Operation Optimization Of Container Terminals Considering Quay Crane Emergencies

In order to keep the dominant position of container terminals in win-win coopetition,terminal operators have to provide a safe and effective service of loading and unloading.As the most important quayside handling equipment,quay cranes,because of their big volume,high cost and the characteristic of being mutually non-crossing,once caught in accidents or failures,will severely affect the implementation of terminal operation plan and the ship turnaround time,causing enormous economic loss.This research,targeting at the resource re-distribution problem of container terminals during quay crane emergencies,covers the following three issues:(1)Targeting at the uncertainty of the quay crane emergency duration,this research constructs an emergency duration prediction model based on the reasoning of the quay crane emergency severity.Grounded on the classification and causation-identification of quay crane emergencies,this research constructs a fault tree which is then converted into a Bayesian network.Furthermore,this research endeavors to optimize the Bayesian network first by combining the algoritlum of Greedy Thick Thinning and the opinions of the experts,and then by determining the network parameter through machine learning.Thus,a Bayesian network is established for the reasoning of the quay crane emergency severity,and on this basis,a functional relationship model which reflects the interactions between the emergency duration and its type,occurrence time and the emergency severity,is constructed.The case study verifies the effectiveness of the proposed emergency duration prediction model,which provides the basic data for the decision-making of resource re-distribution during quay crane emergencies.(2)Targeting at the re-distribution of berth and quay crane in consideration of the uncertain duration of quay crane emergencies,this research constructs a disruption restoration model for the integrated berth allocation and quay crane assignment.Taking regard of the practical restraints such as the position and the movability of failed quay cranes and taking the probability distribution of the predicted emergency duration as an input,this research aims at minimizing the expectation of the re-distribution results under different emergency durations.In order to evaluate the adaptability of the disruption restoration plan,an index of adaptability coefficient is introduced.According to the characteristic of the problem,a combination of heuristic and genetic algorithms is applied for the solution method,and then the influence of the position and duration of failed quay cranes on the re-distribution is examined.On the planning level,this research develops a method to optimize the re-distribution of berth and quay crane considering the uncertainty of the duration of quay crane emergencies.The case study verifies the effectiveness and practicality of the model in the circumstances which face uncertain duration of emergencies.(3)Targeting at optimizing the operation sequence of quay cranes,internal trucks and yard cranes,this research constructs an optimization model of integrated scheduling of quay cranes,internal trucks and yard cranes based on the disruption restoration model.With the principal of system optimal,this research takes the minimization of the total cost of the terminal system as the objective function.Moreover,considering the practical restraints such as the movability of failed quay cranes,as well as the complexity and randomicity of quay cranes,internal trucks and yard cranes in their practical operation,this research designs a solution framework that combining the optimization and simulation.On the operating level,this research works out a method to optimize the integrated scheduling of quay cranes,internal trucks and yard cranes based on the disruption restoration model.The case study verifies the effectiveness of the proposed constraints,the objective function and the solution for the integrated scheduling of quay cranes,internal trucks and yard cranes in face of quay crane emergencies.