Robust optimization of the insecticide-treated bed nets procurement and distribution planning under uncertainty for malaria prevention and control

Abstract

Vector control, particularly distribution of insecticide-treated bed nets (ITNs), constitutes one of the major pathways to prevent and reduce malaria transmission. ITN distribution campaigns face several challenges, such as inadequate funding, budgetary constraints, hard-to-reach areas, limited transportation, and market and price volatility. While long-term agreements and proper planning can effectively overcome such challenges, those options may not be available for all humanitarian organizations and governments. To gain a better understanding of such tradeoffs we develop a robust optimization model that minimize ITN distribution costs while taking into consideration protection against financial, market and logistical uncertainties. The simultaneous account of such uncertainties is rarely seen on the humanitarian supply chain design literature. The proposed robust model explores data-driven adaptive uncertainty sets that capture the dependence structure among procurement and distribution costs, leading to plausible uncertain scenarios. In addition, we develop a hierarchical optimization approach to ease the burden of setting a specific robustness level for each constraint, when uncertainties are related to the independent terms. We study a United Nations Children's Fund (UNICEF) ITN distribution campaign in Ivory Coast, observing that (1) total costs increase with campaign robustness, as expected, and (2) campaign robustness comprises of improved supply chain flexibility, which might minimize efforts if it becomes necessary to adjust procurement and transportation plans when uncertainty arises. In addition, assessing robust solutions through Monte Carlo simulations against several realizations of uncertain parameter values indicates that, as desired, robust plan feasibility increases with the specified level of conservatism.

Disaster relief operations: past, present and future

“…MCDM Techniques (5): John et al 2018;Laguna-Salvado et al 2018;Venkatesh et al 2018;Malekpoor et al 2018;Mediouni et al 2018 Optimization ( 34): Anparasan and Lejeune, 2017;Baharmand et al 2017;Baidya and Bera, 2018;Bao et al 2017;Cankaya et al 2018;de Mattos et al 2018;Elluru et al 2017;Fazli-Khalaf et al 2017;Griffith et al 2017;Han et al 2018;Hoskins and Medal, 2018;Ivanov and Sokolov, 2019;Jana et al 2018;Kaur and Singh, 2016;Khalilpourazari and Khamseh, 2017;Kim, Lee and Moon, 2018;Kim, Ramkumar and Subramaniyan, 2018;Leigh et al 2017;Li and Teo, 2018;Lodree et al 2018;Nilsang et al 2018;Pyakurel et al 2018;Prasad et al 2017;Rahmani, 2018;Sabouhi et al 2018;Salehi et al 2017;Samani and Hosseni-Motlagh, 2018;Shareef et al 2018;Sharma et al 2017;Tayal and Singh, 2017;Turkes et al 2017;Yahyaei and Bozorgi-Amiri, 2018;Yang et al 2017;...…”

Scenario-robust pre-disaster planning for multiple relief items

“…In recent years, RO has been successfully implemented to solve problems such as lot-sizing, location-allocation problems under demand uncertainty (Atamtürk & Zhang, 2007 ), capacitated vehicle routing (Sungur et al, 2008 ), fixed-charge multi-period facility location problems under network demand uncertainty (Baron et al, 2011 ), storage assignment (Ang et al, 2012 ), and hub location under demand uncertainty (Shahabi & Unnikrishnan, 2014 ). Examples of applications in humanitarian logistics include optimizing the allocation of response equipment (Hassan et al, 2020 ), mitigating risk in humanitarian relief supply chains (Ben-Tal et al, 2011 ), transporting disaster relief commodities and injured people in the aftermath of an earthquake (Najafi et al, 2013 ), locating urgent relief distribution centers (Lu, 2013 ; Lu & Sheu, 2013 ), designing a relief network (Tofighi et al, 2016 ; Yahyaei & Bozorgi-Amiri, 2019 ), preventing and reducing malaria transmission (de Mattos et al, 2019 ), and optimizing the USDA one-round food aid bidding system (Paul & Wang, 2015 ).…”

Long-Lasting Insecticidal Nets retain bio-efficacy after five years of storage: implications for malaria control programmes

“…In Tanzania, access to LLINs was 50% [16] and trends in malaria burden declines in years after mass campaigns and then increases in the following years as LLINs wear out. Insufficient access to LLINs is mainly due to long intervals between net distribution campaigns, population growth, inadequate funds and budget limitations on malaria control programs [5,[16][17][18]. Increasing access to LLINs through cost-effective solutions remains a critical concern and a number of strategies are being explored for "keep-up campaigns" to retain high LLIN access [13].…”