Tramp Fleet Routing And Scheduling Problem Under Uncertain Operating Condition

Characteristics of irregular fixed ports and no fixed routes make the owners of tramp ships face to a market with a lot of information full of uncertainty.How can the ship’s specific voyages be assigned based on the known market information.The task is fully examining the wisdom of decision makers.In view of the above problems,this paper first allocates fleet resources in medium and short term market.Through the establishment of SVM optimization model,according to the historical data in recent years to predict the possible occurrence of various situations,the possibility of a variety of situations,we provide a balance of risk and market opportunities for fleet resource allocation method.After determining the location of the ships at the end of the planning period,we continue to study the ships or cargoes may be affected by external uncertain factors affecting the occurrence of schedule delays,the case of changes,how to schedule the path to visit the entire fleet of the cargoes.We will generalize these uncertainties as cases where the ship on behalf of the transport demand may undergo various changes relate to the ship that represents the supply of the transport.In the real market,many cargoes have a strong randomness in the time window.In view of this kind of problem,we will focus on the problem that the time window has random ship routing and scheduling problem.In this paper,a ship berth time survey is carried out in some ports with large export volume.It is concluded that the waiting time of the ship in some ports is in accordance with certain statistical laws.Combining the acceptability of different shipowners for the time of berths,uncertain time window constraints can be transformed into deterministic constraints.Through the multi-stage model construction and the case calculation,we can get the satisfactory effect of the model design efficiency and the precision of the solution,which can provide the corresponding decision for the decision makers in the real market when facing many uncertain factors.