| With the frequency of disasters in recent years,the operation and management of humanitarian logistics has become an important way for the government and related emergency management organizations to accelerate the response to disasters and reduce casualties.As the core link of humanitarian logistics,the development of a reasonable resource allocation and transportation plan can maximize the demand for resources in disaster areas and realize the social and economic benefits of humanitarian logistics.However,due to the multidimensional uncertainty and high complexity of disasters,how to deliver multiresource to the disaster area quickly and effectively is a huge challenge in the actual rescue.Therefore,with the social and economic benefits of humanitarian logistics as the goal,the research on resource facility location and allocation problem and vehicle routing problem for humanitarian logistics is carried out,focusing on the mathematical modeling ideas and solution methods of the location and allocation problem and vehicle routing problem,in order to provide theoretical guidance and scientific basis for the operation and management of humanitarian logistics by the government and related emergency management organizations.Firstly,taking the pre-disaster preparedness stage as the starting point,the multiresource multi-period prospective humanitarian facility location and allocation problem for humanitarian logistics is studied.Focusing on the characteristics of multi-resource,multi-period and multi-uncertainty,a distributionally robust optimization model for location and allocation problem is developed for two types of uncertainties in humanitarian logistics,i.e.,emergency resource demand and resource transportation time,taking humanitarian efficiency,effectiveness and equity into account.To solve the model,the model is equivalently transformed into a mixed-integer linear model and the branch-and-Benderscut algorithm is proposed.Meanwhile,the In-out Benders cut generation strategy,dual lifting strategy,and normalization of the dual variables strategy are introduced to improve the performance of the algorithm.Through extensive numerical studies,the performance of the designed algorithm is verified,the advantages of the distributionally robust optimization model over the corresponding deterministic model and stochastic optimization model are evaluated,the impacts of key parameters on the model are discussed,and the efficiency,effectiveness and fairness of the model are trade-off analyzed and management insights are obtained.Finally,the validity of the constructed model and the practicality of the designed algorithm are verified by the Jiuzhaigou earthquake.Secondly,for the pre-disaster preparedness stage and the mid-disaster response stage,the multi-period reliable humanitarian facility location and allocation problem for humanitarian logistics is studied to resist the impact of facility disruptions caused by disasters.In the mid-disaster response stage,based on the scenario that the existing and fixed facilities will be disrupted by the disaster and have interruptions,the two-stage adaptive robust optimization model for location and allocation is constructed by considering the actual characteristics such as insufficient supply capacity due to disruptions and various mitigation strategies to mitigate the impact of disruptions,and combining the uncertainty of disruption probability and resource demand.The model aims to determine the location of fixed facilities and resource allocation plan in the pre-disaster preparedness stage and the location of temporary facilities and resource reallocation plan in the mid-disaster response stage under the objective of considering the humanitarian efficiency and effectiveness.To address the characteristics of the model,a row and column generation algorithm based on Benders decomposition algorithm is proposed,and some acceleration strategies are introduced,including In-out Benders cut generation strategy,mixed-integer rounding strategy and warm start strategy.Through extensive numerical experiments and the Wenchuan earthquake case study,the practicality and effectiveness of the model are verified,and the advantages of robust optimization in dealing with uncertainty problems are highlighted.Finally,based on the resource allocation plan in the mid-disaster response stage,flexible and mobile drones transportation is used to solve the drawback that trucks cannot visit some of the road-damaged disaster areas,and the truck-drone collaborative routing problem for humanitarian logistics is investigated.The problem is to achieve a coordinated delivery of relief resources to a disaster area through a set of trucks and their associated drones,where a drone can be launched from its associated truck at a node,independently transporting relief resources to one or more of the affected areas,and returning to the truck at another node along the truck route.For this problem,the tailored robust optimization model is constructed based on the well-known budgeted uncertainty set,and the enhanced branch-and-price-and-cut algorithm incorporating a bounded bidirectional labelling algorithm is developed.To enhance the performance of the algorithm,on the one hand,subset-row inequalities are used to tighten the lower bound of the algorithm,and on the other hand,some enhancement strategies are considered to improve the speed of solving the pricing subproblem.Through extensive numerical studies,the performance of the designed algorithm is evaluated,the advantages of considering uncertainty and robustness are discussed,and the effects of key model parameters on the optimal solution are analyzed.Finally,the benefits of the truck-drone collaborative transport mode over the truck-only transport mode are also evaluated through a real case study of the Wenchuan earthquake. |
