Performance indicators for municipal solid waste management systems in Saudi Arabia: selection and ranking using fuzzy AHP and PROMETHEE II

Abstract

Municipalities in the Kingdom of Saudi Arabia (KSA) are managing their municipal solid waste management (MSWM) systems without a structured performance assessment mechanism. For long-term sustainability, all the key components of a MSWM system need to perform efficiently. Identifying suitable performance indicators (PIs) for the regions without having an established performance benchmarking process is a daunting task. A framework is developed to select the PIs for seven key components of MSWM systems, including public service and participation, personnel, physical assets, operational, environmental, sustainability, and financial. Initially, 87 potential PIs were identified under these components through an exhaustive review of literature and expert knowledge. Interview surveys were conducted with decision-makers from two municipalities and academia in the Qassim Region of KSA to evaluate the PIs against three decision criteria, i.e., “relevance,” “measurability,” and “understandability.” For addressing the uncertainties due to vagueness in group decision-making, criteria weights were established through fuzzy analytic hierarchy process (FAHP) while the linguistic scores (defined as fuzzy numbers) were aggregated using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II). Finally, 61 PIs were selected and ranked, for the seven key components, by the decision-makers based on the outranking relationships. Network maps encompass the selected PIs using the decision-makers’ boundary and leave a choice for including additional PIs in future for continuous performance improvement. A conceptual performance assessment framework has also been proposed for practical implementation of the selected PIs for MSWM systems in KSA and other parts of Gulf region with similar conditions.

Appendix

Application of multicriteria decision-making methods for selecting the PIs for MSWM systems in Saudi Arabia

Application of fuzzy analytical hierarchy process (FAHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) multicriteria decision-making methods to select of performance indicator for ‘public service and participation’ (PU) category for the municipal solid waste systems in Saudi Arabia.

Fuzzy analytical hierarchy process for estimation of criteria weights

AHP was employed for estimating the weights of the selection criteria. The geometric means of the fuzzy scores (see Table 3 of main text) allocated by four experts from academics and two municipalities’ managers are presented in Appendix Table 8.

Table 8 Pairwise comparison matrix for weight estimation using FAHP

The defuzzified weights of “relevance,” “measurability,” and “understandability” obtained from the normalized matrix shown in Appendix Table 9, using Eq. (3), are 0.56, 0.30, and 0.14, respectively.

Table 9 The normalized fuzzy weighted matrix

The value of consistency index (CI) is formed to be 0.035. Subsequently, consistency ratio (CR) using Eq. (4) in the main text comes out to be 0.061 which is less than the threshold value of 0.1.

PROMETHEE II for selection and ranking of PIs

Eight PIs were identified through the screening process under the category of “public service and participation” (PU). The matrix listing geometric mean of linguistic scores, using TFN in Table 5 of the main text, allocated by four experts from academics and two municipalities’ mangers is presented in Appendix Table 10. All the scores are higher the better.

Table 10 Fuzzy scoring matrix showing average scores allocated by 6 decision-makers from academics and participating municipalities

After defuzzification using Eq. (3), the preference functions using Eq. (8) are presented in Appendix Table 11. Aggregated preference index matrix using Eq. (9) is given in Appendix Table 12. The outgoing flow, the incoming flow, and the net flow using Eqs. (10)–(Capelli et al., 2011) with the final ranking of PIs in “public service and participation” component are presented in Appendix Table 13.

Table 11 Preference functions for all the pairs of alternatives (i.e., PIs from PU1 to PU8)

Table 12 Aggregated preference functions for all the PIs

Table 13 Outgoing flows, incoming flows, net flows, and final ranking of all the PIs

Based on the expert judgment of all the decision-makers, it was unanimously decided to select top 70% (i.e., top 6) of the PIs under the key component of “PU.” Visual presentation of outranking relationship amongst the PIs in this category is shown in Fig. 4. Similarly, final ranking for the PIs evaluated under all the seven key components is presented in Table 7 of the main text. Detailed discussion on all the indicators along with the visual demonstration of outranking relationships can be seen in the “Development of PIs for MSWM systems in Qassim Region” section of the main text.