Allocation and Distribution Path Planning of Emergency Food Materials Under Flood Scenarios: A Case Study in Fengxian District, Shanghai, China

Abstract

Supply–demand allocation is important for supporting emergency food material management and decision making. This study proposed a supply–demand allocation method for market-supplied materials. The method considers the constraint that market supply reserve depots (MSDs) need to preferentially supply emergency food materials to original demand points, which is mostly neglected in traditional methods. The constraint enables the method to provide a more rational allocation scheme of MSDs. Based on the supply–demand allocation method, an emergency material distribution path planning method under flood scenarios was further developed. Unlike the traditional methods, which mostly neglect simultaneous consideration of the travel time and path reliability factors, this method comprehensively achieves two critical objectives: the shortest path travel time and highest path reliability. The heuristic algorithms are used to solve the optimal path. It can enhance the safety and reliability of food material distributions. Three criteria—degree, squares clustering coefficient, and road design daily traffic volume—are integrated to evaluate the reliability of each road section based on the real road networks, and the impact of the flood on travel time is fully considered. A case study in Fengxian District, Shanghai, China, was conducted to demonstrate the feasibility of the method. Three categories of supplies—rice, drinking water, and infant milk—were chosen to represent the food supplies. The results of the case study can support decision making for emergency rescue and relief efforts of relevant government departments. The methods proposed provide methodological references for related studies in other similar regions.

Keywords

Collaborative supply of emergency materials / Distribution path planning / Emergency food materials / Flood disaster / Shanghai / Supply–demand allocation

References

[1] Al-Qadami EHH, Mustaffa Z, Al-Atroush ME, Martinez-Gomariz E, Teo FY, El-Husseini Y. A numerical approach to understand the responses of passenger vehicles moving through floodwaters. Journal of Flood Risk Management, 2022, 15(4): Article e12828.

[2] Alves MJ, Almeida M. MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Computers & Operations Research, 2007, 34(11): 3458-3470.

[3] Chinese Nutrition Society. Recommended intake of dietary nutrients for Chinese residents, 2013, Beijing. Science Pressin Chinese

[4] Chinese Nutrition Society. Dietary guidelines for Chinese residents, 2016, Beijing. People’s Medical Publishing Housein Chinese

[5] Choo KS, Kang DH, Kim BS. Impact assessment of urban flood on traffic disruption using rainfall–depth–vehicle speed relationship. Water, 2020, 12(4): Article 926.

[6] Du, L., and X. Yang. 2011. Research on the impact of different road waterlogging conditions on traffic and resulting losses. In Proceedings of the 11th National Urban Road Traffic Academic Conference, 1 October 2011, Dalian, China, 389–393. Beijing: China Civil Engineering Society (in Chinese).

[7] Edwards B, Gray M, Borja JB. Measuring natural hazard-related disasters through self-reports. International Journal of Disaster Risk Science, 2021, 12(4): 540-552.

[8] Fleischmann M. MOMEPY: Urban morphology measuring toolkit. Journal of Open Source Software, 2019, 4(43): Article 1807.

[9] Fujita M, Wakabayashi H. Estimation of link reliability under natural disaster environment. Transportation Research Procedia, 2021, 52: 597-604.

[10] Geng J, Hou H, Geng S. Optimization of warehouse location and supplies allocation for emergency rescue under joint government–enterprise cooperation considering disaster victims’ distress perception. Sustainability, 2021, 13(19): Article 10560.

[11] Grüne L, Krügel L, Müller MA. Multiobjective strict dissipativity via a weighted sum approach. Systems and Control Letters, 2022, 170: Article 105396.

[12] Hagberg, A.A., D.A. Schult, and P.J. Swart. 2008. Exploring network structure, dynamics, and function using NetworkX. Proceedings of the 7th Python in Science Conference (SciPy2008), 11–15 August 2008, Pasadena, CA, USA. https://networkx.org/documentation/stable/index.html. Accessed 21 Jun 2022.

[13] Hao Y, Zhao C, Zhang Y, Cao Y, Li Z. Constrained multi-objective optimization problems: Methodologies, algorithms and applications. Knowledge-Based Systems, 2024, 299: Article 111998.

[14] Hu X, Wang M, Leeson MS, Hines EL, Di Paolo E. Deterministic ripple-spreading model for complex networks. Physical Review E, 2011, 83(4): Article 046123.

[15] Hussein Al-Qadami EH, Mustaffa Z, Abdurrasheed AS, Takaijudin H, Hussain Shah SM, Yusof KW. Static and moving vehicles stability criteria inside floodwaters—A review. Engineering Letters, 2021, 29(3): 1264-1276

[16] Järv O, Tenkanen H, Salonen M, Ahas R, Toivonen T. Dynamic cities: Location-based accessibility modelling as a function of time. Applied Geography, 2018, 95: 101-110.

