Analytic Hierarchy Process (AHP)

What is the AHP and why use it? The AHP is a multi-criteria decision making method that finds its origins in the early 1970s as its creator was working on contingency planning for the Department of Defense (Saaty 1980). Its development stemmed from the need to organize and make decisions dealing with unstructured problems. Not only were the problems unstructured, but the components within these problems had no unit or measure or several different units of measure. The creator of the method sought to overcome this issue through the creation of hierarchy that could be broken down into levels. The elements on these levels could be placed a pairwise matrix where each element could be compared against the other element. The goal and stated achievement is that the method mimics how people actually think and decide (Saaty 1990b).

Comparing components to one another creates a matrix based on a ratio scale that must utilize matrix calculations to arrive at weights for the various competing decision criteria. While the AHP has been modified in other work and expanded since its inception, this research uses the eigenvector approach just as the creator of the method

originally suggested. Weights are created from the method by calculating the maximum eigenvector for pairwise matrix and then normalizing that vector so that the weights will sum to one. The maximum eigenvalue is used to test for consistency of judgments, an important point because the AHP relies on consistent judgments by it participants (Saaty 1990a).

This research does not seek to validate the AHP, rather it assumes the AHP is a credible decision making method and seeks to apply it to the project selection decision making problem in pavement management. The assumption of credibility is based on the evolution of the AHP over 40 years and its expansion into multiple sectors of decision making. In fact it has become one of the most widely used multi-criteria decision making tools finding it ways into the manufacturing, engineering, industrial, governmental, and political sectors, just to name a few. The method has evolved to the point that in 2006 an article was published devoted to a review of the applications of the AHP (Vaidya and Kumar 2006). Literature also states that confidence has grown in the method as decisions have been made across a diverse group of situations that are currently accepted and used in industry (Forman and Gass 2001).

Sinha et al. (2009) applied the AHP to establish weights of performance criteria for transportation systems. Sinha et al. (2009) tries to overcome the fact that single-criteria decision making assumes one parameter is so much more important than all other parameters that the decision can be based on that one thing and this type of mentality cannot be used in transportation decision making. The goal is to make a decision that

gives consideration to all inputs necessary to achieve the highest level of desirability for the managing agency and its customers.

Al-Barqawi and Zayed (2008) use the AHP to create a decision making tool for water mains. This method was chosen because the authors believe it simulates the human decision process. Interestingly, water main decisions include many variables, some of which are considered in pavement projects. Examples include material type, location, and operation.

Farhan and Fwa (2009) used the AHP to prioritize pavement maintenance needs.

The authors choose the AHP because of its ability to measure quantitative and qualitative variables. This is better than using a single index or composite number because it will not mask the contributions of the various input parameters. Farhan and Fwa (2009) stated that the AHP method has less variation than the direct weighting method to which it was compared, an important point when legislators and the public are demanding consistency and accountability from highway agencies such as TxDOT.

Šelih et al. (2008) and Khademi and Sheikholeslami (2010) each use the AHP to develop weights for the different criteria used in highway decision making. Each uses the AHP to establish priorities and do so because of the method’s ability to capture the relative importance of each variable. Al-Harbi (2001) used the AHP to help solve the problem of contractor prequalification. This problem presents a quandary to project owners as effort is made to eliminate unqualified contractors from the bidding process.

It is difficult to determine how the relationships associated with the various criteria relate and many have struggled with not only the relation of components, but how should the

components be weighted. All of these issues were addressed using the AHP (Al-Harbi 2001).

Korpela and Tuominen (1996) used AHP for selecting the optimum site for warehouse placement. The authors mentioned the need to choose wisely based on economic and competitive advantages that can be achieved by having a warehouse in the proper location. This is an interesting parallel to pavement preservation project selection because this article is published in an economics journal, but it illustrates the need for structured decision making.

Zhang et al. (2001) have used AHP to help prioritize pavement data collection efforts for TxDOT. This study was done to isolate and weight data that is needed in PMIS. This is performed from a collection and policy standpoint, not a project decision support perspective (Zhang et al. 2004).

A recent study, Sun and Gu (2010), has been performed that focuses on pavement condition and the prioritization of pavement projects. Similar to the parameters captured by Sun and Gu, their study speaks of the importance of the road when determining where funds should be spent. The definition of this importance is made up of several parameters that the author uses the AHP to comparatively weight.

This author focuses on different parameters included in pavement condition and seeks to make the optimum decision by appropriately aggregating this parameter, while at the same time mentioning that in practice other components are also included and must be accounted for in project prioritization. This study also only focuses on the prioritization of eight roadway sections while mentioning that an actual pavement network will

contain tens of hundreds of segments (Sun and Gu 2010). That article is most similar in application of the AHP to the current research; however this research makes efforts to capture more than pavement condition and seeks to apply the AHP to a real network consisting of thousands of sections.

3. AGREEMENT BETWEEN PMIS OUTPUT AND ACTUAL