The most common approach for modeling and solving routing and scheduling problems in a dynamic setting is to solve, as close to optimal as possible, a series of deterministic, myopic models. The argument is most often made that, if the data changes, then we should simply re-optimize. We use the setting of the load matching problem that arises in truckload trucking to compare the value of optimal myopic solutions versus varying degrees of greedy, suboptimal myopic solutions in the presence of three forms of uncertainty: customer demands, travel times and, of particular interest, user noncompliance. A simulation environment is used to test different dispatching strategies under varying levels of system dynamism. An important issue we consider is that of user noncompliance, which is the effect of optimizing when users do not adopt all of the recommendations of the model. Our results show that (myopic) optimal solutions only slightly outperform greedy solutions under relatively high levels of uncertainty, and that a particular suboptimal solution actually outperforms optimal solutions under a wide range of conditions.
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