.6% compared to traditional delivery methods. Sensitivity analyses reveal that improving the cost-saving capability of MV platooning can enhance overall benefits. 

This paper studies the problem of modeling multi-agent dynamical systems, where agents could interact mutually to influence their behaviors. Recent research predominantly uses geometric graphs to depict these mutual interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Prototypical Graph ODE (PGODE) to address the problem. The core of PGODE is to incorporate prototype decomposition from contextual knowledge into a continuous graph ODE framework. Specifically, PGODE employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of PGODE compared to various baselines. 

Abstract New stimulus‐responsive scaffolds are of interest as constituents of hierarchical supramolecular ensembles. 1,3,5–2,4,6‐Functionalized, facially segregated benzene moieties have a time‐honored role as building blocks for host molecules. However, their user as switchable motifs in the construction of multi‐component supramolecular structures remains poorly explored. Here, we report a molecular cage 1, which consists of a bent anthracene dimer3paired with 1,3,5‐tris(aminomethyl)‐2,4,6‐triethylbenzene2. As the result of the pH‐inducedababab↔bababaisomerization of the constituent‐functionalized benzene units derived from2, this cage can reversibly convert between an open state and a closed form, both in solution and in the solid state. Cage 1was used to create stimuli‐responsive hierarchical superstructures, namely Russian doll‐like complexes with [K⊂18‐crown‐6⊂1]⁺and [K⊂cryptand‐222⊂1]⁺. The reversible assembly and disassembly of these superstructures could be induced by switching cage 1from its open to closed form. The present study thus provides an unusual example where pH‐triggered conformation motion within a cage‐like scaffold is used to control the formation and disassociation of hierarchical ensembles.