Browsing by Author "Xu, Lan"

Now showing 1 - 3 of 3
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    A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics
    (2020-04-04) Xu, Lan; Tu, Yiliu; Zhang, Yuting
    A framework for the algorithm-based CL platform is established, based on which, the operational mode of it is described in detail. An integrated logistics task assignment model is built to optimally match logistics service resources and task of large scale in the algorithm-based CL. Particularly, an improved grasshopper optimization-based bitarget optimization algorithm (GROBO) is proposed to solve the biobjective programming model for service matching in CL. The case of Linyi small commodity logistics is taken as an application. Simulation results show that the proposed GROBO provides better solutions regarding to searching efficiency and stability in solving the model.
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    Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)
    (2020-04-28) Xu, Lan; Tu, Paul; Tang, Qian
    Purpose. This paper aims to design the contract and present the profit distribution mechanism for CL platform, so as to realize the intelligent and automatic operation of the artificial intelligent- (AI-) based CL platform. Design/Methodology. A smart contract based on BT is designed for the AI-based CL platform. Profit distribution mechanism based on the Nash bargaining model for the CL platform is also put forward to coordinate different participators’ benefit relationship in CL. Findings. The AI-based CL platform and the proposed smart contract based on BT map the scenario which may be influenced by human factors and involve trust issues onto execution of codes. Practical Implications. The study will help CL practitioners in establishing effective profit mechanism and designing contracts on the platform, thus facilitating its sustainable operation. Originality/Value. The AI-based CL platform with BT smart contract can be totally free of human intervention, and hence, the problems of trust during CL platform’s operation are solved.
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    Quality Prediction Model Based on Novel Elman Neural Network Ensemble
    (2019-05-21) Xu, Lan; Zhang, Yuting
    In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.