Probability

Decision Modelling and Decision AnalysisJOB SELECTION

A research on the job location revealed the most expensive and least expensive cities. Probability scores in the range of 1-10 were used to rank the various alternatives on criteria. Final scores of the product of weights and probability scores helped to reach the conclusion that American Systems Developers provides the best opportunity to Claire. Table of Contents S.No. Topic Page No. 1 Introduction 3 2 Problem statement 3 3 Goal statement 3 4 Background 3 5 Main criteria for job selection 4 6 Various alternatives available 5 7 Linking interests and alternatives 6 8 Methodology 7 9 Assumptions 8 10 Results 9 11 Conclusion 10 Introduction Claire Dale has job offers from five different companies. Each company has different salary structure to offer and a different job profile. Some are offering a job requiring a lot of travel while others are offering single location jobs. Claire also needs to look at the location where she will be working from so that she is able to balance her professional and personal life. She has a number of criteria which she has to look at before deciding which job has the potential to offer her maximum job satisfaction as well as compensate her financially and allow her to pursue her personal interests. Problem statement The candidate has been presented with five job offers. She is unable to decide which is best for her. Goal statement Selecting the right job offer which helps to satisfy the financial, professional and personal expectations of Claire. Background In real life situations, it sometimes become very difficult to decide which options to choose from when there are many criteria for deciding upon an option. MCDA techniques help us in deciding the best possible option in a scientific and methodological way. MCDA has a number of techniques to help in zeroing on an alternative and they all follow similar steps of organization and decision matrix construction (Linkov et al. 2006). However, each differs in the way it analyses the data. Some of the commonly used approaches are MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory, AHP (Analytical Hierarchy Process) and Outranking. The first three approaches are grouped under optimization approaches. For example, MAUT tries to provide a numerical criterion to evaluate the various alternatives. It gives scores to the various criteria to show the merits of each. The final scores can be seen by summing up the individual scores. Similarly, AHP tries to judge alternatives based on their scores and chooses the one with the highest score. It compares pairs. Outranking is a technique assumes that one alternative has more dominance over the other (Linkov et al. 2006). This technique does not assume that one alternative can be identified. It compares two or more alternatives at a time and tries to find out the extent to which one can be preferred over the other (Linkov et al. 2006). This method looks at favoring an alternative that performs best on maximum number of criteria. Thus, the weaknesses that one criterion has are compensated by the advantages that are there in the other criteria. Main criteria for selecting the job After a discussion with Claire and the project team, following criteria were identified which would impact her, the most (in order of importance, first one being the most important) once she decides on a particular offer: 1. Financial –

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