Ordered Weighted Averaging Research Articles

Volatility GARCH models with the ordered weighted average (OWA) operators

Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS) to create an estimator that can treat high degrees of uncertainty. In the application of the ARCH-GARCH models, we develop approaches with the OWA and the induced OWA operator. Some further generalizations are also developed by using generalized means. The main advantage of this new methodology is to add additional information to the process of estimating the models according to the attitudinal character of the decision-maker. Finally, the work presents an application in the volatility of the MX/US exchange rate, where the efficiency of the OWA operators in forecasting is proved.

GIS-Based Multi-Criteria and Multi-Objective Evaluation for Sustainable Land-Use Planning (Case Study: Qaleh Ganj County, Iran) \u201cLanduse Planning Using MCE and Mola\u201d

Land evaluation is a key factor in land-use spatial planning, affecting both success and sustainability. This study showcases the value of using the multi-criteria evaluation (MCE) and multi-objective land allocation (MOLA) GIS decision-making tools determine the most favorable spatial development of various land-use types, for Qaleh Ganj County in Iran. Weighted linear combination (WLC) and ordered weighted averaging (OWA) were used to assess the potential of seven land uses based on predefined criteria. MOLA was also used for land-use zoning based on suitability. The results derived from these techniques indicate that the rangeland zone with 30.80% and the ecotourism zone with 22.9% have the highest suitability potential, and aquaculture with 0.26% and tourism with 0.24% have the lowest potential in Qaleh Ganj. Considering the 7 land uses and a lot of defined criteria, MCE and MOLA provided an automatic and flexible way of dealing with qualitative multi-dimensional environmental effects, factors, constraints and objectives. The combination of WLC and OWA helped to manage selection factors differently, as their level of risk and trade-off is different. The result can be considered as optimal suitability maps with an environmental preservation goal which can help to protect the natural environment of this area, and will also allow for continued economic development. The approach described in this study can help developing countries and the sensitive area facing environmental challenges due to rapid development. This approach and its application procedures can be applied to similar territorial contexts, where several territorial factors should be considered and taken into account.

Developing a GIS-Based Decision Rule for Sustainable Marine Aquaculture Site Selection: An Application of the Ordered Weighted Average Procedure

Fish consumption is on the increase due to the increase in growth of the global population. Therefore, taking advantage of new methods such as marine aquaculture can be a reliable source for the production of fish in the world. It is necessary to allocate suitable sites from environmental, economic, and social points of view in the decision-making process. In this study, in order to specify suitable areas for marine aquaculture by the Ordered Weighted Averaging (OWA) methodology in the Caspian Sea (Iran), efforts were made to incorporate the concept of risk into the GIS-based analysis. By using the OWA-based method, a model was provided which can generate marine aquaculture maps with various pessimistic or optimistic strategies. Eighteen modeling criteria (14 factors and 4 constraints) were considered to determine the appropriate areas for marine aquaculture. This was done in 6 scenarios using multi-criteria evaluation (MCE) and ordered weighted average (OWA) methodologies. The results of the sensitivity analysis showed that most of the parameters affecting the marine aquaculture location in the region were as follows: Social-Economic, Water Quality, and Physical–Environmental parameters. In addition, based on Cramer’s V coefficient values for each parameter, bathymetry and distance from the coastline with the most effective and maximum temperature had the least impact on site selection of marine aquaculture. Finally, the final aggregated suitability image (FASI) of weighted linear combination (WLC) scenario was compared with existing sites for cage culture on the southern part of the Caspian Sea and the ROC (Relative Operating Characteristics) value turned out to be equal to 0.69. Although the existing sites (9 farms) were almost compatible with the results of the study, their locations can be transferred to more favorable areas with less risk and the mapping risk level can be controlled and low- or high-risk sites for marine aquaculture could be determined by using the OWA method.

Proposal for a Fuzzy Model to Assess Cost Overrun in Healthcare Due to Delays in Treatment

Apart from the effects of treating those infected with COVID-19, the pandemic has also affected treatment for other diseases, which has been either interrupted or canceled. The aim of this paper is to provide a financial model for obtaining the cost overrun resulting from the worsening of illnesses and deaths for each of the causes considered. To achieve this, first deaths have been classified into causes of death and for each of these causes, an estimation has been made of the worsening condition of patients due to delay in treatment. Through these data, a fuzzy relation between deaths and the worsening condition of patients can be obtained. Next, the expertise process has been used to estimate cost overrun in relation to patients’ pathologies. The experts’ opinions have been aggregated using ordered weighted average (OWA). Lastly, using fuzzy logic again, a correction coefficient has been determined, which optimizes the future implementation of the proposed model without the need for a new estimation of inputs. The paper concludes with a numerical example for a better comprehension of the proposed theoretical model. Ultimately, it provides the scientific community in general and in particular managers of public administration entities with a novel tool for improving the efficiency of the healthcare system.

Ranking range model in multiple attribute decision making: A comparison of selected methods

Multiple attribute decision making (MADM) approaches have been investigated extensively for ranking the alternatives related to multiple attributes. In most existing MADM approaches, attribute weights play a key role due to the fact that the ranking of alternatives may changes with attribute weights vector. Ranking range can be used to measure the lower and upper bounds of all possible rankings of alternatives when attribute weights are varying. In the paper, we investigate the ranking range for the selected seven popular MADM approaches: Weighted averaging (WA), weighted geometric averaging (WGA), ordered weighted averaging (OWA), ordered weighted geometric averaging (OWGA), TOPSIS, PROMETHEE and ELECTRE. Through determining the ranking ranges of alternatives, associated with attribute weights, in the selected seven approaches, we present several desirable properties of the ranking range. Then, we design the simulation experiments with either random or real data to compare the ranking range for selected seven MADM approaches. Interestingly, the experiment results clearly show that TOPSIS≻ELECTRE≻PROMETHEE~WA≻WGA≻OWA≻OWGA in the average sense, where '≻' denotes the larger ranking range, and '~' denotes no difference in the ranking range. A larger ranking range means an easier to manipulate the ranking of alternatives, and also means a worse robustness of a MADM approach.

Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation

Forest fires are a major environmental issue because they are increasing as a consequence of climate change and global warming. The present study was aimed to model forest fire hazard using the ordered weighted averaging (OWA) multi-criteria evaluation algorithm and to determine the role of human, climatic, and environmental factors in forest fire occurrence within the Golestan National Park (GNP), Iran. The database used for the present study was created according to daily classification of climate changes, environmental basic maps, and human-made influential forest fire factors. In the study area, the forest fires were registered using GPS. Expert opinions were applied through the analytic hierarchy process (AHP) to determine the importance of effective factors. Fuzzy membership functions were used to standardize the thematic layers. The fire risk maps were prepared using different OWA scenarios for man-made, climatic, and environment factors. The findings revealed that roads (weight = 0.288), rainfalls (weight = 0.288), and aspects (weight = 0.255) are the major factors that contribute to the occurrence of forest fire in the study area. The forest fire maps prepared from different scenarios were validated using the relative operating characteristic (ROC) curve. Values of forest fire maps acquired from scenarios of human, environment, climate factors and their combination were 0.87, 0.731, 0.773 and 0.819, respectively.

Axiomatization and construction of orness measures for aggregation functions

The notion of an orness measure for aggregation functions has been a relevant study subject whose history can be traced back to the early works of Dujmović in 1973. Intuitively, an orness measure quantifies the similarity of an aggregation function to the “or” function and results in an essential tool for decision engineering, field in which the choice of aggregation function is sometimes restricted to a desired value of orness (orness-directed aggregation). In 1988, Yager presented a particular example of orness measure for ordered weighted averaging (OWA) functions and initiated a series of contributions aiming at proposing an axiomatic definition of orness measure for OWA functions. In this paper, we go much further and present an axiomatic definition of orness measure for the whole family of aggregation functions. We end by proposing two natural construction methods for an orness measure for aggregation functions. The particular examples of the (discrete) Choquet integral and uninorms are studied in detail.

