Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

This paper investigates a novel variant of the two-echelon vehicle routing problem (2E-VRP) within the context of modern e-commerce logistics. The model integrates time windows, occasional trucks, occasional drivers, heterogeneous regular vehicles, and two increasingly relevant types of intermediate facilities: transshipment nodes and parcel lockers. Goods are transported from a first-echelon depot to intermediate facilities via occasional trucks and heterogeneous regular trucks, then delivered to customers through occasional drivers and heterogeneous regular riders at the second echelon, or retrieved by customers from designated parcel lockers. Given significant disparities in service scope, vehicle attributes, delivery rules, and cost structures between the two echelons, the crowdsourcing model for the first echelon is redesigned, and new intermediate facilities are incorporated to expand application scenarios and further enhance the operational advantages of the crowdsourcing mode. The objective aims to minimize transportation costs and minimize customer dissatisfaction costs. This study constructed a two-echelon vehicle routing optimization model under the crowdsourcing mode with time windows and intermediate facilities and developed an improved multi-objective sparrow algorithm (IMOSSA) to solve it. Algorithm comparisons and extensive case studies validate the algorithm’s effectiveness and superiority. Scenario analysis investigates the impact of crowdsourced vehicles on routing decisions, experimentally demonstrates the crowdsourcing model’s advantages, and derives practical managerial insights.

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems.