Emergency Vehicle Dispatching Model Considering Demand Urgency and Delivery Fairness
In emergency vehicle dispatching after unexpected events,it is crucial to prioritize critical nodes with high demand urgency while minimizing excessive delivery delays at other nodes to prevent secondary impacts.On this basis,this study developed an emergency vehicle dispatching model that considered demand urgency and delivery delay fairness.Firstly,the entropy weight-TOPSIS method was used to calculate the demand urgency for each node,and a delivery delay balance index was introduced to minimize disparities in delivery delays between nodes.Secondly,a multi-objective optimization model was established with total delivery time,overall cost,and delivery delay balance as objectives,while considering constraints such as inventory levels at distribution centers and route continuity.Finally,the K-Means algorithm and the Clarke-Wright(CW)saving algorithm were employed to partition the sub-networks,and an improved ant colony algorithm incorporating a variable neighborhood local search was applied.The results indicate that compared with the basic model,the proposed model reduces the average and maximum delivery delays at nodes with high demand urgency by 25.26%and 22.98%,respectively,while increasing the delivery delay variance by 34.30%.Compared with a model considering only demand urgency,the proposed model decreases the average delivery delay,maximum delivery delay,and delivery delay variance at nodes with high demand urgency by 10.79%,20.26%,and 40.57%,respectively.These findings demonstrate that the proposed model effectively balances priority for critical nodes with fairness for standard nodes.
smart transportationemergency vehicle dispatchingdelivery delay balancedemand urgencyimproved ant colony algorithm
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