a b s t r a c tCost of energy generated from offshore wind is impacted by maintenance cost to a great extent. Cost of maintenance depends primarily on the strategy for performing maintenance. In this paper a maintenance cost model for offshore wind turbine components following multilevel opportunistic preventive maintenance strategy is formulated. In this strategy, opportunity for performing preventive actions on components is taken while a failed component is replaced. Two kinds of preventive actions are considered, preventive replacement and preventive maintenance. In the former, components that undergo that action become as good as new (i.e., the replaced components, are not just as good as new, but are actually new), but in the latter, ages of components are reduced to some degree depending on the level of maintenance action. Total cost associated with maintenance depends on the setting of age groups that determine which component should be preventively maintained and to what degree. Through optimum selection of the number of age groups, cost of maintenance can be minimized. A model is formulated where total maintenance cost is expressed as a function of number of age groups for components. A numerical study is used to illustrate the model. The results show that total cost of maintenance is significantly impacted by number of age groups and age thresholds set for components.

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During natural or anthropogenic disasters, humanitarian organizations face a series of time-sensitive tasks.One of the tasks involves picking up critical resources (e.g., first aid kits, blankets, water) from warehouses and delivering them to the affected people. To successfully deliver these items to the people in need, the organization needs to make decisions that range from the quick acquisition of vehicles from the local market, to the preparation of pickup and delivery schedules and vehicle routes. During crises, the supply of vehicles is often limited, their acquisition cost is steep, and special rental periods are imposed. At the same time, the affected area needs the aid materials as fast as possible, and deliveries must be made within due time. Therefore, it is imperative that the decisions of acquiring, scheduling, and routing of vehicles are made optimally and quickly. In this paper, we consider a variant of a truckload open vehicle routing problem with time windows, which is suitable for modeling vehicle routing operations during a humanitarian crisis.We present two integer linear programming models to formulate the problem. The first one is an arc-based mixed integer linear programming model that is solved using a general purpose solver. The second one, on the other hand, is based on a path-based formulation, for which we design a column generation framework so as to solve it. Finally, we perform numerical experiments and compare the performance of the two models. The comparison shows that the latter path-based formulation outperforms the former without sacrificing solution quality when employing our column generation framework.

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Thus far, limited research has been performed on resilient supplier selection-a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise decision relevant information (DRI), this problem becomes even more difficult to solve with the classical optimization approaches. Possibility distribution based Multi-Criteria Decision Analysis (MCDA) is a viable alternative approach for handling inherent uncertainty of imprecise DRI associated with the evaluation offered by a group of contradicting decision makers. However, prior research focusing on MCDA based supplier selection problem has been lacking in the ability to provide a seamless integration of numerical and linguistic evaluation criteria along with the consideration of multiple decision makers. To address these challenges, we present a comprehensive decision-making framework for ranking a set of suppliers from resiliency perspective. The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers. We adapt two popular tools -Single Valued Neutrosophic Sets (SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extend them to incorporate both crisp and linguistic evaluations in a group decision making platform to obtain aggregated SVNS and IVFS decision matrix. This information is then used to rank the resilient suppliers by using TOPSIS method. We present a case study to illustrate the mechanism of the proposed algorithm. A sensitivity analysis demonstrates the strength of the proposed algorithm to generate alternative ranking scheme with respect to the priorities of evaluation criteria, and thus shows the potential to provide a reliable decision-making framework.