Abstract: In the case of public health outbreaks such as the new coronavirus, the distribution of emergency supplies, as a basic means of responding to health and safety emergencies, plays a crucial role in the prevention and control of major public health emergencies such as the new coronavirus. To solve the problem of emergency materials scheduling under major public health emergencies, this paper introduces fuzzy demand into the secondary supply chain structure of “supplies transfer center & demand point” to reflect the uncertainty of demand under the influence of epidemic.
In terms of model establishment, an optimization model based on credibility theory is constructed for the distribution of urban emergency materials. For the urban community emergency distribution scenario under a public health event outbreak, this paper utilizes triangular fuzzy numbers to characterize the material demand at the community points. Further, we induce opportunity constraints by using decision maker's preference values to quantify the fuzzy demand for emergency materials in each community to determine whether or not to carry out the scheduling task. In addition, considering the actual needs of emergency material distribution, the emergency material scheduling optimization model used is finally constructed with the optimization objective of shortest delivery time.
In addition, this paper designs an improved metaheuristics algorithm based on the traditional sparrow search algorithm (SSA). To improve the optimization speed and capability of the SSA, strategies such as Cauchy variation and backward learning are introduced. Furthermore, numerical experiments are conducted to evaluate the effectiveness of our designed algorithm compared to similar algorithms, such as the partial swarm optimization algorithm (PSO), genetic algorithm (GA), and SSA etc. The designed algorithm is tested on benchmark functions, and the results indicate that our algorithm performs better in optimization than the other algorithms.
Further, a case study is conducted to evaluate the effectiveness of proposed method. Thirty communities as demand points and three hospitals as transit centers are selected from Shanghai for simulation experiments. The geographic locations of these demand points and transit centers are obtained from the open geographic data platform to obtain the distance matrix. Similarly, our algorithm is compared with PSO, GA, simulated annealing (SA) and SSA. The results show that the designed algorithm can reduce the vehicle cost by 6.61%, time cost by 5.10%, etc. compared with other cutting-edge algorithms. Finally, the impact of decision maker preference value on distribution is analyzed. New evaluation metrics are introduced to quantitatively analyze the satisfaction level of each community point, and the overall implementation results of the generated scheduling scheme are quantitatively analyzed. The decision makers need to rationally select preference values according to the different objectives in order to make better use of the distribution resources. The results show that the integrated case is optimal when the preference value is 0.4, while the best benefit exists for unit distribution vehicles when the preference value is 0.2.
The contributions of this paper are summarized as follows: 1)An emergency material scheduling optimization model considering fuzzy demand is developed to reflect the real scheduling demand under the outbreak of health and safety events, and the fuzzy demand is effectively handled by the introduction of distribution opportunity constraints for emergency materials and decision maker's preference value. 2)An improved meta-heuristic algorithm is designed for solving the problem. The numerical test results show that the designed algorithm has better optimization effect than other similar algorithms such as PSO, GA, etc. 3)Simulation experiments are carried out by using public data and the decision makers' preference values are analyzed. However, the research in this paper has idealized the road situation and timeliness of materials in the process of emergency material scheduling. In the future research, we will further focus on the realism and general applicability of the model, and will actively explore the optimization problem of multimodal transportation in emergency material scheduling.

Key words: emergency material scheduling, fuzzy demand, chance constraint, metaheuristic algorithm

摘要： 本文将模糊需求引入“物资转运中心—需求点”的二级供应链结构,以反映疫情等公共卫生事件爆发前期的应急物资需求不确定性,并且重点分析了决策者对调度方案的潜在影响。首先构建了基于模糊需求的配送机会约束,在此基础上建立以车辆成本和时间成本最小化为优化目标的应急物资调度优化模型。此外设计了一种改进的元启发式算法,并通过数值实验验证了所设计算法较同类算法的优越性。此外,借助公开地理数据以上海市为仿真背景,将所设计算法与前沿算法的对比。结果表明所设计的算法较其他算法能减少6.61%的车辆成本、5.10%的时间成本等。最后,通过引入社区满意程度等衡量指标,分析了决策者偏好值对调度的影响,结果表明在实验场景下当偏好值为0.4时为综合最优情况,而当偏好值为0.2时单位配送车辆存在最佳效益。本文的研究为探索真实情况下的城市应急保障物资配送问题提供了有效参考。

关键词: 应急物资调度, 模糊需求, 机会约束, 元启发式算法