decision support model
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This chapter presents a real case of a decision problem in supplier selection of one of the main raw materials of a wind blades industry. The study considered all currently qualified suppliers according to considerably rigorous standards and specifications and one in qualification process. It is a complex choice, given the strategic importance of the product and the multiplicity of criteria to be considered, both quantitative and qualitative. The strong competitiveness requires a special attention which concerns the supplier selection; not only the price matters; in fact, a day of stoppage due to failure in a delivery, for example, corresponds to high losses that would have justified the purchase from a supplier with a higher price but with no delivery failures. In order to contribute to the problem resolution, the methodologies PROMETHEE and AHP were applied, whose results allow the authors to stablish a ranking of the considered suppliers. The results will support the company on the selection of fiberglass suppliers and in some cases clarify where they can find the main trade-offs.

In order to support the decision-making process related to the reduction of land consumption into the urban regeneration interventions, the present research has the aim to define and propose a goal programming-based model that can be adopted for the negotiation phases of public and private subjects involved. In particular, the proposed model can provide for a range of feasible scenarios that, according to the specific purposes of the Public Administration, can be implemented in order to achieve the financial, environmental and social level of sustainability targets set by the Agenda 2030. In this way even the private entrepreneur can verify his personal convenience to participate in the investment. Furthermore, the possibility provided by the model to choose a different combination of urban parameters that define the convenience of interventions before their implementation, could reduce the increasingly significant problem of badly concluded interventions, interrupted because they lack an effective ex ante evaluation.

This study aims to develop a geographic information system (GIS)-based multi-criteria decision support model to create optimal plans for locating municipal solid waste (MSW) collection points. The model performs a series of consecutive GIS-based spatial analyses to determine alternative plans. Then, it weighs the alternatives considering the social, economic and environmental criteria to determine the optimum solution through analytical hierarchy process. The model was implemented as a case study in Çağdaş neighbourhood of Izmir, Turkey. A total of 42 locations were determined as the optimum collection points out of 245 possible collection points, which yields 39% reduction in collection points compared to the existing system. Total number of waste bins and average walking distance to waste collection points were calculated as 129 and 33 m, respectively. The municipal authority would spend 48.79 $ day−1 on fuel for waste collection and transport. In addition, daily air pollutant emissions generated during the operations were estimated as 2.052 g CO, 0.231 g NMVOC, 8.409 g NOx, 0.954 g N2O, 0.260 g NH3, 0.000227 g Pb and 0.0231 g PM 2.5. The results indicated that 14 out of 69 collection points in the existing collection system were not allocated to any waste source geographically. This study presents a unique method for planning MSW collection points on two key aspects: (1) development of a novel method to determine all possible collection point locations using Thiessen polygons and (2) presenting a holistic planning method considering the impacts of the collection system on the waste generators and waste collectors.

Improving Newborn Screening for Genetic Diseases Screening newborns for life-threatening genetic diseases is an important public health initiative. Cystic fibrosis is one of the most prevalent diseases in this context. As part of the cystic fibrosis screening process, all states in the United States use multiple tests, including genetic tests that detect a subset of the more than 300 genetic variants (specific mutations) that cause cystic fibrosis. In “Optimal Genetic Screening for Cystic Fibrosis,” El-Hajj, D.R. Bish, and E.K. Bish develop a decision support model to select which genetic variants to screen for, considering the trade-off between classification accuracy and testing cost, and the technological constraints that limit the number of variants selected. Because variant prevalence rates are highly uncertain, a robust optimization framework is developed. Further, two commonly used cystic fibrosis screening processes are analytically compared, and conditions under which each process dominates are established. A case study based on published data are provided.