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Ezekiel Moore
Ezekiel Moore

Expert Choice Ahp


Expert Choice is intuitive, graphically based and structured in a user-friendly fashion so as to be valuable for conceptual and analytical thinkers, novices and category experts. Because the criteria are presented in a hierarchical structure, decision makers are able to drill down to their level of expertise, and apply judgments to the objectives deemed important to achieving their goals. At the end of the process, decision makers are fully cognizant of how and why the decision was made, with results that are meaningful, easy to communicate, and actionable.




Expert Choice Ahp



In response to initial expert feedback, we simplified the AHP model (limited the number of objectives to those on which data were available and included only medications considered second-line in the recent statement on management of hyperglycemia [2]; (Fig C in S2 File) and specified a patient population, the type of decision being made (e.g., regulatory or for an individual patient), and the relevant step in the sequence of therapy (first-line versus add-on) (Fig D in S2 File). Although our preliminary model included all medications and outcomes, all medication options and outcomes could not be included in the final AHP model. Pragmatic considerations, input from experts and the specific decision context dictated the final choice of medications and outcomes. As an example, rosiglitazone was excluded because of lack of use in the United States. Lactic acidosis, a well-known complication, was excluded because it was too rare and had an unknown incidence.


A major strength of our study is the iterative process used to develop the AHP model and instrument. While participants were clinical or research experts in diabetes, they did not have experience with the AHP, and with feedback and refinement, we conducted an AHP with results that were reasonable to participants. Our protocol can be easily translated to other decision contexts.


Our model for this AHP certainly excludes important outcomes such as macro- and microvascular complications, quality of life, and cost as well as many treatment alternatives. We removed many of these from our model in response to piloting with diabetes experts and decided to rely on HbA1c as the main benefit and exclude consideration of cost to be consistent with the FDA convention to making decisions about diabetes medications. We were also limited by the lack of data on many outcomes such as quality of life, and sought to limit the number of objectives to maintain decisional capacity and minimize respondent burden. Additional objectives and treatment alternatives could be added to the model.


In this study, the AHP is composed of four levels. Level 1 consists of the goal of choice of for selecting the most suitable method for the fabrication of drug loaded nanoparticles. Level 2 contains four main criteria, namely operational performance, machinery information, process output and production cost. Level 3 encompasses eleven sub-criteria, it represents different intensities of the criterion. Level 4 consists five alternatives, these can be used to reach the goal. The judgment on pairwise comparisons of the AHP is carried out by using Saaty's discrete 9-value scale method shows in Table 1.


So the main goal is to choose the right contractor for building the swimming pool. The main objectives of contractor choice are as follows: to achieve good quality; to achieve good design; to select optimal financial options.


equipment. These are main criteria when choosing contractor. Also sub-criteria should be taken into account. For example, when evaluating capacity, number of projects on which contractor is currently working should be evaluated as well as capacity to add this exact project. Based on the literature overview, and questioning of experts and stakeholders the criteria set was determined. So there are two levels in this model criteria and sub criteria.


2.The nine-stage model for solving decision making problems in construction have been suggested. Based on the literature overview and opinion of experts set of criteria was determined: a) technical experience; b) performance recourses; c) financial stability; d) management performance and employees qualification; e) capacity; f) safety record; g) operation and equipment.


While Taiwanese hospitals dispose of large amounts of medical waste to ensure sanitation and personal hygiene, doing so inefficiently creates potential environmental hazards and increases operational expenses. However, hospitals lack objective criteria to select the most appropriate waste disposal firm and evaluate its performance, instead relying on their own subjective judgment and previous experiences. Therefore, this work presents an analytic hierarchy process (AHP) method to objectively select medical waste disposal firms based on the results of interviews with experts in the field, thus reducing overhead costs and enhancing medical waste management. An appropriate weight criterion based on AHP is derived to assess the effectiveness of medical waste disposal firms. The proposed AHP-based method offers a more efficient and precise means of selecting medical waste firms than subjective assessment methods do, thus reducing the potential risks for hospitals. Analysis results indicate that the medical sector selects the most appropriate infectious medical waste disposal firm based on the following rank: matching degree, contractor's qualifications, contractor's service capability, contractor's equipment and economic factors. By providing hospitals with an effective means of evaluating medical waste disposal firms, the proposed AHP method can reduce overhead costs and enable medical waste management to understand the market demand in the health sector. Moreover, performed through use of Expert Choice software, sensitivity analysis can survey the criterion weight of the degree of influence with an alternative hierarchy.


