Research On The Whole Delivery Optimization Based On Bulk Terminal Information Platform

With the further expansion of water transport volume,the volume of terminals set up in Hong Kong has significantly increased and the competition among ports has become more fierce.In this context,the opacity of the transport process caused a mismatch between supply and demand of motor vehicles and cargoes,which in turn led to increased fleet transportation costs,cargo owners’ waiting costs and lowered the service level of Hong Kong side.Based on the port information platform,arranging the fleet dispatching scientifically and effectively is conducive to realizing the entire distribution process of the port.It has always been a hot spot for scholars to create profits for the Hong Kong side while providing quality services to shippers and their convoys.Therefore,it is of theoretical and practical significance to study the whole distribution optimization problem based on the bulk terminal information platform.This paper first summarizes the related researches at home and abroad,distinguishes the existing researches,and takes the whole distribution optimization problem based on the bulk terminal information platform as the main research direction of this paper.Secondly,considering the demand of goods,the supply of goods and the time window of social convoys,we focus on the transfer process of convoys among different customers,and optimize the goal of minimizing logistics costs,distribution costs,inventory costs and out-of-stock costs.Mixed Integer Nonlinear Programming Model with.After that,based on the characteristics of the whole distribution optimization problem of bulk terminal information platform,a hybrid genetic simulated annealing algorithm is designed.Finally,combining with the relevant data to solve the example,a specific fleet scheduling scheme is given.Relevant experiments show that the proposed scheme can effectively solve the whole distribution optimization problem based on bulk terminal information platform,validate the effectiveness of the proposed algorithm and model,and provide reference for future related research.