Logistic activity can be thought of as a socio‐technical process whereby a social network of individuals orchestrates a series of technical activities using supporting systems such as transportation and communications. To understand the functioning of the entire system requires proper consideration of all its components. We identify seven key components: the objectives being pursued, the origin of the commodity flows to be transported, knowledge of demand, the decision‐making structure, periodicity and volume of logistic activities, and the state of the social networks and supporting systems. Based on our analysis of the differences between commercial and humanitarian logistics, we pinpoint research gaps that need to be filled to enhance both the efficiency of humanitarian logistics and the realism of the mathematical models designed to support it. We argue that humanitarian logistics is too broad a field to fit neatly into a single definition of operational conditions. At one end of the spectrum we find humanitarian logistic efforts of the kind conducted in long‐term disaster recovery and humanitarian assistance, where operational efficiency – akin to commercial logistics – is a prime consideration. At the other, post‐disaster humanitarian logistic operations involved in disaster response and short‐term recovery activities represent a vastly different operational environment, often in chaotic settings where urgent needs, life‐or‐death decisions and scarce resources are the norm. The huge contrast between these operational environments requires that they be treated separately.

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.

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Several findings call into question current practices. The chief conclusion is that the accuracy of freight generation (FG) and freight trip generation (FTG) models depends on the consistency between the model's structure and actual FG-FTG patterns, the degree of internal heterogeneity of the economic and land use aggregation used to estimate the model, and the appropriateness of the spatial aggregation procedure used to obtain the desired FG-FTG estimates. Relative to model structure, the paper establishes strong reasons to treat FG and FTG as separate concepts, because the latter is the output of logistic decisions, whereas the former is determined by the economics of production and consumption. The connection between business size variables–for example, employment–and FG is relatively strong because they are economic input factors, whereas the one with FTG is weaker because inventory and transportation costs come into play. Thus it is generally not correct to assume proportionality between FTG and business size or to assume that using constant FTG rates could be problematic. For instance, only 18% of the industry sectors in New York City exhibit constant FTG rates per employee. For economic and land use aggregation, the finer the level of detail the better, as independent variables have a better chance to explain FG-FTG. In the case of spatial aggregation, the correct aggregation procedure depends on the underlying disaggregate model. For a FG-FTG model to work well, both economic and land use and spatial aggregations must be appropriate.