Multi-Criteria Decision Making - Case Study Example

Multiple-criteria decision-making (MCDM) is a tool used to solve problems with numerous sets of criteria. It helps decision-makers tackle such problems. There are typically no particular solutions and it is essential to use the preferences of makers to separate the different solutions (Aghaarabi E et al, 2014). A decision-making process begins with the definition of the aim of the MCDM process, it then proceeds to the selecting parameters, alternatives, weighing options and aggregation systems in that order before making the final results based on the results from aggregation.

The MCDM process operates through a standard working principle as outlined below: Selection of Criteria: The selected criteria have to be; coherent with the decision, independent of other decisions, measurable, represented on a similar scale, unrelated to the alternatives (Christos, 2014). Selection of Alternatives: The selected alternatives have to be; available, real rather than ideal, comparable, feasible, and practical. Selection of the Weighing Methods to Represent Significance: The selected weight determination methods can either be out-rankable or compensatory.

Examples of the compensatory method include the Fuzzy Multi-Criteria Decision Making Process (FDM) and the Analytical Hierarchy Process (AHP) among others. Outranking methods includethe  Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEUS) and Elimination and Choice Expressing Reality (ELECTRE) (Calvin, 2011). Method of Aggregation: Methods of aggregation can either be an average, a product, or a function. The outcomes of aggregation distinguish the best alternative from those present (Christos, 2014).

There are two different types of MCDM, are the compensatory and the out-ranking methods. The compensatory method relies on a systematic analysis of presenthe t criteria. The desirable characteristicsf a particular attributes are compensated for the less favorable ones. This method follows a systematic decision making process. This method is referred to as a compensatory model because the positive accumulation of scores from favorable attributes outweigh the negative scores from the less favorable attributes (Aghaarabi et al., 2014).

A compensatory decision encompasses the distinction between the negative and positive aspects of a certain product. A good example of this is when a consumer wants to buy a car and has to choose between one with a commendable gas mileage, slow acceleration but with a lower price and another with better acceleration, but uses more gas, and costs more, the consumer will choose the former as it is sufficiently gas efficient and inexpensive (Calvin, 2011). There are occasions when a decision can be made devoid of a compensatory strategy, for example when a parent rejects to purchase soft drinks with artificial sugar regardless of favorable features like good taste (Christos, 2014).

The amount of effort in a decision process depends on a set of factors that range from factors like market conditions (the number of competitors and the differences between brands), the characteristics of a product (product significance, product complexity, product quality), to consumer characteristics (the interest of the consumer in performing product analysis to make the best possible deal) as well as the situational characteristics as discussed above (Christos, 2014). 1.3.2 Outranking Methods The outranking method is different from the compensatory method, it was coined France and has a considerable applications in numerous European countries (Aghaarabi et al., 2014). This method depends on the concept of outranking and has evolved from similar alternatives that rely on outranking and domination.

The only difference between outranking methods and former alternatives is that unlike the standard dominance method, the outranking frame uses weights to emphasize the significance of some criteria over others (Calvin, 2011). One option is said to have out-ranked the other if it outperforms another on a sustainable set of criteria with sufficient significance depending on the total sum of attribute weights. This ‘superior’ option should not be out-performed by recording an inferior performance in any other criterion.

All the alternatives are eventually evaluated based on their level of proliferation in exhibiting sufficient out-ranking on a complete set of options against set threshold parameters (Christos, 2014). One interesting characteristic of the outranking model is that under certain conditions, it is possible for two separate conditions to be regarded as ‘incomparable’ (hard to compare). It is important to note that incomparability between two options is different from indifference between them and can, for instance, be the result of missing information during data analysis.

This is a common occurrence in most decision-making processes (Christos, 2014; Aghaarabi et al., 2014). Developing this possibility in the outranking mathematical structure allows for a standard problem analysis while neither dropping one option nor imposing judgment on indifference as a measure of lacking information. The major concern of the outranking method is that it depends on unclear definitions of the main constituents of outranking, the specific process of setting parameters and the manipulation by the decision maker (Calvin, 2011).

The outranking method specifically eliminates alternatives with poor performance in any set of criterion and may consequently promote dedicated advocacy from respective decision makers. The outranking method is also an instrumental tools for assessing the development of preferences in between options. Examples of the outranking method include the FLDM and the AHP which are the most common compensatory MCDM significantly used to solve various global decision support systems and solving decision-making problems (Aghaarabi et al., 2014). 1.

4 Analytical Hierarchal Process (AHP) The AHP is a specialized application with a broad range of uses in decision making situations. It can be used in businesses, healthcare, industry as well as education. As opposed to selecting a ‘right’ decision, the AHP assists decision makers to select alternatives with favorable characteristics to their problem goals.