Research Of Scheduling Optimization Model And Algorithm On Automotive Ro-Ro Terminals

The world economy is developing rapidly,the market demand for automobiles has been increasing dramatically.Since the establishment of China’s First Automobile Manufacturing Plant in Changchun,Jilin in the1950 s,automobile production and sales have increased significantly and China’s automobile manufacturing industry has made great strides and is now in a booming phase.China’s automobile sales rose from the third place in the world in 2010 to the first place in the world in 2019,and during this period,automobile production has been maintained as the first in the world.Automobile transportation is an important part of the delivery process of commercial cars,there are three general ways of automobile transportation: road transportation,railroad transportation and waterway transportation,but as an important component of automobile transportation,waterway transportation,there are still many problems that need to be improved.In this thesis,the scheduling research based on RO-RO terminals can contribute some guidelines and suggestions on how to improve the current situation,supporting companies to achieve the goal of reducing costs and increasing efficiency for improving the competitiveness in industry.This thesis studies the RO-RO terminal scheduling problem covering three major parts of daily RO-RO terminal operations: berth allocation,labor allocation,and yard allocation.Firstly,this thesis systematically studies the daily operation system of RO-RO terminals.The components of the RO-RO terminal scheduling system are introduced from two perspectives,entity and organization,respectively,and then the daily operation process of RO-RO terminals is introduced.The differences between RO-RO terminals and container terminals are analyzed,and the existing problems of RO-RO terminals are pointed out,and the research theme of this thesis is established based on the above system analysis.Secondly,the two problems covered by the research topic are modeled and analyzed separately,and the fast solution algorithms are designed.In Chapter 3 for the joint scheduling problem of RO-RO terminal berth allocation and labor allocation and Chapter 4 for the joint scheduling problem of RO-RO terminal yard allocation and labor allocation,the corresponding mixed-integer programming models are established based on the description and analysis.And an improved genetic algorithm and a heuristic algorithm based on column generation are designed respectively.A multi-group parallel evolutionary genetic algorithm is designed to solve this problem,and a constructive heuristic algorithm is proposed to calculate the individual fitness value in the improved genetic algorithm.The heuristic algorithm based on column generation is composed of three algorithms in three stages,consisting of a genetic algorithm in the first stage to provide initial feasible solutions for the column generation algorithm in the second stage,and a branch-and-bound algorithm in the third stage to generate highquality integer solutions based on the computational results of the column generation algorithm.Finally,the validity and practicality of the established mathematical models and the designed algorithms are verified through numerical experiments and case studies.Several sets of numerical experiments are designed and generated to prove the correctness of the models and to verify the effectiveness of the algorithms.The experimental results show that the genetic algorithm designed for berth allocation can solve the case containing60 ships and 120 berth sections in 15 minutes of computation time,and the heuristic algorithm based on column generation designed for yard allocation can solve the case containing 200 orders and 100 yard areas sections in 3minutes.The practicality of the algorithm was verified based on real cases,and it proved that the algorithm can efficiently generate optimal scheduling solutions for RO-RO terminals with much better performance than commercial solver and actual planning sections.The case study results show that the improved genetic algorithm can reduce the total vessel service time by 12.94% in the daily schedule for the berthing planning department,and the heuristic algorithm based on column generation can reduce the necessary travel distance of commercial cars by 33.72% in the daily schedule for the yard planning department.