Integration Of Analytic Hierarchy Process Research Articles

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods

Along with the rise in population and the acceleration of economic expansion, there has been a concomitant rise in the urgent requirement for additional property that can serve as a venue for a wide variety of community activities. It is not uncommon for large cities that are the epicenter of urbanization, such as the city of Surabaya, to experience a sharp uptick in the demand for land. One of the regions that has excellent accessibility is the Sidoarjo Regency, which is comparable to the City of Surabaya in this regard. The goal of this research is to use Web-GIS to conduct an analysis of spatial data in order to identify the land functions that are most suitable for use in residential areas. The Analytic Hierarchy Process (AHP) and the Weighted Product Model (WPM) are two of the methodologies that are included in the spatial data modeling method that uses multi-criteria decision making (MCDM). The characteristics parameters that are employed are derived from data such as the distance to the city center, the distance to the market, the distance to the hospital, the distance to public transit, the slope, the kind of soil, and the amount of rainfall. The outcomes of spatial data modeling categorize the suitability of new residential land into categories of land that is not suitable for residential use and land that is acceptable for residential use. A K value of 0.27 is the outcome of the comparison test that was run between the two MCDM approaches using Cohen's Kappa coefficients.

Information Fusion for the Training of Public Administrators: Design of a Composite Indicator through the Integration of AHP and TOPSIS Methods

The article addresses the importance of capacity development among public administrators to ensure the efficient functioning of the state and the provision of quality services to the citizenry. This entails acquiring legal knowledge, ethical principles, and technical skills in areas such as strategic planning, financial management, and policy evaluation. In addressing the evaluation of public administrators' training programs, the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are employed. The methodology delineates the fundamental concepts and key equations to understand the application of these methods in evaluating public administrators' training programs. The findings underscore the significance of specific competencies in public administrators' training. These competencies, combined with comprehensive training encompassing cognitive, attitudinal, and instrumental dimensions, can equip professionals to confront current governmental challenges effectively. The article emphasizes the relevance of capacity development among public administrators and introduces methods such as AHP and TOPSIS to assess and enhance training programs in this field, aiming to bolster the state's capacity to respond efficiently to societal needs.

A Novel Fusion Method for Enhanced Multi-Criteria Decision-Making in Energy Management: Integrating Analytic Hierarchy Process

This paper suggests a novel fusion approach of combining Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance multi-criteria decision-making for energy management. In this way, by integrating the two powerful methodologies, the fusion approach is enabled that allows for dealing with the complex and highly dynamic character of energy management decisions for which there is required the careful consideration of many conflicting criteria. The method uses A to derive weightings for each decision criterion via AHP based on expert judgments, ensuring all relevant factors are systematically considered proportionately. Subsequently, TOPSIS is applied to evaluate and rank the alternatives such that the most effective energy solutions close to the ideal solution are identified. This integration of AHP with TOPSIS leads to comprehensive analysis that draws both the strengths of these techniques and provides a powerful tool to make informed and balanced decisions in the energy sector. The effectiveness of this fusion method, when applied, could then lead to the attainment of subtler findings and dependable suggestions, making it a beneficial contribution to this field of energy management.

Identifying flood vulnerable and risk areas using the integration of analytical hierarchy process (AHP), GIS, and remote sensing: A case study of southern Oromia region

Many people have died, and economies have been devastated by flooding, which is one of the world's most prevalent and catastrophic natural catastrophes. As a result of human activity on the land, the flooding patterns have changed. Inadequate drainage infrastructures, as well as various activities such as deforestation, are exacerbating the flood. In Ethiopia, southern Oromia is one of the regions affected by flooding, with the West Guji and Guji zones being the most frequently flooded places. People are exploiting natural vegetation for various purposes as a result of rapid population growth, resulting in deforestation and susceptibility to natural disasters such as floods. As a result, identifying flood-prone locations in great detail is essential for effective issue-solving. Thus, the study aims to locate and map flood-prone and flood-risk regions in southern Oromia utilizing Remote Sensing, analytical hierarchy Process (AHP), and Geographic Information system (GIS). Eight flood-causing factors, such as drainage density, rainfall, elevation, slope, Topographic wetness index (TWI), soil type, distance from the river, and LULC were developed in the GIS environment, and ERDAS Imagine software was employed to make land coverage and land usage data. The weighted overlay Multi-criteria evaluation (MCE) approach was then used to construct a flood-sensitive map based on the AHP pairwise comparison of eigenvector value. To determine flood risk, population density, a vulnerable flood map, and land usage/land coverage layer were overlaid, having equal weightage. The flooding vulnerability outcome in southern Oromia indicates that 0.3% (20.53km2), 31.4% (24,986.4km2), 62% (49,310.6km2), 6.39% (5081.98km2), and 0.19% (148.866) is, very high, high, moderate, low, and very low flooding vulnerable region correspondingly. From the flood risk map, it was estimated that 2% (1587.3km2), 19.3% (15,384.4km2), 75.5% (60,071.7km2), and 3.15% (2505.3km2) of the area were endangered to very high, high, moderate, and low, flood risk levels correspondingly. As a result, the study will assist the relevant authorities in formulating development policies based on the area's existing flood danger and risk.

Mapping of Mount Semeru Volcanic Mudflow Susceptibility Along the Rejali River using the GIS-based AHP-TOPSIS Ensemble Approach

Volcanic mudflow floods occur when rainfall runoff combines with volcanic material and flows downstream. These devastating events cause significant damage to infrastructure, disrupt economies, and result in injuries and casualties. One area where the flow of volcanic material greatly affects the situation is the Rejali River, which receives a substantial amount of volcanic debris from Mount Semeru. To address this issue and begin mitigating the associated risks, it is crucial to start by mapping the potential distribution of volcanic mudflow floods. Therefore, this study aimed to assess factors impacting volcanic mudflow flood susceptibility and to create a corresponding susceptibility map. The study employed the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the influence of various factors and classify the areas, respectively. These methods were integrated with the Geographic Information System (GIS) to enhance the analysis. The weighted analysis results showed that the most impactful factors conditioning volcanic mudflow floods, in descending order, were rainfall (42.40%), land cover (13.89%), elevation (13.39%), slope (12.51%), distance from the river (7.09%), soil type (6.58%), and rock distribution (4.13%). The TOPSIS calculation further highlighted that rainfall intensity between 104.03 and 109.65 mm day-1 had the greatest influence on susceptibility. The successful integration of AHP and TOPSIS methods with GIS helped develop a volcanic mudflow flood susceptibility model with an outstanding accuracy of 0.969. The model showed that approximately 46.40% of the areas along the Rejali River exhibited very high susceptibility to volcanic mudflow floods, while an additional 16.21% indicated high susceptibility and substantial risk in most regions. Therefore, the generated susceptibility map offered important insights for shaping future mitigation strategies and influencing policy decisions.

Integration of Analytic Hierarchy Process (AHP) and Remote Sensing to Assess Threats to Preservation of the Oases: Case of Al Ain, UAE

Identifying threats to historical sites is important for formulating preventive measures to reduce their impacts. The oases in Al Ain, United Arab Emirates (UAE), are one of the historical sites and were inscribed as cultural sites in 2011 by UNESCO World Heritage Committee. This study assessed the threats to oases based on the UNESCO-listed factors affecting the outstanding universal value of the World Heritage properties. An Analytic Hierarchy Process (AHP) coupled with remote sensing was used for data collection and analysis. Expert feedback showed that water, urban expansion, soil salinity, palm disease, and the legal framework were major threats. To determine whether urban expansion influences oases preservation, remote sensing images were used to investigate land use and land cover (LULC) around the oases. The LULC change between 1972 and 2022 showed that palm trees, grass, and built-up areas increased by 59%, 76%, and 91%, respectively. The normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to assess stress in the oases. The results indicated that oases have not been impacted by urban expansion since 1972, reflecting the UAE’s commitment to the preservation of oases. The availability of cloud-based and open-access satellite images coupled with AHP is an effective tool for understanding threats. This research aligns with UN SDG 15—“Life on Land”. The concept of this study could be used to assess threats to historical sites. It is recommended that policies for the oases’ preservation be maintained and updated to cater to issues related to population and climate change. Gray water and smart irrigation systems could be assessed as alternatives to minimize water use. Hyperspectral remote sensing is recommended for future studies related to soil salinity and palm diseases.

Flood vulnerability and Risk assessment using the Integration of Analytical Hierarchy Process (AHP), GIS, and Remote Sensing: A Case Study of southern Oromia Region.

Many people have died, and economies have been devastated by flooding, which is one of the world's most prevalent and catastrophic natural catastrophes. As a result of human activity on the land, the flooding patterns have changed. Imperfect drainage infrastructures, as well as various activities such as deforestation, are exacerbating the flood. In Ethiopia, southern Oromia is one of the regions affected by flooding, with the West Guji and Guji zones being the most frequently flooded places. People are exploiting natural vegetation for various purposes as a result of rapid population growth, resulting in deforestation and susceptibility to natural disasters such as floods. As a result, identifying flood-prone locations in great detail is essential for effective issue solving. Thus, this research was conducted to construct flood sensitive and flood risk mapping in southern Oromia utilizing the Remote Sensing, AHP (analytical hierarchy Process), and GIS. Eight flood-causing factors, such as drainage density, rainfall, elevation, slope, TWI, soil type, distance from the river, were developed in the GIS environment, and ERDAS Imagine software was employed to make land coverage and land usage data. The weighted overlay MCE approach was then used to construct a flood-sensitive map based on the AHP pairwise comparison of eigenvector value. To determine flood risk, population density, a vulnerable flood map, and land usage/land coverage layer were overlaid, having equal weightage. The flooding vulnerability outcome in the southern Oromia indicates that 0.3% (20.53km2), 31.4% (24986.4km2), 62 % (49310.6km2), 6.39% (5081.98km2), and 0.19% (148.866) is, very high, high, moderate, low, and very low flooding vulnerable region correspondingly. From the flood risk map, it was estimated that 2% (1587.3km2), 19.3% (15384.4km2), 75.5% (60071.7km2), and 3.15% (2505.3km2) of the area were endangered to very high, high, moderate, and low, flood risk levels correspondingly. As a result, the study will assist the relevant authorities in formulating development policies based on the existing flood danger and risk in the area.

GIS-based Flood Risk Mapping: The Case Study of Kosi River Basin, Bihar, India

Flood risk mapping aims to create an easily read and rapidly accessible map to prioritize the mitigation effects. This study presents an empirical approach to flood risk mapping through the integration of Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) techniques. SRTM 30m DEM is processed using ArcGIS 10.3 software. The study methodology includes the selection of the study area, the identification of the factors responsible for flood and collection of the required data, the generation of the desired thematic layers, and their integration to produce the flood risk map. Geomorphic, hydrologic, and socio-economic analyses are carried out to generate the thematic layers, namely slope, district`s distance to active stream, highest elevation, drainage density, rainfall, population density, and land use-land cover. AHP is used to determine the relative impact weight of the thematic layers. The influence of each thematic layer and the scale values provided based on the weights and score calculated by the AHP are used to integrate the layers in GIS environment to prepare the flood risk map. Consistency ratios are determined from the judgment process to validate the reliability of the proposed approach and results. The study classified the area falling in the basin under different risk zones with Purnia and Madhepura having large areas under high risk. This study may aid decision and policymakers in the evaluation and rapid assessment of flooding phenomena in the region.

Sand Supplier Selection Analysis Based on the Integration of Analytical Hierarchy Process (AHP) and Taguchi Quality Loss Function Methods

Supplier selection process is a step to evaluate and select the most suitable supplier to see the needs of the company or organization. This evaluation is important to ensure that the selected supplier is able to provide quality products or services, competitive prices, timely delivery, and meet ethical and sustainability standards. This research uses two methods, namely Analytical Hierarchy Process (AHP) and Taguchi Loss Function. AHP is used to calculate the weight of criteria, while the Taguchi Loss Function is used to estimate losses caused by suppliers. The criteria applied in supplier evaluation are quality criteria, product accuracy, delivery, price, warranty policy, and response to claims. The results obtained from this study PT. Bumi Makmur with a percentage loss of 21%, while the supplier that has the largest loss value is PT. Mergo Bopo with a percentage of 47%.
 Highlights :
 
 Integration of AHP and Taguchi Loss Function enhances supplier selection accuracy.
 Identified supplier exhibits substantial loss percentage, emphasizing the importance of thorough evaluation.
 Findings enable informed decision-making, facilitating risk mitigation and operational optimization
 
 Keywords :Supplier Evaluation; Sand; Analytical Hierarchy Process, Taguchi Loss Function

Integration Integration of AHP and TOPSIS Methods for Small and Medium Industries Development Decision Making

Financial problems are one of the reasons why small and medium-sized industries (SMIs) in West Kutai have not developed optimally. Government assistance programs are one of the solutions. This program must be appropriate, so a decision-making tool is needed to help choose the right SMIs to be assisted later. The weight of the criteria was determined using the Analytical Hierarchy Process (AHP) technique, and the priority of the SMIs as the preferred proposal for the recipients of development assistance was determined using the Technique for Other Reference by Similarly to Ideal Solution (TOPSIS) approach. Labor, investment, production capacity, production value, and raw materials were used to determine the priorities of SMIs beneficiaries. Furthermore, TOPSIS prioritizes the development of alternative small and medium-sized industries with types of handicraft commodities. Integration of AHP and TOPSIS methods has been successfully used in the IKM Development Priority Determination Application, with 83.3% precision and 96.4% accuracy achieved by using a confusion matrix so that the IKM ranking can be known. The results of the study found that integration of the two methods was successfully used for Small and Medium Industries Development Decision Making.

Modeling wildfire risk in western Iran based on the integration of AHP and GIS.

This study aimed at delineating the wildfire risk zones in a fire-prone region located in a rarely addressed area of western Iran (Paveh city)by assessing the potential of factors such as NDVI, topographic factors (elevation, slope, and aspect), land cover, and evaporation in explaining the fire occurrence probability. Analytic hierarchy process (AHP) and geographical information system (GIS) methods were used synergistically to integrate the mentioned factors into analysis, following an informed categorization of each factor based on the information on previous fire occurrence. In the AHP process, elevation and evaporation data were considered to be the most critical factors. It was found that the predicted wildfire risk areas were in agreement with past fire events by the use of the methodology proposed by this study. Accordingly, the study's final wildfire risk map indicated that approximately 64.7% of the study area is located in the high- and very high-risk zones. Land-use planners and decision-makers may use the developed map to setup and implement fire prevention strategies and enhance or develop the fire-surveillance logistics and infrastructure, including but not limited to the positions of fire watchtowers, fire lines, and fire sensors, with the aim to minimize potential fire impacts.

The application of factor analysis (FA) in evaluating supplier selection criteria in PT. Wijaya Karya Beton Tbk and ranking suppliers using integration of analytical hierarchy process (AHP) and adaptive ratio assessment (ARAS)

The selection of a concrete iron supplier is critical for a long-term supply chain in the infrastructure industry. Due to the participation of various qualitative and quantitative elements, the evaluation process of concrete iron supplier selection is a difficult work for decision specialists. Because uncertainty is widespread in concrete iron supplier selection challenges, improving integrated criteria selection and supplier selection procedures has proven to be one of the most efficient and superior ways to represent practical difficulties. The current study presents a novel framework for evaluating and selecting a preferred concrete iron supplier based on Factor Analysis and the ARAS (Adaptive Ratio Assessment) method, as well as the AHP (Analytical Hierarchy Process) methodology. An enhanced technique is used in the proposed method to determine the criteria weights based on expert preferences. Next, an actual case study of the concrete iron supplier selection problem is conducted in a comprehensive setting to demonstrate the effectiveness and practicability of the suggested methodology. A sensitivity analysis is also undertaken to ensure that the stated methodology is stable. Finally, the strength of the resulting result is tested by comparing it to current methodologies. The final results show that the established framework is more consistent and powerful than other approaches already in use.

