Research On Collaborative Scheduling Path Optimization Of Pick-up And Delivery Of Shared Motorcycles

At present,China’s economy is in a long-term sustainable,healthy and highquality development.Many new first-tier cities have experienced large-scale population inflows,and the expansion of cities often takes a long time.Therefore,the rapid population growth will make the city’s transportation network more and more congested.This has brought a great impact on people’s daily travel,and the continuous increase in the number of private cars has also caused serious environmental pollution.Under such circumstances,shared motorcycles have undoubtedly become a new choice for green,healthy and pollution-free travel.However,in the actual dispatching process of shared motorcycles,the carrier company often dispatches vehicles to complete the two tasks of new car delivery and damage,waste,and out-of-area motorcycle recycling,which results in the inevitable waste of resources when the vehicles return with no load.Therefore,the research establishes a mathematical model for the problem of simultaneous pickup and delivery vehicles based on multi-parameter constraints,and designs a hybrid particle swarm algorithm to solve it,hoping to reduce the distribution cost of enterprises and improve transportation efficiency.Firstly,the research status of simultaneous pickup and delivery at home and abroad is reviewed,and in view of the problems existing in the scheduling process of existing shared motorcycles,based on theoretical research on vehicle routing problems,the maximum working hours of vehicles,Constraints such as the maximum load of the vehicle,combined with the freight transportation cost and the pollution emission cost of the vehicle with the change of the vehicle load,etc.,an optimization model of the vehicle route for the simultaneous pickup and delivery of shared motorcycles is proposed,and the pursuit of minimizing the distribution cost is carried out.Secondly,it finds that when solving discrete optimization problems such as vehicle routing problems,the ordinary particle swarm optimization algorithm has defects such as being easy to fall into local optimum and unable to update speed and position in this paper.Therefore,based on several classical intelligent optimization algorithms,the paper will The idea of simulated annealing and genetic crossover is added to the standard particle swarm optimization algorithm,an improved hybrid particle swarm optimization algorithm is designed,and the steps of this algorithm to solve the vehicle routing problem with simultanenous pick-up and delivery are explained in detail,and its specific search method is given.optimal process.Finally,a case study on actual data is carried out to verify the validity of the pickand-delivery vehicle routing model.In addition,through the comparison with other basic algorithms,it is also concluded that the hybrid particle swarm optimization algorithm has certain generality and superiority for solving such problems.