Traffic information and dynamic pickup and delivery planning in forwarding agencies

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Freight transportation is crucial for economies, particularly through forwarding agencies that manage less-than-truckload freight. Direct transport from origin to destination is often too costly, so the focus is on consolidating small shipments for efficient transportation. This involves collecting commodities from various customer locations and grouping them at transshipment points, where they are loaded onto trucks heading to the same destination region. Upon arrival, goods are transferred to smaller trucks for final delivery. Forwarding agencies handle numerous pickups and deliveries daily, requiring a fleet of vehicles to serve local customers. The performance of these agencies is affected by two key dynamic factors: the rise of information and communication technology, which leads to last-minute pickup orders, and unexpected traffic situations that threaten timely pickups, despite the availability of traffic information. However, this information is often underutilized, resulting in late shipments and poor vehicle utilization. To address these challenges, this work proposes an intelligent planning system based on mathematical optimization heuristics, specifically a tabu search. This system anticipates travel times and incorporates unknown customer orders into route planning, enabling forwarding agencies to improve delivery timeliness and vehicle routing efficiency.