Land use suitability analysis for town development planning in Nanjing hilly areas: A case study of Tangshan new town, China

Land use suitability analysis plays an important role in sustainable land use and solving environmental problems caused by rapid urban development. A land use suitability mapping approach for town development planning in hilly areas was constructed based on two multi-criteria evaluation methods: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), to comparatively evaluate and map land use suitability of Tangshan new town in Nanjing, China. Fourteen evaluation factors related to topographic, environmental, socio-economics and historical sites data were used as suitability criteria. The analytic hierarchy process (AHP) method and GIS techniques were integrated into the evaluation models to create the land use suitability map for town development planning. The results of WLC approach showed that 11.4% of the total area is highly suitable while the 48.6% is unsuitable. The results of WLC and OWA approach showed the distribution of degree of land use suitability is almost the same. The areas located at the southern and eastern flat regions are highly suitable for land use, whereas the areas close to the mountain forests, steep slopes, waters, and hot springs, have lower suitability for land use. Sensitivity analysis indicated that the suitability results of the two proposed methods are robust. Indirect validation was achieved by mutual comparison of suitability maps derived from the WLC and OWA methods. It demonstrated that the overall agreement is 90.81% and kappa coefficient is 0.81, indicating that both methods provide very similar spatial suitability distributions. By overlaying the resultant map with the previous master plan map of Tangshan new town, the overlay map once again indicated a satisfactory ecological fit between the two maps. At last, several recommendations are proposed aiming at improving the long-term town development plan for Tangshan new town.

Land-use suitability assessment for urban development using a GIS-based soft computing approach: A case study of Ili Valley, China

Land-use suitability assessment is an important step in land use planning for urban development. We propose a GIS-based soft computing approach (GSC), which is a combination of two multi-criteria analysis methods, i.e., the ordered weighted averaging (OWA) method and the logic scoring of preference (LSP) method, to evaluate and map land-use suitability for urban development in Ili Valley, China. The evaluation uses 13 factors as suitability criteria for urban development. These factors are related to the topography and geology, socio-economic feasibility, ecological restrictions, and prohibitive constraints. Based on the final suitability results, Ili Valley was classified using five suitability levels: highly suitable, suitable, moderately suitable, marginally suitable, and not suitable. By comparing seven preference decision coefficient (α) scenarios, we determined that the area of the land that is highly suitable for urban development decreases, and the area of the marginally suitable land increases with increasing α. All of the scenarios show that approximately 32.6% of the land area is not suitable. Among the seven scenarios, the policy orientation of three of the scenarios was an urban expansion policy orientation (α = 0.5), a balanced policy orientation (α = 1), and an ecological protection policy orientation (α = 2). The result of the policy orientation analysis of urban development can be used as an urban development boundary for different development preferences and stages. The local land-use planning was evaluated by overlaying its planned urban development zones with the areas of the resultant suitability map. Specific recommendations are presented for the scale and timing of urban development in Ili Valley.

Prioritization in wildfire restoration using GIS-based ordered weighted averaging (OWA): A case study in southern California

<abstract> <p>Wildfires are a prevalent natural disaster that can significantly impact human populations and result in considerable losses. With a changing climate, wildfires in many countries have increased in intensity and frequency, making effective restoration efforts in affected areas crucial. This paper aims to evaluate the efficacy of ordered weighted averaging (OWA), a GIS-based multi-criteria decision analysis technique, in identifying priority areas for wildfire restoration. A case study using the 2009 Station Fire in California is presented, using the restoration criteria of slope, erodibility, proximity to forest cover, and proximity to surface water. By applying both importance and order weights, multiple OWA decision strategies with varying risk levels were examined. Different strategies greatly influence the spatial distribution of land considered high and low priority for wildfire restoration, each with varying levels of trade off. In the OWA decision space, placing full emphasis on the highest (best) values (using the risk-taking OR operator) or the lowest values (using the risk-averse AND operator) resulted in composite priority maps that cannot be recommended for practical use. More nuanced scenarios are achieved with the OWA operators representing a range of compromise decision strategies between these extremes. The OWA technique in GIS can thus help to explore the impact of decision-makers' risk attitudes in a wildfire restoration setting.</p> </abstract>

Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors

The ordered weighted averaging (OWA) operator and its associated weight vectors have been both theoretically and practically verified to be powerful and effective in modeling the optimism/pessimism preference of decision makers. When several different OWA weight vectors are offered, it is necessary to develop certain techniques to aggregate them into one OWA weight vector. This study firstly details several motivating examples to show the necessity and usefulness of merging those OWA weight vectors. Then, by applying the general method for aggregating OWA operators proposed in a recent literature, we specifically elaborate the use of OWA aggregation to merge OWA weight vectors themselves. Furthermore, we generalize the normal preference degree in the unit interval into a preference sequence and introduce subsequently the preference aggregation for OWA weight vectors with given preference sequences. Detailed steps in related aggregation procedures and corresponding numerical examples are also provided in the current study.

