Abstract:

In recent decades， all kinds of natural disasters and public health emergencies have occurred frequently. In order to reduce the loss and casualties caused by emergencies as much as possible， emergency supplies must be delivered to all the points of need in the shortest possible time. Therefore， the center location and material distribution in the emergency network become the key problems to be solved after an emergency. To improve the operational efficiency and reliability of the emergency supply chain network， the location and distribution of the emergency supply chain network are studied from the perspective of system integration and optimization. The supply chain network studied includes the supply point of emergency supplies， the transit warehouse of supplies and the demand point. According to the characteristics of emergencies， considering the uncertainty of emergency supplies supply and demand， a two-stage emergency supply chain network planning model is constructed by adopting the multi-transport mode distribution model. Firstly， based on the robust optimization theory， the uncertain demand and supply are represented as interval data， and the linear duality theory is used to transform the uncertain parameter constraints， and a multi-objective robust optimization model is established， with the network response time， cost and carbon emissions as the minimum optimization objectives. Secondly， a meta-heuristic algorithm is used to solve the model. Considering the shortcomings of the standard cuckoo algorithm， which uses fixed step size control factor and fixed discovery probability to search the optimal solution， an optimal cuckoo search （OCS） algorithm based on dynamic parameter adjustment strategy is proposed in this paper. OCS algorithm is applied to four test problems， namely DZT1， DZT3， DTLZ2 and DTLZ5， and the optimization results are compared with those of CS algorithm and NSGA-II algorithm to verify the effectiveness of the proposed algorithm. The experimental results show that compared with CS algorithm， the solving ability of OCS algorithm has been significantly improved. The introduction of the dynamic adaptive adjustment strategy can effectively improve the convergence and uniformity of the algorithm. In addition， the experimental results show that the OCS algorithm has a strong competitiveness compared with the mainstream NSGA-II algorithm. Finally， the emergency supply chain network decision-making problem with uncertain parameters is studied by using the emergency material allocation data of the disaster areas of Wuhan. The results show the effectiveness of the multi-objective robust optimization model， and the sensitivity analysis results verify the effective inhibition effect of the robust control coefficient on the uncertain disturbance. This study can provide effective guidance for the construction of emergency supply chain network in emergencies.

Key words: emergency supply chain network, planning scheme, adjustable robust optimization, optimization cuckoo search algorithm