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&rsquo;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&rsquo;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> <p><a href="https://medium.com/@rrajbh/revolutionizing-trucking-how-our-optimization-algorithm-cut-operational-costs-by-20-113df8f4bf9d"><strong>Click Here</strong></a></p>