GIS and AHP Based Techniques for Agricultural Land Suitability Assessment in Erbil Province, Kurdistan region, Iraq

The study area comprises Erbil province, Kurdistan region, Iraq. Thirty-five soil samples have been taken from different districts. Several soil analyses have been performed in order to find soil loss as a criterion for land suitability assessment. The other criteria were elevation, slope, aspect ratio, and land use and land cover (LULC). All used criteria have been weighted using Analytic Hierarchy Process (AHP) methodology to find their priorities in order to use them on weighted overlay methodology (WOM) based on the Geographical Information Systems (GIS) technique. Integration of AHP and GIS have been utilized in purpose to find the land suitability based on five classes; high suitable (S1), moderately suitable (S2), marginally suitable (S3), not suitable (N1), and not suitable permanently (N2). The result of land suitability shows that the S1 class is generally located at the northwest of the middle part in the study area extended to the southwest, and it occupies an area of 1243.94 km2 (8.61%). The S2 class occupies a minimum area of 85.52 km2 (0.59%), while the S3 class occupies a massive area relatively about 4886.75 km2 (33.82%). The N1 class occupies the highest area, around 6538.32 km2 (45.26%). At the same time, N2 class takes 1693.16 km2 (11.72%). Both N1 and N2 have an area of 8,231 km2 (56.98%) of the total area while S1, S2, and S3, which takes only 6,216 km2 (43.02%).In this study we found the possibility of using GIS and AHP in order to find the land suitability assessment.

GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya

ABSTRACT The main sought of this study is to assess the landslide susceptibility map (LSM) of the East Sikkim Himalaya based on the comparative model analysis using frequency ratio (FR), logistic regression (LR), random forest (RF) and integration of analytical hierarchy process (AHP) with FR (AHP-FR). The models were trained by 166 landslides (70% training) and 12 landslide causative factors whilst tested with the help of 71 landslides (30% testing). Their spatial correlation between the landslides and the causative factors was analysed by using a multicollinearity test. The generated LSM was classified into five classes, i.e. very low, low, moderate, high and very high. In East Sikkim, very high classes of the AHP-FR, LR and FR models cover the area of 11.97%, 11.99% and 7.13%, respectively. The accuracy of prepared LSM was evaluated by using the success rate curve (SRC), prediction rate curve (PRC) and seed calculation area index (SCAI). The area under the curve (AUC) of the success rate curve is 0.88 for the RF model, 0.85 for AHP-FR, 0.78 for LR and 0.79 for FR, whilst the prediction rate curve is 0.86% for the RF model, 0.81 for AHP-FR, 0.79 for LR and 0.78 for FR. The SCAI values of very high susceptibility classes are 0.14, 0.17, 0.18 and 0.19 for the RF, AHP-FR, LR and FR models, respectively. The RF and integrated AHP-FR methods indicate better results as compared to other statistical models.

Integration of shannon entropy (SE), frequency ratio (FR) and analytical hierarchy process (AHP) in GIS for suitable groundwater potential zones targeting in the Yoyo river basin, Méiganga area, Adamawa Cameroon

Study regionYoyo river basin, Méiganga, Adamawa region, Cameroon. Study focusThe study aimed firstly to delineate and classify groundwater prospective zones in granito-gneissic terrain using advanced applications of remote sensing and geographical information systems through the integration of analytical hierarchy process (AHP), frequency ratio (FR) and Shannon entropy (SE). Secondly to compare the performance of these models to ensure effectiveness in computation. Thirteen groundwater influencing factors were adopted and weights were assigned to each thematic layer based on their relationship and characteristic with groundwater occurrence. Overlay weighted linear combination analysis tool in Arcgis environment was applied for groundwater prospect maps constructed. New hydrological insights for the regionGroundwater maps was classified into four categories:I poor, moderate, good and excellent. These maps analysis revealed that AHP have performed well for good and excellent classes with percentage of coved area of 23.73% and 23.75% respectively. These areas are localised in central, south-eastern, north-western and southern parts of the basin. Validation of outputs maps using wells location showed that AHP have higher accuracy (Kappa= 79.53%, AUC (predicted) = 82.40%, AUC (success) = 87.09%), compared to FR (Kappa= 74.62%, AUC (predicted) = 76.68%, AUC (success) = 77.21%) and to SE (Kappa= 77.75%, AUC (predicted) = 70.61.40%, AUC (success) = 75.78%). These results are reliable and useful for various decision-making processes and sustainable management of groundwater resources in the Yoyo river basin.

