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.

The fourth-party logistics routing problem (4PLRP) is an important issue in the operation of fourth-party logistics (4PL). In this paper, the study of fourth-party logistics (4PL) path optimization considers that more third-party logistics (3PL) undertake transportation tasks. Under the condition that the 3PL transportation time, transportation cost, node transit time, and transit cost are uncertain, 4PL provides customers with a set of transportation solutions to transport transportation tasks from the initial node to the destination node according to the customer’s risk aversion preference. The transportation scheme not only meets the customer’s time and cost requirements but also meets the carrying capacity and reputation constraints of 3PL. Between the two nodes, one or more 3PLs will undertake the transportation task. The customer’s risk preference will be measured by the ratio utility theory (RUT). An ant colony system-improved grey wolf optimization (ACS-IGWO) is designed to solve the model, and the grey wolf optimization (GWO) is improved by the convergence factor and the proportional weight. Problem analysis is conducted through simulation experiments.

The meal delivery routing problem faced by the e-commerce platform is challenging. In order to reduce the logistics cost and meet the needs of customers, a multi-objective meal delivery routing problem is studied under the shared logistics services mode (SLSM). In the meal delivery problem, the third-party logistics providers need to arrange vehicles in the multi-depot to pick up meals from multiple food providers and deliver them to customers, so as to realize the sharing of logistics services between food providers and compare them with the traditional logistics service mode (TLSM). While realizing sharing, it is also necessary to meet customer time requirements as much as possible. In this case, a multi-objective mathematical model to minimize customer dissatisfaction and delivery cost is established, and the linear weighting method is employed to transform the model. An improved Ant Lion Optimizer (IALO) is proposed to solve the problem, and compared with other algorithms. The experiments verified the effectiveness of the proposed algorithm and show that SLSM can save logistics costs and meet customer needs more than TLSM, and provide certain reference opinions for the e-commerce platform.

Revenue distribution is an important issue in the operations of a logistics service supply chain (LSSC). The existing works on revenue distribution are mostly based on the assumption of rational economic people that are purely self-interested. However, people also have a fairness preference, which impacts people’s decision-making behavior or even the success operations of the LSSC. For a two-level supply chain consisting of logistics service integrator (LSI) and several functional logistics service providers (FLSP), this paper establishes an improved revenue distribution model considering FLSPs’ inequity aversion. Specifically, the BO model (abbreviation of a model proposed by Bolton and Ockenfels in 2000) is improved to describe the FLSPs’ inequity aversion, which is combined into the conventional revenue distribution model. The proposed model aims to maximize the revenue of logistics service supply chain and obtains the best revenue distribution ratio of each member under equilibrium. In the numerical cases, the impacts of inequity aversion and the number of members with inequity aversion on the revenue distribution are discussed, respectively. The results show that a higher degree of FLSP’s advantageous inequity aversion corresponds to a lower revenue distribution ratio; a higher degree of FLSP’s disadvantageous inequity aversion corresponds to a higher revenue distribution ratio. Increasing the number of FLSP members with inequity aversion results in a higher profit of LSI and lower total utility of FLSPs and the utility of the supply chain. The more FLSP members with inequity aversion there are, the higher the LSI’s profit is, and the lower the total utility of FLSPs and the utility of supply chain are. In addition, the revenue distribution ratio of the FLSP increases with its relative fairness revenue coefficient among FLSPs.

Door-to-door delivery is becoming more prevalent in practice. However, crowdsourcing couriers' uncertainties (delivery distance, monthly income, and the monthly volume of tasks) make it hard for logistics companies to maintain a stable crowdsourcing couriers' participation rate and guarantee a good level of delivery coverage rate. To solve this problem, this paper proposed the crowdsourcing delivery optimization model, which considers the bounded rationality of crowdsourcing couriers, analyses the crowdsourcing couriers' participation rate under three crowdsourcing couriers' uncertainties, and helps logistics companies to decrease the delivery cost by adjusting the delivery price per parcel and delivery distance while guaranteeing a certain level of delivery coverage rate. Further, this paper combined the whale optimization algorithm (WOA) with the model to design a heuristic algorithm for solving the crowdsourcing delivery problem. The experiments were conducted according to a data set from Beijing (China). Experiment results shows that 4.07 million USD a month is enough to provide door-to-door delivery services in 94.22% area of the city, which is feasible for giant companies.