Energy efficient ship operation through speed optimisation in various weather conditions

Speed optimisation or speed management has been an attractive topic in the shipping industry for a long time. Traditional methods rely on masters’ experience. Some recent methods are more efficient but have many constraints, which preclude obtaining an optimum speed profile. This paper introduces a relatively advanced model for global speed optimisation towards energy efficient shipping in various weather conditions and shows the effect when the method is employed. With this model, if a ship type, departure and destination ports and fixed ETA (Estimated Time Arrival) are given, the stakeholders can be provided with a more reasonable speed operation plan for a certain commercial route, which leads to lower fuel consumption. Weather conditions and, hence, routing plays a very important role in this process. Several case studies over different shipping conditions are considered to validate the model

Maritime fleet deployment with speed optimization and voyage separation requirements

A shipping company operates a heterogeneous fleet of ships to service a given number of voyages on a number of trade routes over the planning horizon. Each ship has a predefined speed range within which it can sail. Fuel consumption, and hence fuel cost, significantly depends on the chosen speed. Furthermore, the shipping company makes Contracts of Affreightments with the shippers stating that the voyages on each trade route should be fairly evenly spread. This leads to the maritime fleet deployment problem with speed optimization and voyage separation requirements. We propose two formulations for this problem, i.e. one arc flow and one path flow model. The non-linear relationship for fuel consumption as a function of ship speed is linearized by choosing discrete speed points and linear combinations of these. Computational results show that the path flow model performs better than the arc flow model and that incorporating speed decisions in the fleet deployment gives better solutions and more planning flexibility.acceptedVersio

The LEANWIND suite of logistics optimisation and full lifecycle simulation models for offshore wind farms

The offshore wind sector has achieved significant cost reductions in recent years. However, there is still work to be done to maintain and surpass these savings across current and future farms. There is increased competition to reduce costs within the industry itself. Additional challenges are foreseen at future sites located further from shore, in harsher conditions and deeper waters. Larger turbines and projects also mean new equipment, logistics and maintenance requirements. Moreover, farms are approaching the decommissioning phase where there is little experience. Modelling is a safe and cost-effective way to evaluate and optimise operations. However, there is a lack of comprehensive decision-support tools, detailed enough to provide insight into the effects of technological innovations and novel strategies. To address the gap, the EU FP7 LEANWIND project developed a suite of state-of-the-art logistics optimisation and financial simulation models. They can assess a farm scenario in detail at every stage of the project lifecycle and supply-chain, identifying potential cost reductions and more efficient strategies. This paper introduces the models including: an overview of their scope and capabilities; how they can be applied; and the potential end users

Tramp ship routing and scheduling with voyage separation requirements

In this paper we explore tramp ship routing and scheduling. Tramp ships operate much like taxies following the available demand. Tramp operators can determine some of their demand in advance by entering into long-term contracts and then try to maximise profits from optional voyages found in the spot market. Routing and scheduling a tramp fleet to best utilise fleet capacity according to current demand is therefore an ongoing and complicated problem. Here we add further complexity to the routing and scheduling problem by incorporating voyage separation requirements that enforce a minimum time spread between some voyages. The incorporation of these separation requirements helps balance the conflicting objectives of maximising profit for the tramp operator and minimising inventory costs for the charterer, since these"br/"costs increase if similar voyages are not performed with some separation in time. We have developed a new and exact branch-and-price procedure for this problem. We use a dynamic programming algorithm to generate columns and describe a time window branching scheme used to enforce the voyage separation requirements which we relax in the master problem. Computational results show that our algorithm in general finds optimal solutions very quickly and performs much faster compared to an earlier a priori path generation method. Finally, we compare our method to an earlier adaptive large neighbourhood search heuristic and find that on similar-sized instances our approach generally uses less time to find the optimal solution than the adaptive large neighbourhood search method uses to find a heuristic solution. Document type: Boo

A heterogeneous fleet liner ship scheduling problem with port time uncertainty

We deal with a schedule design problem for a heterogeneous fleet liner shipping service under uncertain waiting and handling times at ports. In a liner shipping service, longer than expected waiting and handling times at a port may cause a delay from scheduled departure time. We consider the problem to find the departure times at ports and sailing times of ships between ports so that the total fuel burn is minimized while targeted overall service level (a performance measure based on on-time departure probabilities) is achieved. We consider two new aspects of the problem. The first one is the heterogeneous fleet where each ship type may have different fuel efficiency, i.e. a different fuel burn function. The second one is considering critical ports on the route, i.e. considering the fact that on-time performance at some critical ports might be more important for the shipping company. We propose a model which finds different service levels for different ship type-port pairs by considering importance of ports and fuel efficiencies of ships. We also give a new overall service level measure for the entire route by combining service levels for different ship type-ports pairs. We propose a chance constrained nonlinear mixed integer programming formulation for the problem. Finally, we give computational results that show the effects of several experimental factors on fuel consumption, speed and service level