Integrated Multicriteria Decision Model for Software Project Selection at PT Hexavara Tech

As a relatively young software company, PT. Hexavara Tech needs to select incoming projects since the number of requests exceeds the amount that the company can manage. Currently, there are no guidelines for selecting projects, so the project selection process is less structured and subjective. In fact, project selection is faced with several qualitative and quantitative criteria that tend to be conflictual in achieving objectives. So in this study, the integration of Analytical Hierarchy Process (AHP) and Goal Programming (GP) methods is used to select software projects that consider limitations and multi-objectivity. Based on the calculation of the level of importance of the criteria using AHP, the sequence of project selection criteria is obtained, namely profit for the company (0.287), potential development of the project scope (0.155), initial development capital (0.1), the opportunity to get relations from clients (0.097), conformity to team capabilities (0.093), risks contained (0.078), increasing the value of the company's portfolio (0.073), the opportunity to create a template for the next project (0.07), and the opportunity to learn new technology (0.047). After the optimization process using GP, the best final results are obtained, namely the e-wallet development project of savings and loan cooperatives, development of an android-based online motorcycle taxi application, development of e-commerce design works, development of overseas money transfer applications, and development of a website framework for cross border e-commerce

Application of AHP to Multi-Item Inventory Management Using Part Period Algorithm

This empirical study deals with integration of Analytical Hierarchy Process (AHP) with multi-item inventory management using Part-Period Algorithm. Among many variables which affects inventory systems, this study considers the variables of lot-sizing rules, sequencing, demand pattern, coefficient of variations, and change over cost. AHP is used to pick a sequencing rule and performance criteria. This study describes the application of AHP methodology in the form of multiple tables that will assist production managers in a Group Technology environment, to minimize employee and machine idleness. It will help managers make decisions on production order quantity, the sequence in which jobs should enter work centers, and in the determination of uniform production cycle times.

The application of AHP-PROMETHEE II for supplier selection

The evaluation and selection of the best supplier is a crucial decision-making process as it can reduce the supply chain cost and increase the quality of items supplied to a company. Choosing suppliers solely based on their previous experiences may contribute to higher purchase risk. The decision-making process is usually influenced by a variety of criteria that are often in conflict. Thus, this study employs the integration of Analytic Hierarchy Process (AHP) method and Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE) II to handle supplier selection issues. AHP method was adopted to evaluate the weights of criterion. PROMETHEE II is a Multi-Attribute Decision Making method that is based on a mutual comparison of each alternative pair with respect to each of the selected criterion allowing the rankings of the alternatives from the best to the worst. A real-life data about supplier selection for railways project was used to demonstrate the operation of AHP-PROMETHEE II method. The data involved four decision makers, five alternatives (SI, S2, S3, S4, S5) and eight criteria specifically; aspect of the items (C1), arrangement of time (C2), shipment accomplishment (C3), total price of the item (C4), way of payment (C5), relation with supplier (C6), scope of product (C7), and supplier’s professional ability (C8). The findings showed that C4 was the most preferred criterion among the chosen criteria. Thus, the ranking order for criteria was C4 > C8 > C1 > C2 > C5 > C7 > C6 > C3 while the ranking order for the alternatives was S2 > S1 > S4 > S3 > S5, respectively. The findings were then compared with the result from the previous research. The sensitivity analysis was conducted by reducing criteria weights to zero. In conclusion, AHP-PROMETHEE II can be used to analyse the perfect order from best to worst.

Integration of AHP-MOORA Algorithm in Green Supplier Selection in the Indonesian Textile Industry

In selecting suppliers, one approach that can be used to solve this problem is Multi-Criteria Decision Making (MCDM). MCDM involves several criteria with both quantitative and qualitative assessments. This research attempts to solve the Green Supplier Selection (GSS) problem by using the integration of Analytical Hierarchy Process (AHP) and Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA). AHP is used as the weighting of the GSS criteria and sub-criteria. Furthermore, MOORA is used to rank the best suppliers. This research was conducted in the textile industry in Indonesia. Eight criteria and 15 sub-criteria GSS are used to select eight suppliers. The results show that the product price has the most excellent weight in supplier selection. In addition, the AHP-MOORA method can be used in solving GSS problems.