Integrated Optimization Of Low Carbon Oriented Berth And Quay Crane Assignment Under Uncertainty

Container terminals play an important role in the global container transportation network.The efficient and reliable container handling service of the terminal is one of the main driving forces of maritime logistics.The performance of a container terminal depends largely on the efficiency of the terminal resources(mainly berths and quay cranes).The planning problems related to coastline are faced with the uncertainty of various parameters,which makes the effective planning of these operations more complex.In berth allocation planning(BAP),especially the uncertainty of estimated arrival time of vessels and the uncertainty of workload caused by loading and unloading containers have a crucial impact on the implementation of BAP.Improper handling will cause serious waste of resources for the whole port operation.At the same time,the environmental pollution of the port has been highly concerned by the government,and the green port has become the focus of the sustainable development of China’s shipping industry.The International Maritime Organization(IMO)puts forward that in the long run,it is a feasible solution to reduce carbon emissions for ports to levy carbon emission taxation.Therefore,based on the study of BAP in uncertain environment and BAP considering "green" factors in recent years,this thesis analyzes the impact of these two research points on traditional BAP problems,and develops a green,robust and lowcarbon oriented berth allocation and quay crane assignment planning.The uncertain factors include the estimated arrival time and the workload caused by the loading and unloading of containers.This thesis aims to reduce the carbon emissions caused by the operation of the quay crane by considering two different port carbon emission tax policies: single tax rate and segmented tax rate.A two-stage stochastic integer programming model based on a series of scenarios is established for the low-carbon oriented berth allocation and quay crane assignment plan in uncertain environment.Considering the uncertainty of vessel arrival time and the fluctuation of required workload,the first stage is the baseline schedule,including the berth,the time of entering and leaving the berth and the allocation of quay crane(QC).The second stage is adjustment plan based on baseline schedule,including the actual berth,the time of entering and leaving the berth and the allocation of the quay crane.In order to solve the above mathematical model,this thesis designs a heuristic method based on column generation(CG).Numerical experiments are conducted to validate the efficiency of our column generation-based solution method.The traditional CG process is used in two steps.One is at the second stage of the model for generating columns(i.e.,adjustment plans)for each scenario;the other is at the first stage for solving a transformed model that includes the baseline decision and the decision on selecting a suitable adjustment plan(one of the above-generated columns)for each scenario.And some steps embedded in the proposed method can be decomposable.Besides the specific BAPs investigated in this thesis,the proposed method could also be applied to similarly structured two-stage stochastic integer programming models.