Research On Route Optimization Of Multi-modal Transport With Timetable Limit For Stochastic Time-dependent Networks

In the route optimization of multi-modal transport networks,there are two major practical problems.First,influenced by traffic management,weather condition,traffic condition and seasonal change,the transport time changes accordingly.As a result,the transport scheme suggested under these conditions can not meet the requirements of the enterprises.Secondly,there are timetable limit on the various transportation modes in the multi-modal transport.For example,the liners in water and railway transport will inevitably affect the transportation scheme.Therefore,to effectively deliver goods to a destination in a complex multi-modal transport networks,it is of great importance to select optimal transport route.This paper summarizes the researches on multi-modal transport route optimization at home and abroad.By studying multi-modal transport route optimization considering timetable limit in static networks,time-dependent networks as well as stochastic time-dependent networks,the paper mainly investigates the multi-modal transport route with timetable limit in stochastic time-dependent networks in an easy-to-difficult hierarchical order.The main research works are as follows:(1)The multi-modal transport route optimization with timetable limit in static networks is studied and a multi-modal transport route optimization model with timetable limit is constructed.Besides,the paper designs a genetic algorithm relied on connected path coding method,based on which an adaptive genetic algorithm and an annealing genetic algorithm are designed to solve the problem.Cases are then applied and the three algorithms are compared and analyzed accordingly.The research results show that the scheduling of railway,waterway and other transportation vehicles will affect the decision of multi-modal transport route decision.The model and algorithm is able to quickly select transportation plan with the least time according to the requirements of the decision makers,and thus providing decision support for decision makers.(2)The multi-modal route optimization problem with timetable limit in timedependent networks is studied.For the FIFO problem of time-dependent networks,the travel speed is introduced to calculate the travel time of different road segments,and the non-cross-time driving,partial cross-time driving,and full cross-time driving are all considered.Moreover,the situation when vehicle can not drive out the cross-time driving problem in one or more periods of time under the conditions of this road segment is also investigated,and the calculation method of the travel time of the road segments is designed.Finally,a multi-modal transport route optimization model with timetable limit in the time-dependent networks is constructed.The genetic algorithm solving process based on connected path coding method is customized for this problem.On this basis,an adaptive genetic algorithm and an annealing genetic algorithm are designed to solve the problem,and are applied to cases.At the same time,the customized three algorithms are compared and analyzed.(3)The problem of multi-modal transport route optimization with scheduling limit in stochastic time-dependent networks is studied.Since the stochastic time-dependent networks weights are random,the paper adopted Monte Carlo method to obtain the route reliability by Monte Carlo simulation.The multi-modal transport route optimization model in stochastic time-dependent networks with scheduling limit,time window and other constraints is constructed.On the basis of the genetic algorithm relied on the connected path coding method,and in view of the travel time distribution,the Monte Carlo method is adopted to calculate the reliability of the route selected.In the process of calculating the reliability,to solve FIFO problem in the stochastic time-dependent networks,the calculating method designed in multi-modal transport route optimization in time-dependent networks with timetable limit is adopted to calculate the travel time and the problem is solved by the Monte Carlo adaptive genetic algorithm and the Monte Carlo annealing genetic algorithm based on connected path coding method and explained in cases.And meanwhile,the three algorithms designed are also compared and analyzed in this part.The results show that the three algorithms designed in the paper are applicable to multi-modal transport route selection with timetable limit in stochastic time-dependent networks and could provide decision-making references for enterprises to choose the best transportation route.Starting from the actual operation of multi-modal transport,this paper constructs a model based on the two practical problems in optimizing multi-modal transport path,and designs a genetic algorithm in accordance with the NP-hard characteristic of the model.Moreover,in the process of algorithm crossover and mutation,the situation that there will be no path or loops has been fully considered and corresponding solutions has been given accordingly.The research done in this paper,therefore,will further enrich the research results of multi-modal transport path optimization,and is of certain theoretical and practical significance.