With the fast growth of the parcel volume of online shopping, home delivery (delivering parcels to customers’ homes or workplaces) has accentuated the pressure on last mile delivery actors. Customer pickup, which allows customers to pick up their parcels from shared delivery facilities near them, has become widely popular. This study introduces a novel travelling salesman problem with time windows for the last mile delivery in online shopping. The purpose is to find a minimum cost tour over given customers and/or shared delivery facilities (SDFs) in which unvisited customers are assigned to the SDFs. A general variable neighbourhood search heuristic is developed to solve the problem. Computational results corroborate that the proposed heuristic is competitive relative to well-known algorithms.

This study addresses the problem of choosing the appropriate closed-loop supply chain structure for the collection of used products from customers under random demand. We consider a two-echelon supply chain with three decision-making structures: (1) the manufacturer who coordinates with the retailer to obtain optimal channel profit, (2) the manufacturer who collects used products directly from customers, and (3) the retailer who collects used products from customers and the manufacturer who pays a transfer price to reacquire the products. We show that a unique optimal solution exists when the collection cost function satisfies the specified conditions under different models. We also identify differences among the three models and provide managerial guidelines for various marketing environments and collection cost functions. Surprisingly, contrary to previous studies, we find that the collection rate is not always largest under a centrally coordinated system compared with other making structures. As a special case, we investigate the effect of the coefficient of collection effort cost when cost structure is given.

Accurate tourist flow forecasting is an important issue in tourist destinations management. Given the influence of various factors on varying degrees, tourist flow with strong nonlinear characteristics is difficult to forecast accurately. In this study, a deep learning method, namely, Gated Recurrent Unit (GRU) is used for the first time for tourist flow forecasting. GRU captures long-term dependencies efficiently. However, GRU’s ability to pay attention to the characteristics of sub-windows within different related factors is insufficient. Therefore, this study proposes an improved attention mechanism with a horizontal weighting method based on related factors importance. This improved attention mechanism is introduced to the encoding–decoding framework and combined with GRU. A competitive random search is also used to generate the optimal parameter combination at the attention layer. In addition, we validate the application of web search index and climate comfort in prediction. This study utilizes the tourist flow of the famous Huangshan Scenic Area in China as the research subject. Experimental results show that compared with other basic models, the proposed Improved Attention-based Gated Recurrent Unit (IA-GRU) model that includes web search index and climate comfort has better prediction abilities that can provide a more reliable basis for tourist destinations management.

Frequent time window changing disruptions result in high secondary delivery rates in the last mile delivery. With the rapid growth of parcel volumes in online shopping, the time window changing disruptions could translate to substantial delivery cost-wastes. In recent years, customer pickup (CP), a new delivery mode that allows customers to pick up their parcels from shared delivery facilities, has provided a new way to deal with such disruptions. This study proposed a disruption recovery problem with time windows change in the last mile delivery in which customers can be served through home delivery (HD) or CP. A variant variable neighborhood descent (VVND) algorithm was presented to solve the problem. Computational experiments based on a set of instances were tested, and results were compared with other heuristics in the literature, which have affirmed the competitiveness of the model and algorithm.