Optimization models for cloud seeding network design and operations

Water shortage has become a significant challenge in many regions, affecting sectors such as agriculture, energy, industry, and the economy. Based on this subject matter and considering cloud seeding as an emerged water achieving promising technique, this paper provides an effective and innovative optimization approach for cloud seeding. This approach involves location analysis and operational planning, which offers significant potential for addressing water shortage challenges. The proposed approach considers atmospheric conditions, functional properties of facilities, seeding method variants, and cloud seeding system vulnerabilities as strategic programming for integrated facility network design. Addition-ally, operational planning, including flight routing and ground-based facility scheduling during rainfall storms, is considered to manipulate storms according to atmospheric conditions and systemic/facility operational constraints. To this end, the paper proposes a multi-objective mixed-integer linear programming model for facility location, which uses an innovative and application depended point-to-polygon cover-ing algorithm, with the principal objective of maximization of spatial coverage. Moreover, a bi-objective mixed-integer linear programming model for seeding operations is developed based on the optimally lo-cated facilities in the strategic model, which uses an innovative line-to-polygon covering algorithm. The first objective of this model is to maximize precipitation augmentation, while the second objective of the both models is to minimize total system-wide costs. Finally, the applications of the proposed models and the managerial insights are also illustrated in a real case study domain.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an optimization approach for cloud seeding to address water shortage challenges. The approach involves location analysis and operational planning, considering atmospheric conditions, facility properties, seeding method variants, and vulnerabilities. The paper presents multi-objective mixed-integer linear programming models for facility location and seeding operations, aiming to maximize spatial coverage and precipitation augmentation, while minimizing total system-wide costs.