A methodology for developing evidence-based optimization models in humanitarian logistics

Hossein Baharmand (1, 2) , Diego Vega (2) , Matthieu Lauras (3, 4) , Tina Comes (5)

1 UIA - University of Agder
2 Hanken School of Economics
3 CGI - Centre Génie Industriel
4 IMT - Institut Mines-Télécom [Paris]
5 Faculty of Technology, Policy and Management [Delft]

Abstract The growing need for humanitarian assistance has inspired an increasing amount of academic publications in the field of humanitarian logistics. Over the past two decades, the humanitarian logistics literature has developed a powerful toolbox of standardized problem formulations to address problems ranging from distribution to scheduling or locations planning. At the same time, the humanitarian field is quickly evolving, and problem formulations heavily rely on the context, leading to calls for more evidence-based research. While mixed methods research designs provide a promising avenue to embed research in the reality of the field, there is a lack of rigorous mixed methods research designs tailored to translating field findings into relevant HL optimization models. In this paper, we set out to address this gap by providing a systematic mixed methods research design for HL problem in disasters response. The methodology includes eight steps taking into account specifics of humanitarian disasters. We illustrate our methodology by applying it to the 2015 Nepal earthquake response, resulting in two evidence-based HL optimization models.

Mots clés: Humanitarian logistics; Mixed methods; Research design; Field research; Optimization; Case study

Domaines: Recherche opérationnelle [math.OC]; Autre

Hossein Baharmand, Diego Vega, Matthieu Lauras, Tina Comes. A methodology for developing evidence-based optimization models in humanitarian logistics. Annals of Operations Research, 2022, 319, p. 1197-1229. ⟨10.1007/s10479-022-04762-9⟩. ⟨hal-03690305⟩