Elin Espeland Halvorsen-Weare

Routing and scheduling in a liquefied natural gas shipping problem with inventory and berth constraints

Liquefied natural gas (LNG) is natural gas that has been transformed to liquid form for the purpose of transportation, which is mainly done by specially built LNG vessels travelling from the production site to the consumers. We describe a real-life ship routing and scheduling problem from the LNG business, with both inventory and berth capacity constraints at the liquefaction port. We propose a solution method where the routing and scheduling decisions are decomposed. The routing decisions consist of deciding which vessels should service which cargoes and in what sequence. The scheduling decisions are then to decide when to start servicing the cargoes while satisfying inventory and berth capacity constraints. The proposed solution method has been tested on several problem instances based on the real-life problem. The results show that the proposed solution method is well suited to solve this LNG shipping problem.

Optimal fleet composition and periodic routing of offshore supply vessels

The supply vessel planning problem is a maritime transportation problem faced by amongst others the energy company Statoil. A set of offshore installations requires supplies from an onshore supply depot on a regular basis, a service performed by a fleet of offshore supply vessels. The problem consists of determining the optimal fleet composition of offshore supply vessels and their corresponding weekly voyages and schedules. We present a voyage-based solution method for the supply vessel planning problem. A computational study shows how the solution method can be used to solve real-life problems. Statoil has implemented a planning tool based on the voyage-based solution method and reports significant savings.

Robust supply vessel planning

In the supply vessel planning problem, a set of offshore installations receives supplies from an onshore supply depot on a regular basis. This service is performed by a fleet of offshore supply vessels. The supply vessel planning problem then consists of determining the optimal fleet size and mix of supply vessels and the corresponding weekly voyages and schedules. This is a real planning problem faced by among others the energy company Statoil. In a previous study this problem was examined and a deterministic voyage based solution approach presented. In this study we address the problem of creating robust schedules to the supply vessel planning problem. Several approaches are tested and compared in a computational study, and the results show that there is an improvement potential if some robustness considerations are made when finding a solution to the supply vessel planning problem.

Vessel routing and scheduling under uncertainty in the liquefied natural gas business

Liquefied natural gas (LNG) is natural gas transformed into liquid state for the purpose of transportation mainly by specially built LNG vessels. This paper considers a real-life LNG ship routing and scheduling problem where a producer is responsible for transportation from production site to customers all over the world. The aim is to create routes and schedules for the vessel fleet that are more robust with respect to uncertainty such as in sailing times due to changing weather conditions. A solution method and several robustness strategies are proposed and tested on instances with time horizons of 3-12months. The resulting solutions are evaluated using a simulation model with a recourse optimization procedure. The results show that there is a significant improvement potential by adding the proposed robustness approaches.

The bi-objective mixed capacitated general routing problem with different route balance criteria

In the mixed capacitated general routing problem, one seeks to determine a minimum cost set of vehicle routes serving segments of a mixed network consisting of nodes, edges, and arcs. We study a bi-objective variant of the problem, in which, in addition to seeking a set of routes of low cost, one simultaneously seeks a set of routes in which the work load is balanced. Due to the conflict between the objectives, finding a solution that simultaneously optimizes both objectives is usually impossible. Thus, we seek to generate many or all efficient, or Pareto-optimal, solutions, i.e., solutions in which it is impossible to improve the value of one objective without deterioration in the value of the other objective. Route balance can be modeled in different ways, and a computational study using small benchmark instances of the mixed capacitated general routing problem demonstrates that the choice of route balance modeling has a significant impact on the number and diversity of Pareto-optimal solutions. The results of the computational study suggest that modeling route balance in terms of the difference between the longest and shortest route in a solution is a robust choice that performs well across a variety of instances.