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

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

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace