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Enhancing the cost performance in regular humanitarian logistics: location-routing and delivery frequency optimization

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Abstract

Motivation and Justification

Regular humanitarian logistics (R-HL) plays an increasingly important role in alleviating suffering of beneficiaries struck by catastrophes and war. A key challenge that humanitarian organizations face is the lack of guidance on decision-making tools when conducting R-HL.

Aim of the study

A core tenet of this article is to incorporate cost performance in R-HL models to ensure delivery strategies that lead to the greatest good for the greatest number of people with lowest cost.

Methodology

Our study introduces cost performance, quantifying the alleviation of human suffering per unit economic cost. We formulate a mixed integer fractional programming model for the location-routing and delivery frequency problem with the objective of maximizing the cost performance. The neighborhood search-assisted genetic algorithm is designed to solve the intractable problem. Additionally, using real data from typhoon-prone Shenzhen and flood-affected Hefei in China, we validate our model and demonstrate its effectiveness.

Findings

Several numerical experiments have revealed that cost performance can provide moderate transportation and delivery strategies in both decreasing suffering of beneficiaries and lowering the cost according to comparisons with other objectives such as social costs minimization.

Significance and originality

Our study proposes a new objective function for efficiency and effectiveness trade-offs and suggests the use of cost performance as the preferred objective function for R-HL models.

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References

  • Ahmadi M, Seifi A, Tootooni B (2015) A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transp Res E-Logist Transp Rev 75:145–163

    Article  Google Scholar 

  • Altay N, Green WG (2006) OR/MS research in disaster operations management. Eur J Oper Res 175(1):475–493

    Article  MATH  Google Scholar 

  • Banomyong R, Varadejsatitwong P, Oloruntoba R (2019) A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016. Ann Oper Res 283(1–2):71–86

    Article  MathSciNet  Google Scholar 

  • Boyle E, Inanlouganji A, Carvalhaes T, Jevtić P, Pedrielli G, Reddy TA (2022) Social vulnerability and power loss mitigation: a case study of Puerto Rico. Int J Disaster Risk Reduct 82:103357

    Article  Google Scholar 

  • Cao CJ, Liu Y, Tang O, Gao XH (2021) A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. Int J Prod Econ 235:108081

    Article  Google Scholar 

  • Cetinkaya C, Ozceylan E, Isleyen SK (2021) Emergency shelter site selection in maar shurin community of Idlib (Syria). Transp J 60(1):70–92

    Article  Google Scholar 

  • Cookson R, McCabe C, Tsuchiya A (2008) Public healthcare resource allocation and the rule of rescue. J Med Ethics 34(7):540–544

    Article  Google Scholar 

  • De Vries H, Van Wassenhove LN (2020) Do optimization models for humanitarian operations need a paradigm shift? Prod Oper Manag 29(1):55–61

    Article  Google Scholar 

  • Donmez Z, Turhan S, Karsu O, Kara BY, Karasan O (2022) Fair allocation of personal protective equipment to health centers during early phases of a pandemic. Comput Oper Res 141:105690

    Article  MathSciNet  MATH  Google Scholar 

  • Ehsani B, Karimi H, Bakhshi A, Aghsami A, Rabbani M (2023) Designing humanitarian logistics network for managing epidemic outbreaks in disasters using internet-of-things. A case study: an earthquake in Salas-e-Babajani city. Comput Ind Eng 175:108821

    Article  Google Scholar 

  • Golabi M, Shavarani SM, Izbirak G (2017) An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake. Nat Hazards 87(3):1545–1565

    Article  Google Scholar 

  • Gupta S, Starr MK, Farahani RZ, Matinrad N (2016) Disaster management from a POM perspective: mapping a new domain. Prod Oper Manag 25(10):1611–1637

    Article  Google Scholar 

  • Hiassat A, Diabat A, Rahwan I (2017) A genetic algorithm approach for location-inventory-routing problem with perishable products. J Manuf Syst 42:93–103

    Article  Google Scholar 

  • Holguin-Veras J, Jaller M, Van Wassenhove LN, Perez N, Wachtendorf T (2012) On the unique features of post-disaster humanitarian logistics. J Oper Manag 30(7–8):494–506

    Article  Google Scholar 

  • Holguin-Veras J, Perez N, Jaller M, Van Wassenhove LN, Aros-Vera F (2013) On the appropriate objective function for post-disaster humanitarian logistics models. J Oper Manag 31(5):262–280

    Article  Google Scholar 

  • Holguin-Veras J, Amaya-Leal J, Cantillo V, Van Wassenhove LN, Aros-Vera F, Jaller M (2016) Econometric estimation of deprivation cost functions: a contingent valuation experiment. J Oper Manag 45:44–56

    Article  Google Scholar 

  • Holguin-Veras J, Taniguchi E, Jaller M, Aros-Vera F, Ferreira F, Thompson RG (2014) The Tohoku disasters: chief lessons concerning the post disaster humanitarian logistics response and policy implications. Transp Res Part a-Policy Pract 69:86–104

    Article  Google Scholar 

  • IFRC (Water, sanitation and hygiene (WASH). https://www.ifrc.org/our-work/health-and-care/water-sanitation-and-hygiene-wash. Accessed 26 Apr 2023

  • IFRC (2023) World Disasters Report 2022. https://www.ifrc.org/document/world-disasters-report-2022. Accessed 26 Apr 2023

  • Inanlouganji A, Pedrielli G, Reddy TA, Aponte FT (2022) A computational approach for real-time stochastic recovery of electric power networks during a disaster. Transp Res Part E-Logist Transp Rev 163:102752

    Article  Google Scholar 

  • Kawase R, Iryo T (2023) Optimal stochastic inventory-distribution strategy for damaged multi-echelon humanitarian logistics network. Eur J Oper Res 309(2):616–633

    Article  MathSciNet  MATH  Google Scholar 

  • Lee HR, Lee T (2018) Markov decision process model for patient admission decision at an emergency department under a surge demand. Flex Serv Manuf J 30(1–2):98–122

    Article  Google Scholar 

  • Lim S, Hughston L, Dhuinn-Bhig ZN, Kwawaldeh Ha (2022) Syria resilience programme: reflections on integration in resilience programming in Syria. https://insights.careinternational.org.uk/publications/syria-resilience-programme-reflections-on-integration-in-resilience-programming-in-syria. Accessed 26 Apr 2023

  • Loree N, Aros-Vera F (2018) Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics. Transp Res E-Logist Transp Rev 116:1–24

    Article  Google Scholar 

  • Masudin I, Fernanda FW (2019) A review of literature on types, stages of recovery and humanitarian logistics operations in the tsunami and earthquake disaster in Indonesia. In: IOP conference series: materials science and engineering, vol 674(012043), p 9

  • Ni WJ, Shu J, Song M (2018) Location and emergency inventory pre-positioning for disaster response operations: min-max robust model and a case study of yushu earthquake. Prod Oper Manag 27(1):160–183

    Article  Google Scholar 

  • Oloruntoba R (2015) A planning and decision-making framework for sustainable humanitarian logistics in disaster response. In: Klumpp M, DeLeeuw S, Regattieri A, DeSouza R (eds) Humanitarian logist sustain, pp 31–48

  • Ozdamar L, Ertem MA (2015) Models, solutions and enabling technologies in humanitarian logistics. Eur J Oper Res 244(1):55–65 (Proceedings paper)

    Article  MathSciNet  MATH  Google Scholar 

  • Park CH, Berenguer G (2020) Supply constrained location-distribution in not-for-profit settings. Prod Oper Manag 29(11):2461–2483

    Article  Google Scholar 

  • Paul JA, Zhang MJ (2019) Supply location and transportation planning for hurricanes: a two-stage stochastic programming framework. Eur J Oper Res 274(1):108–125

    Article  MathSciNet  MATH  Google Scholar 

  • Pereira Resende HF, Cardoso PA, Fontainha TC, Leiras A (2022) Maturity model for evaluating disaster and humanitarian operations. Int J Prod Perform Manag. https://doi.org/10.1108/ijppm-03-2021-0149

    Article  Google Scholar 

  • Perez-Rodriguez N, Holguin-Veras J (2016) Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transp Sci 50(4):1261–1285

    Article  Google Scholar 

  • Pisinger D, Ropke S (2007) A general heuristic for vehicle routing problems. Comput Oper Res 34(8):2403–2435

    Article  MathSciNet  MATH  Google Scholar 

  • Pradhananga R, Mutlu F, Pokharel S, Holguin-Veras J, Seth D (2016) An integrated resource allocation and distribution model for pre-disaster planning. Comput Ind Eng 91:229–238

    Article  Google Scholar 

  • Prakash C, Roy V, Charan P (2022) Mitigating interorganizational conflicts in humanitarian logistics collaboration: the roles of contractual agreements, trust and post-disaster environmental uncertainty phases. Int J Logist Manag 33(1):28–52

    Article  Google Scholar 

  • Reddy KP, Shebl FM, Foote JHA, Harling G, Scott JA, Panella C, Fitzmaurice KP et al (2021) Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study. Lancet Glob Health 9(2):120–129

