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The product recall is becoming a challenging issue in supply chain management, and both suppliers and manufacturers are motivated to exert recall efforts to improve supply chain performance. Considering a supply chain composed of one supplier and one manufacturer with product recalls and demand uncertainty, we investigate the contract design by integrating the revenue sharing contract with three cost sharing policies, namely, fixed-rate cost sharing (Contract F), linear cost sharing (Contract L), and threshold-based cost sharing (Contract T), to coordinate the production quantity and recall efforts. Results show that cost sharing could improve the product recall probability and supply chain profits, but only Contract T can coordinate the supply chain in both production quantity and recall efforts. Moreover, the cost sharing rate in Contract T first decreases and then increases as the recall cost increases. When the recall cost is small (large), Contract T (Contract F) is optimal for the manufacturer. In contrast to our intuition, we find Contract L could overmotivate supply chain members to exert a recall effort that is greater than the first-best when the cost coefficient of recall efforts is large enough. However, demand uncertainty will mitigate the impact of product recalls on motivating recall efforts.

The unmanned aerial vehicles (UAV) are now widely used in search and rescue (SAR) missions to locate casualties and survey terrain. To solve the problem of long calculation time and large memory usage of the UAV obstacle-avoidance path-planning algorithm in cooperative tasks, this paper proposes a method that combines the A∗ algorithm and the task allocation algorithm to achieve a faster and more effective path-planning method. First, the environment is displayed in the form of a grid. Then, the enhanced algorithm divides the task area for UAVs. Finally, each UAV performs SAR path planning in the mission area. The tasks of mapping the environment and searching for target points by UAV swarms are discussed in this study. Our research enhances A∗ algorithms for generating the shortest collision-free paths for drone swarms. Further, we evaluate the algorithm via simulating the task assignment algorithm and path-planning algorithm of a 3D map and 2D map. Compared with the traditional A∗ algorithm, the results demonstrate that the enhanced algorithm is effective in the scenario.