System and method of determining freight/load distribution for multiple vehicles
First Claim
1. A method for determining freight distribution amongst multiple vehicles, comprising:
- accessing, by a processor, operator input data indicative of at least one operator input, said at least one operator input including at least one of route beginning and ending points to define a route, number of vehicles, maximum vehicle freight weight for each of a plurality of vehicles, and total freight weight;
accessing, by the processor, predetermined vehicle parameter data for each of said plurality of vehicles indicative of at least one predetermined vehicle parameter, said at least one predetermined vehicle parameter including at least one of vehicle mass, vehicle drag, vehicle rolling resistance, tire circumference, front area of vehicle, powertrain torque loss, vehicle tank capacity, and engine efficiency;
accessing, by the processor, road terrain data indicative of at least one road terrain element, said at least one road terrain element including at least one of speed limit changes, road grade, air density, position, elevation, and traffic patterns;
determining, by the processor, a first plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a random distribution of freight amongst the plurality of vehicles, the first plurality of freight distribution solutions comprising an initial generation;
evaluating, by the processor, each of said first plurality of freight distribution solutions of the initial generation for fitness based on fuel economy and ranking each solution based on the evaluation;
determining, by the processor, a second plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a distribution of freight amongst the plurality of vehicles according to a genetic algorithm structured to selectively execute a reproduction process first, a crossover process second, and a mutation process third, wherein the second plurality of freight distribution solutions includes a next generation;
generating, by the processor, a first random number associated with the crossover process and a second random number associated with the mutation process;
executing, by the processor, the crossover process responsive to the first random number being less than a crossover probability;
executing, by the processor, the mutation process responsive to the second random number being less than a mutation probability;
evaluating, by the processor, each of said freight distribution solutions of the next generation for fitness based on fuel economy, ranking each solution based on the evaluation, and assessing solution convergence based on the ranking;
determining, by the processor, an optimal freight distribution based on said convergence assessment; and
generating, by the processor, an electronic recommendation signal corresponding to said determined optimal freight distribution and communicating said recommendation signal to a receiver.
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Abstract
Systems and methods of vehicle freight/load distribution are provided to assist in determining optimal freight distribution. Although it is standard practice to fill each vehicle to its maximum limit, due to the non-linear nature of engine fueling maps (that is, fueling maps vary non-linearly as a function of torque and speed), the optimal distribution may not be obtained by the standard practice. Finding a solution for the optimal freight distribution may also need to account for the cost of fueling and operator costs, particularly if the situation involves multiple vehicles not filled to capacity. The benefit is increased freight efficiency in transporting cargo from source to destination amongst a fleet of vehicles.
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Citations
18 Claims
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1. A method for determining freight distribution amongst multiple vehicles, comprising:
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accessing, by a processor, operator input data indicative of at least one operator input, said at least one operator input including at least one of route beginning and ending points to define a route, number of vehicles, maximum vehicle freight weight for each of a plurality of vehicles, and total freight weight; accessing, by the processor, predetermined vehicle parameter data for each of said plurality of vehicles indicative of at least one predetermined vehicle parameter, said at least one predetermined vehicle parameter including at least one of vehicle mass, vehicle drag, vehicle rolling resistance, tire circumference, front area of vehicle, powertrain torque loss, vehicle tank capacity, and engine efficiency; accessing, by the processor, road terrain data indicative of at least one road terrain element, said at least one road terrain element including at least one of speed limit changes, road grade, air density, position, elevation, and traffic patterns; determining, by the processor, a first plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a random distribution of freight amongst the plurality of vehicles, the first plurality of freight distribution solutions comprising an initial generation; evaluating, by the processor, each of said first plurality of freight distribution solutions of the initial generation for fitness based on fuel economy and ranking each solution based on the evaluation; determining, by the processor, a second plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a distribution of freight amongst the plurality of vehicles according to a genetic algorithm structured to selectively execute a reproduction process first, a crossover process second, and a mutation process third, wherein the second plurality of freight distribution solutions includes a next generation; generating, by the processor, a first random number associated with the crossover process and a second random number associated with the mutation process; executing, by the processor, the crossover process responsive to the first random number being less than a crossover probability; executing, by the processor, the mutation process responsive to the second random number being less than a mutation probability; evaluating, by the processor, each of said freight distribution solutions of the next generation for fitness based on fuel economy, ranking each solution based on the evaluation, and assessing solution convergence based on the ranking; determining, by the processor, an optimal freight distribution based on said convergence assessment; and generating, by the processor, an electronic recommendation signal corresponding to said determined optimal freight distribution and communicating said recommendation signal to a receiver. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system adapted to determine freight distribution amongst multiple vehicles, comprising:
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an operator input module containing data indicative of at least one operator input, said at least one operator input including at least one of fuel cost, trip time, route beginning and ending points to define a route, and maximum vehicle speed; a vehicle parameter module containing data indicative of at least one predetermined vehicle parameter, said at least one predetermined vehicle parameter including at least one of vehicle mass, vehicle drag, vehicle rolling resistance, tire circumference, front area of vehicle, powertrain torque loss, vehicle tank capacity, and engine efficiency; a road terrain element module containing data indicative of at least one road terrain element, said at least one road terrain element including at least one of speed limit changes, road grade, air density, position, elevation, and traffic patterns; and a vehicle load distribution module adapted to; determine a first and second plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution from said first plurality comprising a random distribution of freight amongst the plurality of vehicles, the first plurality of freight distribution solutions comprising an initial generation, each solution from said second plurality comprising a distribution of freight amongst the plurality of vehicles according to a genetic algorithm structured to selectively execute a reproduction process first, a crossover process second, and a mutation process third, the second plurality of freight distribution solutions comprising a next generation; generate a first random number associated with the crossover process and a second random number associated with the mutation process; execute the crossover process responsive to the first random number being less than a crossover probability; execute the mutation process responsive to the second random number being less than a mutation probability; evaluate each of said first and second freight distribution solutions for fitness based on fuel economy and ranking each solution based on the evaluation, said fitness module being further adapted to assess solution convergence based on said ranking and determine an optimal freight distribution based on said convergence assessment; and generate an electronic recommendation signal corresponding to said determined optimal freight distribution and communicate said recommendation signal to a receiver. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A tangible, non-transitory computer program product comprising a computer useable medium having stored thereon computer-readable instructions for determining freight distribution amongst multiple vehicles, the computer-readable instructions being executable by a processor to perform operations comprising:
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accessing data indicative of at least one operator input, said at least one operator input including at least one of route beginning and ending points to define a route, number of vehicles, maximum vehicle freight weight for each of a plurality of vehicles, and total freight weight; accessing data indicative of at least one predetermined vehicle parameter, said at least one predetermined vehicle parameter including at least one of vehicle mass, vehicle drag, vehicle rolling resistance, tire circumference, front area of vehicle, powertrain torque loss, vehicle tank capacity, and engine efficiency; accessing data indicative of at least one road terrain element, said at least one road terrain element including at least one of speed limit changes, road grade, air density, position, elevation, and traffic patterns; determining a first plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a random distribution of freight amongst the plurality of vehicles, the first plurality of freight distribution solutions comprising an initial generation; evaluating each of said first plurality of freight distribution solutions of the initial generation for fitness based on fuel economy and ranking each solution based on the evaluation; determining a second plurality of freight distribution solutions based on said maximum vehicle freight weight for each of said vehicles and said total freight weight, each solution comprising a distribution of freight amongst the plurality of vehicles according to a genetic algorithm structured to selectively execute a reproduction process first, a crossover process second, and a mutation process third, the second plurality of freight distribution solutions comprising a next generation; generating a first random number associated with the crossover process and a second random number associated with the mutation process; executing the crossover process responsive to the first random number being less than a crossover probability; executing the mutation process responsive to the second random number being less than a mutation probability; evaluating each of said freight distribution solutions of the next generation for fitness based on fuel economy, ranking each solution based on the evaluation, and assessing solution convergence based on the ranking; determining an optimal freight distribution based on said convergence assessment; and communicating said optimal freight distribution to a display.
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Specification