[17] Jiang Y. Supply and demand analysis of food emergency supplies in Shanghai under natural disaster scenarios, 2018, Shanghai. Shanghai Normal Universityin Chinese

[18] Jiang L, Yu J, Wen J, Tang J, Qi M, Wang L, Zhang M. Risk assessment of extreme flood in the north bank of the Hangzhou Bay under land use change scenarios. Progress in Geography, 2021, 40(8): 1355-1370. in Chinese

[19] Kramer M, Terheiden K, Wieprecht S. Safety criteria for the trafficability of inundated roads in urban floodings. International Journal of Disaster Risk Reduction, 2016, 17: 77-84.

[20] Liu J, Schonfeld PM, Peng Q, Yin Y. Measures of travel reliability on an urban rail transit network. Journal of Transportation Engineering, Part A: Systems, 2020, 146(6): Article 04020037

[21] Ministry of Emergency Management of the People’s Republic of China. 2023. 14th Five Year Plan for Emergency Material Support in China. https://www.mem.gov.cn/gk/zfxxgkpt/fdzdgknr/202302/t20230202_441506.shtml. Accessed 23 Mar 2023 (in Chinese).

[22] Ministry of Transport of China. 2014. JTGB01-2014. Technical standard of highway engineering. Beijing: China Communication Press (in Chinese).

[23] Mohaymany AS, Ehteshamrad S, Babaei M. A reliability-based resource allocation model for transportation networks affected by natural disasters. Promet-Traffic & Transportation, 2012, 24(6): 505-513.

[24] National Development and Reform Commission of the People’s Republic of China. 2015. Key classified catalogue of emergency support materials. Beijing: National Development and Reform Commission of the People’s Republic of China. https://www.ndrc.gov.cn/fzggw/jgsj/yxj/sjdt/201504/t20150410_987672.html. Accessed 10 Dec 2024 (in Chinese).

[25] Ozsoydan FB, Gölcük I. A hybridisation of linear programming and genetic algorithm to solve the capacitated facility location problem. International Journal of Production Research, 2023, 61(10): 3331-3349.

[26] Pradhananga R, Mutlu F, Pokharel S, Holguín-Veras J, Seth D. An integrated resource allocation and distribution model for pre-disaster planning. Computers & Industrial Engineering, 2016, 91: 229-238.

[27] Praneetpholkrang P, Kanjanawattana S. A multi-objective optimization model for shelter location-allocation in response to humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 2021, 37(2): 149-156.

[28] Pregnolato M, Ford A, Wilkinson SM, Dawson RJ. The impact of flooding on road transport: A depth-disruption function. Transportation Research Part D: Transport and Environment, 2017, 55: 67-81.

[29] Shanghai Municipal Civil Defense Office. DB31MF/Z003-2021. Design code for emergency evacuation shelters, 2021, Shanghai. Shanghai Municipal Civil Defense Officein Chinese

[30] Smith K, Lawrence G, MacMahon A, Muller J, Brady M. The resilience of long and short food chains: A case study of flooding in Queensland, Australia. Agriculture and Human Values, 2016, 33(1): 45-60.

[31] Smith GP, Modra BD, Felder S. Full-scale testing of stability curves for vehicles in flood waters. Journal of Flood Risk Management, 2019, 12(S2): Article e12527.

[32] Tansar H, Babur M, Karnchanapaiboon SL. Flood inundation modeling and hazard assessment in Lower Ping River Basin using MIKE FLOOD. Arabian Journal of Geosciences, 2020, 13(18): Article 934.

[33] Wang W, Guo R. Travel time reliability of highway network under multiple failure modes. Sustainability, 2022, 14(12): Article 7256.

[34] Wang J, Ma X, Duan G. Improved K-means clustering k-value selection algorithm. Computer Engineering and Applications, 2019, 55(8): 27-33in Chinese

[35] Wang L, Zhang M, Wen J, Chong Z, Ye Q, Ke Q. Simulation of extreme compound coastal flooding in Shanghai. Advances in Water Science, 2019, 30(4): 546-555in Chinese

[36] Wang F, Xie Z, Pei Z, Liu D. Emergency relief chain for natural disaster response based on government–enterprise coordination. International Journal of Environmental Research and Public Health, 2022, 19(18): Article 11255.

[37] Zhang P, Wang J, Li X, Li M, Di Z, Fan Y. Clustering coefficient and community structure of bipartite networks. Physica A: Statistical Mechanics and its Applications, 2008, 387(27): 6869-6875.

[38] Zhang X, Yu J, Chen Y, Wen J, Chen J, Yin ZE. Supply–demand analysis of urban emergency shelters based on spatiotemporal population estimation. International Journal of Disaster Risk Science, 2020, 11(4): 519-537.

RIGHTS & PERMISSIONS

The Author(s)