Quantifying advance load information under deterministic and stochastic conditions

One way of collaboration between a transportation service provider and its customers is timely communication of load information (from customers) and of pick-up/delivery plans (from the service provider). Motivated by real challenges faced by a large carrier, an optimization-based computational study is conducted. The research goal is to quantify the relative benefits and costs of sharing advance load information and preparing (or preplanning) an advance pick-up and delivery plan in load assignment problems in truckload trucking under both deterministic and stochastic service times. Deterministic setting is handled using an integer programming formulation that models a truckload trucking load assignment problem. Stochastic setting is handled using (1) a deterministic approach and (2) a stochastic planning approach that utilizes chance-constraint programming. Benefits of preplanning are experienced over time and come in many intangible forms, such as reduced driver turnover rate and better trip planning including rest stops. Potentially, the shipper might also benefit from a timely transportation schedule. The main cost of preplanning for a carrier is committing an early plan that might cause missing better assignment opportunities that would come up until the load's pick-up time. The policy of assigning the loads at their latest possible time (just before their pick-up times while giving enough time to reposition an available truck to the pick-up locations) is called dispatching, which is widely used in practice. Instead of attaching dollar figures to the benefits of preplanning, the relative cost differences between preplanning (fixing assignment decisions in advance) and dispatching are reported. One can look at the (tangible) cost differences to see if it is worthwhile to induce shippers to reveal their load information earlier.; An extensive computational study under several settings for both deterministic and stochastic service times not only shows the benefit of using advance load information, but also indicates that the cost differences between a well-designed (intelligent) preplanning policy and the best performing policies such as dispatching with look-ahead is minimal. This means that the carrier can and should provide reasonable incentives for shippers to have them communicate their load information early. The results show that it is not necessary to have the load information very early to get the benefits. As little as two days advance notice could be enough. Finally, the additional benefit of stochastic planning using chance constraint programming depends on the confidence level and the preplanning policy used.