This study deals with an autonomous vessel scheduling problem when collaboration exists between port operators and an autonomous vessel company.A mixedinteger nonlinear programming model is developed,including decisio...This study deals with an autonomous vessel scheduling problem when collaboration exists between port operators and an autonomous vessel company.A mixedinteger nonlinear programming model is developed,including decisions in assigning autonomous vessels to berths at each port and the optimal arrival time of each vessel at each port in an entire autonomous shipping network.This study aims to minimize the total cost of fuel consumption and the delay penalty of an autonomous vessel company.The nonlinear programming model is linearized and further solved using off-the-shelf solvers.Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels.Results show that a company may speed up an autonomous vessel with short-distance voyage once fuel price decreases to gain additional benefits.展开更多
This paper studied a tactical liner shipping schedule design issue under sail and port time uncertainties,which is the determination of the planned arrival time at each port call as well as the punctuality rate and nu...This paper studied a tactical liner shipping schedule design issue under sail and port time uncertainties,which is the determination of the planned arrival time at each port call as well as the punctuality rate and number of assigned ship on the route.A number of studies have tried to introduce the operational speed adjustment measure into this tactical schedule design issue,to alleviate the discrepancies between designed schedule and maritime practice.On the one hand,weather conditions can lead to speed loss phenomenon of ships,which may result in the failure of ships’punctual arrivals.On the other hand,improving the ability of speed adjustment can decrease the late-arrival compensation,but increase the fuel consumption cost.Then,we formulated a machine learning-based liner shipping schedule design model aiming at above-mentioned two limitations on speed adjustment measure.And a machine learning-based approach has been designed,where the speed adjustment simulation,the neural network training and the reinforcement learning were included.Numerical experiments were conducted to validate our results and derive managerial insights,and then the applicability of machine learning method in shipping optimization issue has been confirmed.展开更多
基金This study is supported by the National Natural Science Foundation of China(No.71701178).
文摘This study deals with an autonomous vessel scheduling problem when collaboration exists between port operators and an autonomous vessel company.A mixedinteger nonlinear programming model is developed,including decisions in assigning autonomous vessels to berths at each port and the optimal arrival time of each vessel at each port in an entire autonomous shipping network.This study aims to minimize the total cost of fuel consumption and the delay penalty of an autonomous vessel company.The nonlinear programming model is linearized and further solved using off-the-shelf solvers.Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels.Results show that a company may speed up an autonomous vessel with short-distance voyage once fuel price decreases to gain additional benefits.
基金the National Natural Science Foundation of China(Nos.71572022 and 61473053)the National Social Science Foundation of China(No.18VHQ005)。
文摘This paper studied a tactical liner shipping schedule design issue under sail and port time uncertainties,which is the determination of the planned arrival time at each port call as well as the punctuality rate and number of assigned ship on the route.A number of studies have tried to introduce the operational speed adjustment measure into this tactical schedule design issue,to alleviate the discrepancies between designed schedule and maritime practice.On the one hand,weather conditions can lead to speed loss phenomenon of ships,which may result in the failure of ships’punctual arrivals.On the other hand,improving the ability of speed adjustment can decrease the late-arrival compensation,but increase the fuel consumption cost.Then,we formulated a machine learning-based liner shipping schedule design model aiming at above-mentioned two limitations on speed adjustment measure.And a machine learning-based approach has been designed,where the speed adjustment simulation,the neural network training and the reinforcement learning were included.Numerical experiments were conducted to validate our results and derive managerial insights,and then the applicability of machine learning method in shipping optimization issue has been confirmed.