OWA aggregation with dual preferences for basic uncertain information

Basic Uncertain Information (BUI) as a newly introduced concept generalized a wide range of uncertain information. The well-known Ordered Weighted Averaging (OWA) operators can flexibly and effectively model bipolar preferences of decision makers over given real valued input vector. However, there are no extant methods for OWA operators to be carried out over given BUI vectors. Against this background, this study firstly discusses the interval transformation for BUI and elaborately explains the reasonability within it. Then, we propose the corresponding preference aggregations for BUI in two different decisional scenarios, the aggregation for BUI vector without original information influencing and the aggregation for BUI vector with original information influencing after interval transformation. For each decisional scenario, we also discuss two different orderings of preference aggregation, namely, interval-vector and vector-interval orderings, respectively. Hence, we will propose four different aggregation procedures of preference aggregation for BUI vector. Some illustrative examples are provided immediately after the corresponding aggregation procedures.

An improved OWA-Fuzzy AHP decision model for multi-attribute decision making problem

Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model.

Calculation Model for the Mixing Amount of Internal Curing Materials in High-strength Concrete based on Modified MULTIMOORA

Abstract The internal curing technology has been widely applied to high-strength concrete, for it can make the high-strength concrete marked by low shrinkage and durable frost resistance. The key to its extension and application lies in the reasonable mixing amount of internal curing materials. To address this problem, scholars have proposed a method for determining the water demand in internal curing; however, the water release of internal curing materials is difficult to obtain by measurement due to the mixing method. Therefore, this paper proposed a calculation model for the mixing amount of internal curing materials based on the modified MULTIMOORA method (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form). First, different internal curing materials (super absorbent polymer (SAP), lightweight aggregate (LWA)) and pretreatment methods were selected to calculate their compressive strength, self-shrinkage and frost durability according to a proposed test scheme on the mixing amount of internal curing materials, and in such case, the comprehensive performance evaluation of the above indexes was turned into a multi-attribute decision-making problem. Second, the ordered weighted averaging (OWA) method and the entropy weight method were used to determine the subjective and objective weights of the indexes respectively, to eliminate the impact of outliers in the subjective evaluation values. Finally, the comprehensive performance of each test group was sorted using MULTIMOORA, and based on the sorting results and the calculation model, the mixing amount of internal curing materials was determined. The numerical example application results showed that the mixing amount of SAP curing material calculated based on the model herein was 1.276 kg/m3, and the mixing method adopted the pre-water absorption method with the total water-binder ratio unchanged. The numerical example evaluation results were in good agreement with the test results. The internal curing effect of SAP was better than that of LWA, and reached the best when the mixing amount was calculated at 25 times the water release rate and the requirement for the maximum total water diversion was met. The study may provide new ideas for extension and application of the internal curing technology.

Bonferroni Prioritized Aggregation Operators Applied to Government Transparency

This article applies the Bonferroni prioritized induced heavy ordered weighted average (OWA) to analyze a series of data and focuses on the Bonferroni average and heavy induced prioritized aggregation operators. The objective of the present work is to present a new aggregation operator that combines the heavy induced prioritized Bonferroni and its formulations and represents the Bonferroni mean with variables that induce an order with vectors that are greater than one. This work develops some extensions using prioritization. The main advantage is that different types of information provided by a group of decision makers to compare real situations are included in this formulation. Finally, an example using the operators to calculate the transparency of the websites of the 32 states of Mexico was performed. The main idea was to visualize how the ranking can change depending on the importance of the five components of the methodology. The main results show that it is possible to detect some important changes depending on the operator and the experts considered.

