Revolutionizing Trucking: How Our Optimization Algorithm Cut Operational Costs by 20%
<p>In order to accomplish these objectives, we developed a finite state machine (FSM) that stores the state of the load assignments to drivers at each time step, and changes the state as new information about loads and driver’s destination information from the previous trip becomes available. To select a particular set of loads and reduce deadhead miles, we used progressive geospatial search algorithm with the capability to form trip chains.</p>
<h1>Use of Finite State Machines and Progressive Geospatial Search</h1>
<p>We applied the concept of Finite State Machine (FSM) to this problem, in which the current state represents a specific set of loads assigned to drivers at any given time. As new shipments and information about drivers enter the current state, a new state is created. When there is an input to the machine, the machine’s state changes in that the next state depends only on the current state and the input. In other words, the new state depends on the old state and the input.</p>
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