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With e-commerce rapidly developing, the Online To Offline (O2O) business model requests high efficiency for order allocation and last-mile delivery. Focusing on the challenges associated with online, same-day, and large-scale order allocation and distribution, we formulate an online dynamic vehicle routing problem with pickup and delivery (ODVRPPD), considering the uncertainty of dynamic orders and sustainability of online reassignments to improve the Quality of Experience (QoE). A novel social behavior whale optimization algorithm (SBWOA) with state machine formulation is proposed to solve this problem and express the order closed-loop fulfillment procedure. Inspired by the social behaviors and sonar communication of whale swarms, we propose SBWOA with a double-zone coding (DZC) scheme and affinity propagation clustering (AP clustering). DZC could make real-coding optimization algorithms be used in integer-coding VRPPDs. SBWOA uses AP clustering for the pickup and delivery locations to minimize delivery distance without specifying the initial clustering center and the number of clusters. Additionally, we use the real order data from Alibaba Cloud to construct 11 test problems (including a multi-day test problem with 12925 tasks and 990 vehicles). SBWOA outperforms four compared algorithms. Moreover, the extensive experimental results demonstrate the feasibility and adaptability of our model and SBWOA.

Tasks that change the physical state of a robot and its environment take a considerable amount of time to execute. However, many robot applications spend the execution time waiting, although the following tasks might require time to prepare. This paper proposes to amend robot tasks with a semantic description of their expected outcomes, which allows planning and preparing successive tasks based on this information. The suggested approach allows sequential and parallel compositions of tasks, as well as reactive behavior modeled as state machines. The paper describes the means of modeling and executing these tasks, details different possibilities of planning in state-machine tasks and evaluates the benefits achievable using the approach on two example scenarios.

Tasks that change the physical state of a robot and its environment take a considerable amount of time to execute. However, many robot applications spend the execution time waiting, although the following tasks might require time to prepare. This paper proposes to amend robot tasks with a semantic description of their expected outcomes, which allows planning and preparing successive tasks based on this information. The suggested approach allows sequential and parallel compositions of tasks, as well as reactive behavior modeled as state machines. The paper describes the means of modeling and executing these tasks, details different possibilities of planning in state-machine tasks and evaluates the benefits achievable using the approach on two example scenarios.