Search results

In the Norwegian offshore oil industry, helicopters have been used as a major mode of transporting personnel to and from offshore installations for decades. Helicopter transportation represents one of the major risks for offshore employees. The purpose of this paper is to study the safety of helicopter transportation in terms of the expected number of fatalities on an operational planning level.

Based on an analysis of helicopter accidents, this paper proposes a mathematical model that can aid in the planning of routes for the fleet in order to minimize the expected number of fatalities.

A theorem proven in this paper tells that hub-and-spoke configuration is the best way of routing helicopters in terms of minimizing expected number of fatalities. Computational results indicate that the expected number of fatalities may be reduced at the expense of longer travel time by implementing the proposed method into planning of routes for helicopter fleet.

The main limitation is the present inability to solve large problem instances.

The suggested tool is able to provide decision makers with a set of solutions from which they can choose the one with the best trade-off between travel time and transportation safety.

The mathematical model and theoretical results for route planning with a safety-based objective are original.

In the Norwegian oil and gas industry the upstream logistics includes providing the offshore installations with needed supplies and return flow of used materials and equipment. This paper considers a real‐life routing problem for supply vessels serving offshore installations at Haltenbanken off the northwest coast of Norway from its onshore supply base. The purpose of the paper is to explore how the offshore installation's limited storage capacity affects the routing of the supply vessels aiming towards creating efficient routes.

A simplified version of the real‐life routing problem for one supply vessel is formulated as a mixed integer linear programming model that contains constraints reflecting the storage requirements problem. These constraints ensure that there is enough capacity at the platform decks and that it is possible to perform both pickup and delivery services.

The model has been tested on real‐life‐sized instances based on data provided by the Norwegian oil company Statoil ASA. The tests show that in order to obtain optimal solutions to the pickup and delivery problem with limited free storage capacities at installations, one has to include in the formulation the new sets of constraints, the storage feasibility and the service feasibility requirements. In addition, two visits to some platforms are necessary to obtain optimality.

The main limitation is the present inability to solve large cases.

The contribution of this paper is to provide a better insight into a real‐life routing problem which has a unique feature arising from the limited deck capacity at the offshore installations that complicates the performance of service. This feature has neither been discussed nor modeled in the vehicle routing literature before, hence the formulation of the problem is original and reveals some interesting results.

In inventory theory the inventory holding cost per unit plays an important part. In most transactions concerning selling and buying a certain postponement of payment is offered or accepted by the seller. This should have some consequences for the order size and can be regarded as a kind of discount. Traditionally, when average costs (AC) are used, these kinds of effects are not explicitly incorporated in the classical formulas for economic order quantities (EOQs). On the other hand, such effects have been treated to a certain degree in the literature when a present value criterion (PVC) is used to estimate the inventory holding costs over a certain time interval. However, in these models one does not differentiate between the holding costs incurred by the capital tied up in the inventory and other costs incurred by storing an item. Approaches this problem in an AC manner, but, opposed to the PVC, splits the inventory holding costs into two parts. Offers an EOQ formula for the simple case of a single item stored; enhances this formula for a situation where a family of items are ordered in a co‐ordinated way, and into a situation with stochastic demand for a single item. Finally, interprets the postponed payment in terms of an all unit discount.