This paper has grown out of the challenges we faced modeling complex operational problems arising in freight transportation and logistics, which are characterized by highly dynamic information processes, complex operational characteristics and decentralized control structures. Whereas people solve more traditional problems have struggled with the development of e#ective algorithms, we have struggled with the more basic challenge of simply modeling the problem

Freight transportation is characterized by highly dynamic information processes: customers call in orders over time to move freight; the movement of freight over long distances is subject to random delays; equipment failures require last minute changes; and decisions are not always executed in the field according to plan. The high-dimensionality of the decisions involved has made transportation a natural application for the techniques of mathematical programming, but the challenge of modeling dynamic information processes has limited their success. In this chapter, we explore the use of concepts from stochastic programming in the context of resource allocation problems that arise in freight transportation. Since transportation problems are often quite large, we focus on the degree to which some techniques exploit the natural structure of these problems. Experimental work in the context of these applications is quite limited, so we highlight the techniques that appear to be the most promising.

This paper offers a new vocabulary for representing complex problems in a stochastic, dynamic setting. Our focus is on operational problems that need to be solved under uncertainty, and complex problems which are difficult to formulate mathematically in a clear and elegant way. We believe that similarities between problems are often disguised by semantic differences that reflect the contextual domain of an application. It is not readily apparent, for example, that the blocking problem of railroads and the load planning problem of less-than-truckload trucking are both instances of a dynamic service network design problem. The question that tends to arise is: When are apparently different problems similar, and when are seemingly similar problems different? Consider, for example: