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On the appropriate objective function for post‐disaster humanitarian logistics models
Author(s) -
HolguínVeras José,
Pérez Noel,
Jaller Miguel,
Van Wassenhove Luk N.,
ArosVera Felipe
Publication year - 2013
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2013.06.002
Subject(s) - equity (law) , proxy (statistics) , valuation (finance) , humanitarian logistics , neglect , welfare , estimation , social welfare , logistic regression , economics , business , actuarial science , operations research , public economics , risk analysis (engineering) , operations management , computer science , environmental economics , psychology , finance , engineering , political science , management , market economy , machine learning , psychiatry , law
The paper argues that welfare economic principles must be incorporated in post‐disaster humanitarian logistic models to ensure delivery strategies that lead to the greatest good for the greatest number of people. The paper's analyses suggest the use of social costs—the summation of logistic and deprivation costs—as the preferred objective function for post‐disaster humanitarian logistic models. The paper defines deprivation cost as the economic valuation of the human suffering associated with a lack of access to a good or service. The use of deprivation costs is evaluated with a review of the philosophy and the economic literature to identify proper foundations for their estimation; a comparison of different proxy approaches to consider human suffering (e.g., minimization of penalties or weight factors, penalties for late deliveries, equity constraints, unmet demands) and their implications; and an analysis of the impacts of errors in estimation. In its final sections, the paper conducts numerical experiments to illustrate the comparative impacts of using the proxy approaches suggested in the literature, and concludes with a discussion of key findings.