Truck-drone joint path planning for post-disaster emergency material deployment considering fairness

Abstract: 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.

How to cite this paper

 Hong, W., Liu, S., Xu, S & Pu, X. (2024). Truck-drone joint path planning for post-disaster emergency material deployment considering fairness.International Journal of Industrial Engineering Computations , 15(2), 456-472.

Refrences

Agatz, N., Bouman, P., & Schmidt, M. (2018). Optimization approaches for the traveling salesman problem with drone. Transportation Science, 52(4), 965-981.
Alfandari, L., Ljubić, I., & da Silva, M. D. M. (2022). A tailored Benders decomposition approach for last-mile delivery with autonomous robots. European Journal of Operational Research, 299(2), 510-525.
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of logistics, 11(2), 101-121.
Biswal, A. K., Jenamani, M., & Kumar, S. K. (2018). Warehouse efficiency improvement using RFID in a humanitarian supply chain: Implications for Indian food security system. Transportation Research Part E: Logistics and Transportation Review, 109, 205-224.
BnnoBRs, J. (1962). Partitioning procedures for solving mixed-variables programming problems. Numer. Math, 4(1), 238-252.
Chang, Y. S., & Lee, H. J. (2018). Optimal delivery routing with wider drone-delivery areas along a shorter truck-route. Expert Systems with Applications, 104, 307-317.
Chowdhury, P., Paul, S. K., Kaisar, S., & Moktadir, M. A. (2021). COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 148, 102271.
D'Andrea, R. (2014). Guest editorial can drones deliver?. IEEE Transactions on Automation Science and Engineering, 11(3), 647-648.
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53-66.
Dorling, K., Heinrichs, J., Messier, G. G., & Magierowski, S. (2016). Vehicle routing problems for drone delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), 70-85.
Gutjahr, W. J., & Fischer, S. (2018). Equity and deprivation costs in humanitarian logistics. European Journal of Operational Research, 270(1), 185-197.
Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366.
Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7-8), 494-506.
Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management, 31(5), 262-280.
Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation research part E: logistics and transportation review, 48(1), 2-18.
Jeong, H. Y., Song, B. D., & Lee, S. (2019). Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones. International Journal of Production Economics, 214, 220-233.
Kilic, A., Dincer, M. C., & Gokce, M. A. (2014). Determining optimal treatment rate after a disaster. Journal of the Operational Research Society, 65, 1053-1067.
McCoy, J. H., & Lee, H. L. (2014). Using fairness models to improve equity in health delivery fleet management. Production and Operations Management, 23(6), 965-977.
Moreno, A., Alem, D., Gendreau, M., & Munari, P. (2020). The heterogeneous multicrew scheduling and routing problem in road restoration. Transportation Research Part B: Methodological, 141, 24-58.
Moshref-Javadi, M., Lee, S., & Winkenbach, M. (2020). Design and evaluation of a multi-trip delivery model with truck and drones. Transportation Research Part E: Logistics and Transportation Review, 136, 101887.
Mourelo Ferrandez, S., Harbison, T., Webwer, T., Sturges, R., & Rich, R. (2016). Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm. Journal of Industrial Engineering and Management, 9(2), 374-388.
Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86-109.
Nair, D. J., Grzybowska, H., Fu, Y., & Dixit, V. V. (2018). Scheduling and routing models for food rescue and delivery operations. Socio-Economic Planning Sciences, 63, 18-32.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation research part E: logistics and transportation review, 49(1), 217-249.
Pérez-Rodríguez, N., & Holguín-Veras, J. (2016). Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transportation Science, 50(4), 1261-1285.
Poikonen, S., & Campbell, J. F. (2021). Future directions in drone routing research. Networks, 77(1), 116-126.
Poojari, C. A., & Beasley, J. E. (2009). Improving benders decomposition using a genetic algorithm. European Journal of Operational Research, 199(1), 89-97.
Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2018). Dynamic formulation for humanitarian response operations incorporating multiple organisations. International Journal of Production Economics, 204, 83-98.
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & industrial engineering, 109, 191-203.
Sun, H., Wang, Y., & Xue, Y. (2021). A bi-objective robust optimization model for disaster response planning under uncertainties. Computers & Industrial Engineering, 155, 107213.
Vu, L., Vu, D. M., Hà, M. H., & Nguyen, V. P. (2022). The two‐echelon routing problem with truck and drones. International Transactions in Operational Research, 29(5), 2968-2994.
Wang, X., Wang, X., Liang, L., Yue, X., & Van Wassenhove, L. N. (2017). Estimation of deprivation level functions using a numerical rating scale. Production and Operations Management, 26(11), 2137-2150.
Wang, Z., & Sheu, J. B. (2019). Vehicle routing problem with drones. Transportation research part B: methodological, 122, 350-364.
Wohlsen, M. (2014). The next big thing you missed: Amazon's delivery drones could work-they just need trucks. https://www.wired.com/2014/06/the-next-big-thing-you-missed-delivery-drones-launched-from-trucks-are-the-future-of-shipping/.
Xiang, Y., & Zhuang, J. (2016). A medical resource allocation model for serving emergency victims with deteriorating health conditions. Annals of Operations Research, 236(1), 177-196.
Yu, L., Zhang, C., Yang, H., & Miao, L. (2018). Novel methods for resource allocation in humanitarian logistics considering human suffering. Computers & Industrial Engineering, 119, 1-20.
Yu, L., Yang, H., Miao, L., & Zhang, C. (2019). Rollout algorithms for resource allocation in humanitarian logistics. IISE Transactions, 51(8), 887-909.
Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2019). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research, 283, 1573-1606.