Simple Additive Weighting Model Research Articles

Penentu Industri Kecil Unggulan Dikabupaten Muaro Jambi Menggunakan Metode Simple Additive Weghting (SAW)

Small and Medium Industries (IKM) have an important role in economic development. Muaro Jambi Regency is one of the districts in Jambi Province whose people are engaged in small and medium industrial businesses (IKM). This is inseparable from the important role of the Muaro Jambi Regency Government in supporting the development of small and medium industries (IKM). SMEs in Muaro Jambi Regency are grouped into 5 groups, namely craft SMEs, chemical and building materials SMEs, metal and electronics SMEs, food SMEs, and clothing SMEs. Of these 5 groups, it is necessary to know which group of IKM is growing widely both in terms of the number of IKM, number of workers, investment value and production value. The aim of this research is to find out and analyze which small and medium industrial groups (IKM) are superior in Muaro Jambi district and help simplify the process of selecting superior SMEs in Muaro Jambi district. The analytical model used to determine priorities for leading small and medium industries in Muaro Jambi district is the SAW (Simple Additive Weighting) model. The SAW model is used to formulate a weighted sum of priorities for the performance ranking of each alternative on all attributes, while the system design method uses the System Prototyping method. The result of this research is the SPK system with the SAW model which shows that the leading small and medium industries in Muaro Jambi district selected based on predetermined criteria are the Chemical and Building Materials industry with a total of 0.813

Simple additive weighting – a recommended seeds selections system for nile tilapia fish farmer

Nile tilapias are known to be an economically important farmed fish in Indonesia. Due to its adaptability, this tilapia fish can thrive in various aquatic conditions, making them an important commercial fish to produce locally or export internationally. Because of this potential, this study aims to provide recommendations using the decision support system to facilitate fish farmer decision-making with the Simple Additive Weighting model. This approach is expected to provide recommendations to fish farmers, or the general public interested in having their own Nile tilapia brood stock. The fish in this research was selected randomly following six criteria, namely fish weight, length, movement, fin or body deformation, fish colour and fish stomach condition. The result-based decision making model in this study provides an accuracy of 86.6%.

Analysis of the SAW Model in the Selection of Majors at ISB Atma Luhur

The department is part of the faculty, and is the focus of the field of study chosen by students. The choice of majors is very important to think about beforehand because it will have an impact on the results of these graduates and the job prospects sought later. There are many things that can be considered by high school graduates in choosing a major because if you choose rashly, you will be at risk of failing along the way. For this reason, research was carried out with an emphasis on the criteria for selecting majors at ISB Atma Luhur. The method used in this study is SAW (Simple Additive Weighting) which is a weighted sum method that is weighting criteria and alternative attributes so that normalization can be carried out and calculation of preference values is carried out and the highest and lowest rankings are obtained which are used as decision recommendations. Respondent data was taken based on the sample, namely through a non-probability sampling technique, namely in this study the respondent could only provide one opportunity to be sampled from the population so that one respondent could not be sampled twice. Respondents who were sampled were 100 people who were taken from high school graduates. The criteria presented include department accreditation, number of professionally certified lecturers, study program scholarships, and facilities. Determination of criteria based on the results of interviews with respondents and the results are taken based on the criteria with the most choices. The results of the study are alternative Information Systems with a weight or value obtained from the SAW calculation results, namely 1, Informatics Engineering with a weight of 0.8, while Digital Business is 0.6. From the results of these calculations, it can be concluded that the department that gets the highest score, namely the Information Systems major, can also be used as a recommendation as a major to be chosen by students.

Strategy in developing priority craft industry by using SAWSWOT Model in Jambi Province

This study aims to determine the quality creative craft industry to be developed and to formulate a strategy for developing these priority creative industries. The analytical model used to determine the priority creative craft industry is the SAW (Simple Additive Weighting) model. The SAWSWOT model is used to formulate the priority creative industry development strategy. The result of the study shows that the batik industry has been selected as a priority creative craft industry. At the same time, the strategies that can be used to develop the batik industry are increasing IT mastery by management staff, workforce skills, using Jambi batik design applications, and marketing through E-Commerce.

Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion

Multi-Attribute Decision Making (MADM) is used to select the best alternative from multi-alternatives based on multi-attribute (fashion material) and multi-criteria (sustainable fashion). Multi-alternatives are cotton, linen, silk, wool, acrylic, nylon, polyester, rayon, spandex, and mixed. Multi-attributes are material, texture, color, characteristic, comfort, and wearability. Multi-criteria are material fiber, smooth texture, faded color, elastic clothing, useful long, chilly and comfortable. Hybrid approaches and optimal solutions are needed to determine the best choice in decision making for both producers and consumers. The hybrid approach in MADM used is Simple Multi-Attribute Rating (SMART), Multi-Factor Evaluation Process (MFEP), Multi-Object Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Weighted Product (WP). SMART and MFEP are based on the Non-Benefit Cost Model while MOORA, SAW, and WP are based on a Benefit-Cost Model. The experimental results show that the SMART model with the best alternative is the rayon with the highest value (2.8333). The selection of the MFEP Model with the best alternative is rayon with the highest value (2.8330). The choice of MOORA model with the best alternative is rayon with the highest value (0.2595). The selection of the SAW Model with the best alternative is rayon with the highest value (0.8932). The selection of the WP Model with the best alternative is rayon with the highest value (0.1285). MADM using SMART, MFEP, MOORA, SAW, and WP for sustainable fashion yields the best alternative for consumption and production for the middle-class population in Indonesia.

Erosion susceptibility mapping of sub-watersheds for management prioritization using MCDM-based ensemble approach

Soil erosion (SE) creates several environmental problems in fragile tropical watersheds, such as channel degradation, reservoir sedimentation, and flash flood. Therefore, identification of erosion susceptible areas at the sub-watershed level is necessary for the sustainable management of available resources. Previous studies on sub-watershed prioritization are based on a particular method, which leads to uncertainty because other methods may produce different results. Therefore, in the present work, a novel MCDM (multi-criteria decision-making)–based ensemble approach has been used for the prioritization of sub-watersheds of the Dwarkeswar River basin. Five MCDM models were used in the present study, namely simple additive weighting (SAW), complex proportional assessment (COPRAS), additive ratio assessment (ARAS), technique for order preference by similarity to ideal solution (TOPSIS), and multi-objective optimization on the basis of ratio analysis (MOORA). The non-parametric Spearman rank correlation method was used to determine the best model and measure the degree of similarity among the results obtained from different models. Rank correlation indicated that the COPRAS model has the highest accuracy in the prediction of erosion susceptibility. But the final ranks were given to the sub-watersheds by averaging the ranks obtained from different MCDM models. The TOPSIS model was not included for averaging because TOPSIS shows a negative correlation with the SAW (− 0.082) and ARAS (− 0.179) models and a very low positive correlation with the COPRAS (0.181) model. This combined ensemble method placed sub-watersheds 10 and 11 in first and second ranks, respectively, on the basis of susceptibility to SE.

Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model

It is essential to assess flood risk mapping for sustainable development. The present study aimed to identify the causes of flooding and predict the extent of damage caused in the area of Miyandoab, Iran. The Driver, Pressure, State, Impacts, response (DPSIR) conceptual framework model was used to analyze the factors affecting flooding in the region. DPSIR is a system approach that identifies key relationships between humans and the environment, and its combination with the simple additive weighting (SAW) model identifies a new strategy for achieving sustainable development. The DPSIR method for flood susceptibility analysis in the region, examined the social, economic, physical, and environmental factors as the driving force. The flood risk level was then determined for the region by preparing the driving force map and mapping the region. For this purpose, the decision-making trial and evaluation laboratory (Dematel) and SAW models were used to investigate the causal relationship between the factors and calculate the weight of layers; Matlab software was used to implement the models. Finally, based on the weights extracted from the SAW method, risk mapping was performed in the geographic information system (GIS) environment. The results showed that out of the total area of the study area, about 78,462 hectares have a high risk, 91,542 hectares have medium risk, and 2,952 hectares have a low risk of flood. The results from combining the models of decision support systems and GIS indicated high efficiency in determining the areas with a high risk of flood.

