Research On Resource Allocation In Humanitarian Logistics Based On Dynamic Policies

Large-scale disasters and catastrophic events typically result in a significant shortage of critical resources,posing a great challenge on allocating limited resources among different affected areas to improve the quality of emergency logistics operations.This paper pays attention to the performance of resource allocation which includes three metrics:efficiency,effectiveness and equity,respectively corresponding to economic cost,service quality and fairness.In particular,the effectiveness metric concerns about human suffering by depicting it as deprivation cost,an economic valuation measurement newly put forward in the recent publication,and the equity metric concerns about the service equality at the end of planning horizon.The dissertation studies the relief allocation problem with one unit capacity.A multi-period,multi-objective nonlinear integer programming model and its equivalent dynamic programming model are conducted.The dynamic programming method is used directly to solve the model to optimal.Based on the optimal solutions,an optimal delivery pattern with a cyclically sequenced feature is identified and its sufficient condition is provided as well.Furthermore,a piecewise linear method is proposed to accommodate large-scale instances that cannot be applied to the“Cyclic Delivery Approach,.Then,the relief allocation problem with multiple unit capacity is studied.A nonlin?ear integer model is first proposed,and then an equivalent dynamic programming model is developed to avoid the nonlinear terms which are brought in by the introduction of deprivation cost.The dynamic programming method can solve small-scale problems to optimality,but meets difficulty when solving medium and large-scale problems due to the curse of dimensionality.Therefore,an approximate dynamic programming algorithm called rollout algorithm is proposed to overcome this computational difficulty.The com-putational complexity of the proposed algorithm is theoretically analyzed.Furthermore,a modified version of the rollout algorithm is also presented,with its computational com-plexity analyzed.Extensive numerical experiments are conducted to test the performance of the proposed algorithms,and the experimental results demonstrate that the initially proposed rollout algorithm yields optimal or near-optimal solutions within a reasonable amount of time.In addition,the impacts of some important parameters are investigated as well,from which some managerial insights are drawn.Considering the resources with follow-up sharing character,the dissertation investi-gates three allocation policies,such as classic allocation policy,flexible allocation policy and follow-up sharing allocation policy.Piecewise linear method is used to solve the three models with different policies.Extensive numerical experiments are conducted to evaluate the eciency and eectiveness of the proposed policies.Regarding to the resource allocation problem under uncertain demand,the dis-sertation formulates a stochastic nonlinear integer model,and a probabilistic dynamic programming model.An approximate dynamic programming algorithm is proposed to solve the model.A reinforcement algorithm,called Q-learning method is also introduced to solve the model.The computational complexities of the proposed algorithms are the-oretically analyzed.Numerical experiments are conducted to assess the computational eciency of the algorithms and eectiveness of the proposed model.