In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.
The mobile emergency system is a new emergency mode that provides a solution to deal with increasingly frequent sudden disasters by reasonably allocating mobile emergency facilities and optimizing the allocation of mobile emergency materials. We consider mobile emergency cost and mobile emergency time as two objective functions. This paper establishes a multi-objective mobile emergency material allocation model, and transforms the multi-objective. We choose the emergency material transportation path for coding, and apply the hybrid leapfrog algorithm for material allocation to obtain the optimal solution. Finally, the feasibility of the model is verified by taking Zhengzhou urban area under the “21.7” severe rainstorm and flood disaster in Henan Province. The result analyses show that the model can correspond to each stage of mobile emergency material allocation based on the value of cost preference, and put forward suggestions on the location of mobile emergency facilities and the amount of material allocation.
Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model’s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.
Mobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the five characteristics of mobile emergency big data. Second, based on the characteristics of mobile emergency big data, four data quality dimensions are defined with reference to existing research and national standards and combined with the measure of medium truth degree to give single-dimension and multi-dimension data quality truth degree measure models. Finally, a subjective-objective, qualitative-quantitative mobile emergency big data quality evaluation method based on the measure of medium truth degree is formed. The validity and practicality of the method are also verified by examples of algorithmic analysis of fire text datasets from Australian mountain fire data and the Chinese Emergency Incident Corpus. The experiments show that the method can realize quantitative mobile emergency big data quality assessment, solve the problem of data quality ambiguity, and reduce the interference of low-quality data, so as to save resources and improve the analysis and decision-making ability.
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