Analysis, monitoring and simulation of dust hazard phenomenon in the northern Persian Gulf, Iran, Middle East

Dust is a particulate matter in the atmosphere that is created by natural and human agents. Dust has negative effects on various parts of human life, including agriculture, health and economics. In recent decades, in various areas with a lack of rainfall and drought being contested, dust has happened there. One of these areas is the northern part of the Persian Gulf in Iran which has been exposed to dust in recent years. The purpose of the present study was to evaluate and predict the hazardous phenomenon of dust in the western strip of Iran. Therefore, the data of dust from 14 synoptic stations of the study area (1990–2018) using panel data-hybrid neural network and adaptive neuro-fuzzy inference system (ANFIS) models were used. Finally, TOPSIS and simple additive weighting (SAW) multi-criteria decision-making (MCDM) models were used to prioritize more dust-prone areas. The results showed that the reliability of the panel data-hybrid neural network error estimation models is more than the ANFIS. Based on prediction models, the highest probability of occurrence of the maximum dust in the future was observed at Sarpol-e Zahab and Abadan stations (128.917 and 120.709%, respectively). According to the SAW model, the highest probability of occurrence of dust was at Abadan station (998%) and based on the TOPSIS model, Eslamabad-e Gharb, with 997%. It is necessary the inter-organizational cooperation by contracting an international memorandum with neighbouring countries in addition to domestic actions to reduce the damage caused by the dust phenomenon in the study area.

Evaluation of Iranian electronic products manufacturing industries using an unsupervised model, ARAS, SAW, and DEA models

Iranian electronic products supplier industries are developing day by day and modern techniques and facilities are assigning as well as many promotions about green products supply chain as input materials introduced into the generation cycle of industries. Current cluster study of Iranian Electronic Products Manufacturing Industries (IEPMI) comprised a technical and hierarchical evaluation carried out as the objective of current research. It was used SPSS and Excel Software to classify and analysis about 33 IEPMI via an unsupervised model, Additive Ratio Assessment (ARAS), Simple Additive Weighting (SAW) and Data Envelopment Analysis (DEA) models. Finally, a hierarchical cluster classification has developed for the 33 industries pertaining to 5 main criteria as well as the total inventory of input, output materials and facilities employed. It was found that the ranking systems based on ARAS and SAW presented the same results for IEPMI. DEA model was also classified IEPMI in terms of efficiency score.

A Novel Multi-Criteria Decision-Making Model: Interval Rough SAW Method for Sustainable Supplier Selection

Sustainability in a supply chain is a demand on the one hand and a challenge on the other. It is necessary to balance between these dimensions in order to fulfill the purpose of the supply chain. Therefore, in the first phase—i.e., in procurement—it is necessary to take into account its sustainability. In this paper, a sustainable supplier was selected respecting all three aspects of sustainability. The evaluation was carried out on the basis of a total of 21 criteria arranged into two levels and three groups. A new Interval Rough SAW (simple additive weighting) method, which represents a contribution to the treatment of problems belonging to the multi-criteria decision-making (MCDM), was developed. The integration of interval rough numbers with the SAW method was completed. In addition, the full consistency method (FUCOM) was applied to determine the weights of the criteria. The integrated FUCOM-Interval Rough SAW model enables treatment of multi-criteria problems while reducing subjectivity to the lowest possible level and eliminating uncertainties and ambiguities. The results obtained were determined throughout a sensitivity analysis consisting of a change in the weight of the criteria and the influence of dynamic matrices on the change in ranks. In addition, Spearman’s rank correlation coefficient (SCC) was calculated to confirm the stability of the previously obtained results.

