On using simulation to model the installation process logistics for an offshore wind farm

The development of offshore wind farms (OWFs) in Europe is progressing to sites which are characteristically further from shore, in deeper waters, and of larger scale than previous sites. A consequence of moving further offshore is that installation operations are subject to harsher weather conditions, resulting in increased uncertainty in relation to the cost and duration of any operations. Assessing the comparative risks associated with different installation scenarios and identifying the best course of action is therefore a crucial problem for decision makers. Motivated by collaboration with industry partners, we present a detailed definition of the OWF installation process logistics problem, where aspects of fleet sizing, composition, and vessel scheduling are present. This article illustrates the use of simulation models to improve the understanding of the risks associated with logistical installation decisions. The developed tool employs a realistic model of the installation operations and enables the effect of any logistical decision to be investigated. A case study of an offshore wind farm installation project is presented in order to explore the impact of key logistical decisions on the cost and duration of the installation, and demonstrates that savings of up to 50% can be achieved through vessel optimization

An interactive approach for biobjective integer programs under quasiconvex preference functions

We develop an interactive algorithm for biobjective integer programs that finds the most preferred solution of a decision maker whose preferences are consistent with a quasiconvex preference function to be minimized. During the algorithm, preference information is elicited from the decision maker. Based on this preference information and the properties of the underlying quasiconvex preference function, the algorithm reduces the search region and converges to the most preferred solution progressively. Finding the most preferred solution requires searching both supported and unsupported nondominated points, where the latter is harder. We develop theory to further restrict the region where unsupported nondominated points may lie. We demonstrate the algorithm on the generalized biobjective traveling salesperson problem where there are multiple efficient edges between node pairs and show its performance on a number of randomly generated instances

An evolutionary approach to generalized biobjective traveling salesperson problem

We consider the generalized biobjective traveling salesperson problem, where there are a number of nodes to be visited and each node pair is connected by a set of edges. The final route requires finding the order in which the nodes are visited (tours) and finding edges to follow between the consecutive nodes of the tour. We exploit the characteristics of the problem to develop an evolutionary algorithm for generating an approxiMation of nondominated points. For this, we approximate the efficient tours using approximate representations of the efficient edges between node pairs in the objective function space. We test the algorithm on several randomly-generated problem instances and our experiments show that the evolutionary algorithm approximates the nondominated set well

Constructing a strict total order for alternatives characterized by multiple criteria: An extension

The problem of finding a strict total order for a finite set of multiple criteria alternatives is considered. Our research extends previous work by us, which considered finding a partial order for a finite set of alternatives. We merge the preference information extracted from the preference cones and corresponding polyhedral sets, with the information derived from pairwise comparisons of two alternatives, yielding a preference matrix. This preference matrix is used as input to an integer programming model to obtain a strict total order that provides a transitive ranking for the set of alternatives. (c) 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 155-163, 201