The advancement of Internet of Things technology enables the collection and transmission of real-time environmental and vehicle information, aiding the scheduling of postearthquake emergency supplies. Earthquakes often cause victims psychological pain due to insufficient supplies, and secondary disasters during transportation complicate supply scheduling. This study used a questionnaire to determine the psychological pain perception cost function of victims and identify the parameter value ranges under various environmental conditions. A fuzzy inference system was applied to ascertain the function parameters based on actual earthquake losses. Subsequently, a mixed-integer programming model for the multiperiod scheduling of emergency supplies was developed. An improved particle swarm optimization (IPSO) algorithm with a nondominated solution adjustment strategy was devised to solve the model and compared with the traditional particle swarm optimization (PSO) algorithm. The efficacy of the IPSO algorithm was validated through multiple examples. Additionally, a sensitivity analysis of factors such as supply satisfaction proportion was conducted. Results indicated that when remaining supplies fail to meet the minimum needs of undistributed disaster points, setting a minimum satisfaction percentage effectively reduces the total psychological pain cost. This study offers significant theoretical value in alleviating victims' psychological pain and enhancing rescue efficiency.

With the development of a new generation of information technology, smart elderly care plays an important role in promoting the construction of elderly care services. The emerging application tools provide door-to-door meal service for urban elderly groups, solving meal problems for special and ordinary elderly with different priority levels and penalty costs of violating time windows. Based on this, considering the personalized needs of the elderly group, this study examines the route optimization problem of cooperative delivery of elderly meals by trucks and robots, and builds a mixed integer programming model to minimize the total cost of the system. For large-scale problems, this study designs an improved adaptive large neighbourhood search algorithm that incorporates simulated annealing algorithm and artificial bee colony algorithm to avoid falling into local optimality. Experiments have proved feasibility and effectiveness of the algorithm and proposed the corresponding management insights from the aspects of delivery efficiency and service quality.

As the expert gear of the emergency rescue system, drones are frequently utilized to distribute supplies following a calamity. The cost and effectiveness of rescue efforts as well as equitable distribution should be taken into account when allocating emergency supplies to disaster-affected areas. This work explores the emergency material allocation problem for truck-drone joint transportation with dynamic energy restrictions based on taking the fairness of emergency material allocation into consideration. In order to guarantee the equitable distribution of materials, the psychological stress experienced by the victims at each catastrophe site is measured using the relative deprivation cost. An adaptive large-scale neighborhood search method serves as the foundation for the creation of a two-stage heuristic algorithm, which reduces the overall cost of the system. The integer programming model MIP is built for this purpose. The research findings can serve as a useful guide for developing a just and effective emergency drone rescue system, and the testing results demonstrate the viability and effectiveness of the two-stage heuristic algorithm.