Abstract:Cutting tool selection plays an important role in achieving reliable quality and high productivity work, and for controlling the total cost of manufacturing. However, it is complicated for process planners to choose the optimal cutting tool when faced with the choice of multiple cutting tools, multiple conflict criteria, and uncertain information. This paper presents an effective method for automatically selecting a cutting tool based on the machining feature characteristics. The optimal cutting tool type is first selected using a proposed multicriteria decision-making method with integrated fuzzy analytical hierarchy process (AHP). The inputs of this process are the feature dimensions, workpiece stability, feature quality, specific machining type, and tool access direction, which determine the cutting tool type priority after evaluating many criteria, such as the material removal capacity, tool cost, power requirement, and flexibility. Expert judgments on the criteria or attributes are collected to determine their weights. The cutting tool types are ranked in ascending order by priority. Then, the rule-based method is applied to determine other specific characteristics of the cutting tool. Cutting tool data are collected from world-leading cutting tool manufacturer, Sandvik, among others. An expert system is established, and an example is given to describe the method and its effectiveness.Keywords: expert system; cutting tool type selection; fuzzy AHP; multicriteria decision making; milling process


Composite indices are a great tool for researchers and policymakers alike as they provide a simplification of reality of complex phenomena, as well as their enabling ability for cross-country comparisons. A troublesome issue with constructing composite indices is the selection of the weighting system as it can greatly influence the results of the index developed. One of the most reliable weighting systems is the expert weighting system, where experts on the topic being studied are delegated the weight selection process, and the average of their responses are then transformed into weights. The limitation of this method, however, is the high subjectivity, uncertainty, and inconsistency of the expert responses. This paper seeks to address this limitation by providing a guide to researchers on how to improve the expert weights by subjecting them to the fuzzy analytic hierarchy process (FAHP) method for multicriteria decision making (MCDM) to compute the fuzzy weights, a more objective and reliable weights relative to expert weights. That said, and despite the benefits of the FAHP method, it can produce weights that can skew the composite index results. To address this limitation, the study introduces the interval weights, which are calculated by finding the midpoint between the expert weights and the fuzzy weights. The resulting interval weights exhibit the benefits of both principal component analysis (PCA) and the FAHP process, the difference being that PCA cannot be applied for noncompensatory indices.


Like PCA, the FAHP method highlights the most prominent proxies utilized in the construction of the indices and assigns them the highest weight. However, FAHP is advantageous over PCA as the latter can only be applied to substitutable indicators or fully compensatory indices [1], whilst the former can be applied to indices of various compensatory nature. By integrating expert responses and the FAHP method, the researcher can improve the objectivity of the weights and reduce the uncertainty of the expert responses.


With that said, why FAHP over AHP? FAHP is preferred over AHP since the latter does not deal well with uncertainty. To elaborate, summarizing expert opinion on a particular proxy and transforming them into a single value has a high degree of uncertainty, which could be related to a poor understanding of the task, expert bias, low interest in the survey by the experts, or simply human error, all of which could lead to inaccurate outcomes. Moreover, AHP is more appropriate for more straightforward or crisp decisions [10], whereas FAHP is more appropriate where ambiguity and fuzzy outcomes are likely present. As such, and due to the forestated research gaps, the FAHP method is better suited than the AHP method when collecting expert responses for developing composite indices as this research is attempting.


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