Cheung, R. K.-M., N. Shi, W. B. Powell, and H. P. Simao, “An Attribute-Decision Model for Cross-Border Drayage Problem,” Transportation Research E: Logistics and Transportation Review, Volume 44, No. 2, pp. 217-234 (2008). (c) Elsevier
This paper illustrates a modeling and algorithmic strategy for multi-layered resource scheduling, using the context of the cross-border drayage problem.
Spivey, M. and W.B. Powell, “The Dynamic Assignment Problem,” Transportation Science, Vol. 38, No. 4, pp. 399-419 (2004). (c) Informs.
This paper proposes a general model for the dynamic assignment problem, which involves the assignment of resources to tasks over time, in the presence of potentially several streams of information processes. It proposes an adaptive learning model that produces non-myopic behavior, and suggests a way of using hierarchical aggregation to reduce statistical errors in the adaptive estimation of the value of resources in the future. Finally, it reports on a study on the value of advance information. Past studies of this topic have used myopic models where advance information provides a major benefit over no information at all. Our model uses adaptive learning to bring forecast information into decisions made now, providing a more realistic estimate of the value of future information.
Powell, W.B., M.T. Towns and A. Marar, “On the Value of Globally Optimal Solutions for Dynamic Routing and Scheduling Problems,” Transportation Science, Vol. 34, No. 1, pp. 50-66 (2000). (c) Informs.
This paper addresses the issue of user noncompliance – what happens when the user does not do what the model recommends. Not surprisingly, the value of global optimization methods is diminished. Perhaps more surprisingly, suboptimal algorithms which are more greedy in nature work even better than an optimal solution.
Powell, W.B., W. Snow and R. K.-M. Cheung, “Adaptive Labeling Algorithms for the Dynamic Assignment Problem,” Transportation Science, Vol. 34, No. 1, pp. 67-85 (2000). (c) Informs.
Chen, Z.L. and W.B. Powell, “A Generalized Threshold Algorithm for the Shortest Path Problem with Time Windows,” in Network Design: Connectivity and Facilities (P. Pardalos, D. Du, eds.), pp. 303-318 (1998).
We look at the resource constrained shortest path problem (by Desroschers, Desrosiers and Soumis) and show that Klingman’s threshold algorithm is faster in side by side comparisons of code programmed by the same programmer. This is yet another experimental demonstration that the algorithm with the better complexity bound (label setting) is not quite as good as label correcting algorithms (the threshold algorithm is a member of this class).
Powell, W.B. “A Stochastic Formulation of the Dynamic Assignment Problem, with an Application to Truckload Motor Carriers,” Transportation Science. Vol. 30, No. 3, pp. 195-219 (1996). (c) Informs.
A summary of my work, primarily from the 1980’s, in truckload trucking. This was the first model to integrate the load matching problem with forecasting, using a neat nonlinear approximation of the future. The paper summarizes the research of several masters theses, which investigated the impact of advance information on system performance.
Koskosidis, I A., W.B. Powell and M. Solomon, “An Optimization-Based Heuristic for Vehicle Routing and Scheduling with Soft Time Window Constraints,” Transportation Science, Vol. 26, No. 2, pp. 69-85, 1992. (c) Informs
Powell, W.B. and D. Gittoes, “An Approximate Labeling Algorithm for the Dynamic Assignment Problem,” Advanced Methods in Transportation Analysis, (L. Bianco, P. Toth, M. Bielli, eds.), Springer, New York, pp. 547-584, 1996.
Koskosidis, I.A. and W.B. Powell, “Clustering Algorithms for Consolidation of Customer Orders into Vehicle Shipments,” Transportation Research, Vol. 26B, No. 5, pp. 365-379, 1992.