    Article  Google Scholar 

  • Saatchi HM, Khamseh AA, Tavakkoli-Moghaddam R (2021) Solving a new bi-objective model for relief logistics in a humanitarian supply chain using bi-objective meta-heuristic algorithms. Sci Iran 28(5):2948–2971

    Google Scholar 

  • Saputra TY, Pots O, de Smidt-Destombes KS, de Leeuw S (2015) The impact of mean time between disasters on inventory pre-positioning strategy. Disaster Prev Manag 24(1):115–131

    Article  Google Scholar 

  • Şatir Akpunar Ö, Akpinar S (2021) A hybrid adaptive large neighbourhood search algorithm for the capacitated location routing problem. Expert Syst Appl 168:114304

    Article  Google Scholar 

  • Stumpf J, Besiou M, Wakolbinger T (2023) Supply chain preparedness: How operational settings, product and disaster characteristics affect humanitarian responses. Prod Oper Manag. https://doi.org/10.1111/poms.13988

    Article  Google Scholar 

  • Sun H, Li J, Wang T, Xue Y (2022) A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions. Transp Res Part E-LogistTransp Rev 157:102578

    Article  Google Scholar 

  • Tang YD, Yu X, Liu CP (2012) The economics of disasters: a critical review. In: ISCRAM ASIA conference on information systems for crisis response and management, pp 83–86

  • Tavana M, Abtahi A-R, Di Caprio D, Hashemi R, Yousefi-Zenouz R (2018) An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. Socioecon Plann Sci 64:21–37

    Article  Google Scholar 

  • Tomasini RM, Van Wassenhove LN (2009) From preparedness to partnerships: case study research on humanitarian logistics. Int Trans Oper Res 16(5):549–559

    Article  MATH  Google Scholar 

  • Turrini L, Besiou M, Papies D, Meissner J (2020) The role of operational expenditures and misalignments in fundraising for international humanitarian aid. J Oper Manag 66(4):379–417

    Article  Google Scholar 

  • UNHCR (2022a) Ukraine, other conflicts push forcibly displaced total over 100 million for first time. https://www.unhcr.org/news/news-releases/unhcr-ukraine-other-conflicts-push-forcibly-displaced-total-over-100-million. Accessed 26 Apr 2023

  • UNHCR (2022b) Global Report 2021. https://reporting.unhcr.org/globalreport2021/#_ga=2.62347500.171047543.1682581079-720077122.1682581079. Accessed 26 Apr 2023

  • Wang XH, Fan Y, Liang L, De Vries H, Van Wassenhove LN (2019) augmenting fixed framework agreements in humanitarian logistics with a bonus contract. Prod Oper Manag 28(8):1921–1938

    Article  Google Scholar 

  • Yang YJ, Yin YQ, Wang DJ, Ignatius J, Cheng TCE, Dhamotharan L (2023) Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics. Eur J Oper Res 305(3):1042–1062

    Article  MathSciNet  MATH  Google Scholar 

  • Yu LN, Zhang CR, Yang HS, Miao LX (2018) Novel methods for resource allocation in humanitarian logistics considering human suffering. Comput Ind Eng 119:1–20

    Article  Google Scholar 

  • Zhu L, Gong YM, Xu YS, Gu J (2019) Emergency relief routing models for injured victims considering equity and priority. Ann Oper Res 283(1–2):1573–1606

    Article  MathSciNet  MATH  Google Scholar 

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Funding

This research was supported by National Natural Science Foundation of China Grants 72071189, 72101249 and 71921001.

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Authors and Affiliations

  1. School of Public Administration, Dongbei University of Finance and Economics, 217 Jianshan Street, Shahekou District, Dalian City, 116025, China

    Tongxin Liu

  2. School of Management, University of Science and Technology of China, No. 96, Jinzhai Road, Hefei, 230026, Anhui, China

    Jun Li & Xihui Wang

  3. International Institute of Finance, University of Science and Technology of China, No. 1789, Guangxi Road, Hefei, Anhui, China

    Xihui Wang

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  1. Tongxin Liu
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Correspondence to Jun Li.

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Appendix

Appendix

See Tables

Table 6 The demand for each demand point in the basic case
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Table 7 Transportation time among research sites (hours)
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Table 8 Need of demand points in the large numerical experiment
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Liu, T., Li, J. & Wang, X. Enhancing the cost performance in regular humanitarian logistics: location-routing and delivery frequency optimization. Flex Serv Manuf J (2023). https://doi.org/10.1007/s10696-023-09512-y

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