A data envelopment analysis game theory approach for constructing composite indicator: An application to find out development degree of cities in West Azarbaijan province of Iran

Data envelopment analysis (DEA) model has been widely applied for constructing composite indicator and finding development degree of areas. With the increasing number of indicators, the distinguish power of DEA model is decreased. In this paper, in order to increase distinguish power in DEA model and find out the fair weights in cross-efficiency DEA context, the game theory approach is applied. The DEA-Game theory approach is used to rank cities in West Azarbaijan province of Iran. First, 68 suitable indicators are determined and then, the indicators are classified in 10 sectors. Finally, the actual data for year 2013 is gathered and DEA-Game theory model is applied. To verify and validate the DEA-Game theory approach, simple additive weighting (SAW) and TOPSIS methods are used and the results are compared. The Spearman correlation between DEA-Game, SAW and TOPSIS models shows that the DEA-Game theory model is suitable for constructing the composite indicators.

A hybrid multiple criteria decision-making model for the sustainable management of aquifers

In this study, a hybrid multiple criteria decision-making (HMCDM) model was proposed for prioritizing scenarios for managing groundwater use from an aquifer. Three scenarios, including the construction of subsurface dams, the use of artificial recharge and reducing groundwater use by 5% and 10% were considered to assess the most sustainable development approach. The examined MCDM models were: simple additive weighting (SAW); and MTAHP which is a hybridization of the modified TOPSIS and the analytic hierarchy process models. The criteria proposed for determining the order preference of the scenarios included the sustainable development index (IU) and a modified water exploitation index as well as economic, social and environmental indices. To assess the technical and economic impacts of the management scenarios, modeling of the aquifer was simulated for a 3-year period using these scenarios. The results of the assessment indicated that the scenario of water withdrawal reduction by 10% was the best scenario determined in MTAHP followed by a reduction in groundwater withdrawal by 5%, the use of artificial recharge and the construction of a subsurface dam, respectively. The difference between the results of MTAHP and SAW models was in their first and third ranks, in such a way artificial recharge scored the first rank in SAW model and the third rank in MTAHP model, also withdrawal reduction by 10% scored third rank in SAW model and first rank in MTAHP model. The results of these two models have demonstrated that the construction of a subsurface dam in Shahrekord aquifer is not an appropriate management option. According to the results of this study, MTAHP models can be applied for ranking feasible management scenarios in aquifers using the redefined sustainable development and modified groundwater exploitation indices introduced in this study.

Decision Models for the Ranking of Agricultural Water Demand Management Strategies in an Arid Region

AbstractThe agricultural sector is the largest consumer of water and its demand management has a significant impact on water resources of arid areas. This research proposes a hybrid multi‐criteria decision‐making (MCDM) model to prioritize agricultural water demand management (AWDM) strategies based on surveys carried out with 35 Iranian experts in an arid region. It hybridized the advantages of the modified technique for order preference by similarity to ideal solution (MTOPSIS) and the analytical hierarchy process (AHP) methods, and it is called MTAHP. The AHP model is used to determine the relative weights and consistency ratio assessment and finally the MTOPSIS model is used to rank the strategies. Moreover, ELimination and Choice Expressing REality III (ELECTRE III) as a non‐compensatory model and simple additive weighting (SAW) as a compensatory model were used for comparison with the proposed model. The proposed model result was similar to the SAW model but different from ELECTRE III. High‐ranked strategies are training farmers, improvement of cropping patterns and land levelling and modernization of the irrigation systems in the region, respectively. The results of this research show the ability of the hybrid model of AHP and MTOPSIS to prioritize the strategies for AWDM which is recommended for other similar regions. Copyright © 2017 John Wiley & Sons, Ltd.

Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops

Cultivation priority planning is a very important and vital step in suitable and sustainable revenue of agricultural land. The growth of urban areas and industrial intensification has contributed to a reduction in valuable agricultural lands and to various environmental impacts including climate change. This reduction in agricultural land severely impacts food production and food security. In order to effectively address this issue, spatial analytical and optimization methods based on evaluating multiple criteria decision are needed to evaluate the capability and suitability of available lands for current and future food production. The objective of this study is to implement the GIS and multi-criteria decision analysis (MCDA) techniques as an improved method of multi-criteria decision making for evaluating areas suitable for cultivation priority planning of maize, rape and soybean crops. For this purpose, 12,000ha land which is located in Ardabil province, west-north of Iran was investigated by excavation of 167 soil profiles and 313 augers. After soil sampling and analysis, soils were classified in Aridisols. 24 soil series and 66 land units were identified and separated in study area. The several criteria had limitation for maize, rape and soybean cultivation in studying lands which the most limiting evaluation criteria including soil depth, slope, climate, pH, electrical conductivity, exchangeable sodium percentage, calcium carbonate and gypsum were selected for usage in prioritization models by principal component analysis and multi-dimensional scaling methods. Selected criteria were very important in growth of maize, rape and soybean. Simple additive weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS methods were used for cultivation priority planning of maize, rape and soybean crops in land units. Analytical Hierarchy Process (AHP) and Fuzzy AHP approaches were used to determine weight values of the criteria. Multivariate variance analysis proves significant difference among three methods at 0.05 probability level. With attention to allocated scores by prioritization models, crops cultivation priority was determined as maize, rape and soybean in land units, respectively and maize crop was preferred to other plants. The statistical analysis results with regard to mean comparison derived from least significant difference (LSD) test showed that Fuzzy TOPSIS method set cultivation priority planning of maize, rape and soybean crops for land units more accurately than the others, due to fuzzy TOPSIS method used appropriate values of criteria weights, twin comparing nature of alternative (crop) from positive and negative ideal, data standardization, mathematical equations and matrixes as well as fuzzy logic relations and principles for calculation of process performing. This study emphasizes the successful application of MCDA in dealing with complicated issues in the context of cultivation priority planning management. It is anticipated that, the integration of this developed framework in the planning policies of cultivation priority in developing countries as an effective tool for integrated regional land use planning can help in conducting better control over soil, land and environment losses.

A Framework for Sustainable Strategic Planning of Water Demand and Supply in Arid Regions

AbstractThis paper addresses strategic planning of water resources in an arid region based on sustainable development criteria (SDC). Three multi‐criteria decision‐making (MCDM) models, simple additive weighting (SAW), the analytic hierarchy process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), were used for detailed ranking of the strategies and assessing the result of the Quantitative Strategic Planning Matrix (QSPM). The results of the MCDM models were not identical, so we used aggregation approaches including mean rank, Borda count and Copeland's methods to integrate rankings. The results of sensitivity analysis revealed that all MCDM models were sensitive to the social and environmental criteria. The SAW and AHP models were also less sensitive to changes in criterion weights than the TOPSIS model. The results showed the priority of the MCDM models over QSPM analysis in incorporating SDC in detailed evaluation of the strategies. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment

Aggregation of utility-based individual preferences for group decision-making

Multi-attribute utility theory (MAUT) elicits an individual decision maker’s preferences for single attributes and develops a utility function by mathematics formulation to add up the preferences of the entire set of attributes when assessing alternatives. A common aggregation method of MAUT for group decisions is the simple additive weighting (SAW) method, which does not consider the different preferential levels and preferential ranks for individual decision makers’ assessments of alternatives in a decision group, and thus seems too intuitive in achieving the consensus and commitment for group decision aggregation. In this paper, the preferential differences denoting the preference degrees among different alternatives and preferential priorities denoting the favorite ranking of the alternatives for each decision maker are both considered and aggregated to construct the utility discriminative values for assessing alternatives in a decision group. A comparative analysis is performed to compare the proposed approach to the SAW model, and a satisfaction index is used to investigate the satisfaction levels of the final two resulting group decisions. In addition, a feedback interview is conducted to understand the subjective perceptions of decision makers while examining the results obtained from these two approaches for the second practical case. Both investigation results show that the proposed approach is able to achieve a more satisfying and agreeable group decision than that of the SAW method.

A study on the evaluation of and demand forecasting for real estate using simple additive weighting model: The case of clothing stores for babies and children in the Bundang area

A study on the evaluation of and demand forecasting for real estate using simple additive weighting model: The case of clothing stores for babies and children in the Bundang area