Performance benchmarking model for sustainability of aquatic centres in cold regions: Development and implementation

Aquatic Centres (ACs) in cold regions, serving thousands of visitors year-round, strive to achieve sustainability of their complex processes, e.g., safe indoor environment, water and energy efficiency, and customer satisfaction. In the absence of a structured performance benchmarking process, the facility management functions with no impetus for improving the performance of these components. A performance benchmarking model using a multilevel approach is developed to infer the performance of the key components for improving the sustainability performance of ACs. The bottom-up approach aggregated the scores of sixty-three performance indicators (PIs) using Ordered Weighted Averaging (OWA) to generate the performance indices for the seven key components. Sensitivity analysis identified the indicators with a significant contribution to the component's performance. Application of the model on five facilities operating in the province of British Columbia (BC) (Canada) provides a starting point towards sustainability of ACs. The results showed that two of the ACs (i.e., AC2 and AC5) outperforms the remaining participating centers. However, all the participating centres need to improve almost all of their key-components, with a particular focus on energy efficiency and socio-economic stability. The developed model provides assistance in decision-making at different levels of the ACs’ organizational hierarchy. This model holistically covers all the processes (e.g., water and air quality) and operations (e.g., energy, personnel, and customers) and will help the ACs in achieving 21st-century sustainable development initiatives in cold regions.

A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran

We propose an efficient integrated approach of spatial decision-making systems and geographical information science for spatially explicit sustainable development mapping. The approach was developed, and its efficiency examined for sustainability assessment in East Azerbaijan Province, Iran. To achieve this goal, sustainable development indicators were employed through GIS decision rule and spatial analysis. Accordingly, 13 main criteria and 44 sub-criteria were identified and prepared as GIS dataset. The fuzzy analytical network process (FANP) method was employed to derive the criteria weights and their significance. We also applied the Global Sensitivity Analysis (GSA) for minimizing the uncertainties associated with the FANP weights. The Ordered Weighted Averaging (OWA) method was applied to aggregate the indicators and develop the sustainable development maps. Results confirmed that integrated GIS-based decision rules can be applied for any sustainable development mapping efficiently. Results of this research present an approach for sustainable development assessment and can be applied for similar research effectually. In the case of East Azerbaijan Province, the detailed results represent the unbalanced sustainable development within the different counties of this province. This requires taking necessary actions to ensure more balanced and just economic development in the province. The degree of sustainable development shows a significant spatial correlation with the industrial activities, employment, demography, poverty and infrastructure properties. The obtained results are of great importance for decision makers to identify efficient approaches in light of sustainable development mapping.

Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

Multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster–Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster’s combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc.

Research on the Evaluation Modeling Method of User Experience Quality under Uncertain Noise Environment

Experience economy is a trend of future economic development. Enterprises can only occupy the market more successfully by enhancing the user experience in product design. The user’s product experience is affected by uncertainty noises (such as the user’s environment and different users), rendering the user experience quality evaluation results highly variable. The purpose of this paper is to study the modeling method of user experience quality evaluations under uncertain environmental noises; inspired by normal ordered weighted averaging (OWA) operators, the normal distribution probability density function is implemented to improve the normal ordered weighted averaging (OWA) operators, and a new modeling method designed for the evaluation of user experience quality under uncertainty is proposed, which can overcome the disadvantage of unreasonable weight distribution when the data size and location are different, as well as weigh the importance of both the value and the location of the data. The simulation results show that this method is more effective, accurate, and feasible than the conventional order weighted synthesis operator method. The feasibility and validity of this method are proved by user experience and comparative experiments of multiattribute bread products.

Rekomendasi Penentuan Desa Terbaik dengan Kombinasi Metode OWA (Ordered Weighted Avarage) dan Metode SAW (Simple Additive Weighting) berbasis Web

The success of the government is determined by essential aspects of the village and has increased every day and has changed into a town that has independence in carrying out activities and survival by each of its residents, to support and improve the quality of government villages which motivate for villages to compete to become the best village through a series of costs to improve the quality of the town. The process of determining the best village starts from the sub-district. The winner will be smart at the city district level up to the national level, in the process of determining the best town according to government regulations in sending villages with five aspects and each aspect that has specified specifications and through validation from the government In the process of the government in the form of a committee conducting visits and conducting assessments based on the completeness of the data with the team for each aspect and giving to the committee based on individual orders, the value submitted will do the calculation by determining the final cost of the process of finding an average value -average overall and do the division of the total aspects. To assist the procedure in choosing the best village, the researcher wants to develop an online decision support system application with features that will be equipped in the developed application by uploading documents according to conditions in the field and documentation carried out by the team towards the village, so that the order process is open. It can be witnessed by all concerned, while in the Calculation and analysis Process using the OWA (ORDERED WEIGHTED AVERAGE) combination operator to determine the average value calculation and the SAW (Simple Additive Weighting) Method as part of the ranking of several existing villages