In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. Considering the constraints of multi-distribution center, heterogeneous vehicle performance, distribution cost, quasi-shipment certificate and humanized management, Refined Logistics Supply Chain System (RLSCS) and cross-regional scheduling optimization model of logistics vehicles with multidistribution center were established. The designed model can minimize the transportation cost, reduce the transportation time, and improve the vehicle load rate. An adaptive elite honey badger target algorithm based on cubic mapping mechanism (IHBA), is designed to solve the model. Further, performance evaluation by optimizing test functions, the convergence performance of IHBA algorithm was demonstrated. Finally, the simulation experiment is carried out according to the actual business data and it is compared to eight other optimistic algorithms. The experimental results show that the proposed algorithm is more effective and more robust, and the related models and algorithms can provide research basis for industrial products digital supply chain system.INDEX TERMS Industry 4.0, refined supply chain, multi-objective constraint, logistics distribution, honey badger algorithm, cubic mapping mechanism.
Temperature data inside pallets are used as important data indicators for raw tobacco storage and maintenance. To improve the monitoring effect of wireless sensors in raw tobacco pallets, the layout optimization model of wireless sensors in a three-dimensional complex environment is constructed, a multi-objective function with the smallest sensor layout cost and the largest monitoring range is established, and an improved particle swarm optimization (IPSO) is designed to obtain preliminary results. The layout of wireless sensors is optimized and then the long short-term memory (LSTM) neural network algorithm is used to predict the temperature data in a cigarette pallet to achieve the secondary optimization of the sensor layout. Finally, on the basis of actual temperature data in raw tobacco pallets, a simulation environment model is established and verified by simulation experiments. Simulation results show that the sensor layout optimization method proposed in this paper can effectively reduce the number of sensors arranged and, at the same time, allow enterprises to effectively minimize the cost of raw tobacco storage and maintenance.
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