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Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management

Faculty of Commerce, Van Lang University, Ho Chi Minh City 70000, Vietnam
Sustainability 2022, 14(11), 6565;
Submission received: 12 April 2022 / Revised: 6 May 2022 / Accepted: 25 May 2022 / Published: 27 May 2022
(This article belongs to the Special Issue Multicriteria Decision Analysis in Planning Sustainable Energy Use)


In Vietnam, rapid population and economic growth are responsible for the recent increase in solid waste. Energy production from waste is now becoming an effective solution around the world, especially in Vietnam, to solve environmental challenges while contributing to the country’s sustainable energy production. Waste-to-energy production has become a solution to the municipal solid waste problem, which is projected to increase by 10–16%. In this study, the author proposed a fuzzy MCDM model to assess and select a solid-waste-to-energy plant location in Vietnam. In the first stage, the fuzzy analytic hierarchy process (FAHP) technique is utilized to analyze the relative weight of the primary and secondary evaluation elements, and a combined compromise solution (CoCoSo) model is used to rank the candidates in the final stage. This is the first solid-waste-to-energy plant location evaluation and selection model used in a renewable energy project in Vietnam based on expert interviews and a literature review. This study’s contribution can be a significant guide in analyzing and selecting appropriate locations for solid-waste-to-energy projects, as well as for decision makers and investors in other renewable energy projects in Vietnam and throughout the world.

1. Introduction

Currently, on a daily basis, an average of almost 35,000 tons of solid trash is generated in cities, and 34,000 tons of residential solid garbage is generated in rural areas in Vietnam. About 85% of this solid waste is currently being treated mainly by the use of landfill technology that requires a lot of land, 80% of which is an unhygienic landfill with potential for environmental pollution. Vietnam is currently setting goals to increase power production in order to ensure energy security and economic development toward a "green" and sustainable direction. Therefore, energy production from waste is currently becoming an effective solution to address environmental challenges and land use needs in urban areas. However, this resource is being wasted and not fully utilized for energy production [1]. Turning solid waste into energy can help provide a clean and cheaper source of energy, reduce solid waste pollution, and protect the environment. Therefore, according to experts, Vietnam should prioritize large-scale waste power development projects using modern and advanced technology to convert waste into energy. As a result, the building of a solid-waste-to-energy facility in Vietnam is required. Solid waste statistics from 2002 to 2020 as shown in Figure 1.
Solid waste is waste in solid form, including all waste generated by humans in the process of daily life, production, and business. The composition of solid waste varies depending on the climate, locality, economic conditions, and other factors. However, it can be divided into the following three basic types [2]:
  • ✓ Combustible substances (plastics, leather, rubber, paper, food, straw, wood, and grass).
  • ✓ Non-combustible substances (stone, crockery, porcelain, ferrous metals, non-ferrous metals, and glass).
  • ✓ Mixed substances (sand, soil, hair, and pebbles).
Currently, the technology of waste incineration for electricity generation is gaining interest in various countries because it shows outstanding advantages compared to traditional landfill methods and incinerators, such as reducing over 90% of the volume and waste volume; potentially using heat; reducing greenhouse gas emissions compared to the disposal approach; and reducing water pollution and smells. There are three waste incinerator technologies most commonly used in power generation waste incineration plants today, namely, stocker incinerators, rotary kiln incinerators, and fluidized bed incinerators [3].
Many studies have used multi-criteria decision-making (MCDM) methodologies in many sectors of science and engineering, and this tendency has been growing for many years. The placement selection problem is one of the domains in which the MCDM model has been used [4]. The MCDM’s progression is shown in Figure 2 [5].
The major purpose of this research is to present a fuzzy MCDM model, which incorporates the fuzzy analytic hierarchy process (FAHP) and combined compromise solution (CoCoSo) methodologies, for solid-waste-to-energy plant location selection. The FAHP technique is utilized to analyze the relative weight of the primary and secondary evaluation elements, and the CoCoSo model is used to rank the candidates in the final stage. To show the usefulness of the suggested methodology, a case study on five different locations is carried out. Finally, sensitivity analysis is used to evaluate the proposed model’s robustness.

2. Literature Review

The fast population growth and changes caused by the improvement in living standards have shown solid waste disposal to be an environmental hazard [6,7,8]. A random and non-scientific selection of landfill sites may have a negative impact on the climate; people; and surrounding aquatic resources, including groundwater [9,10,11]. Frequently, it is a challenge to decision making in a multi-criteria environment. Therefore, the use of tools, such as FAHP and fuzzy TOPSIS, should be preferred to emphasize the pros and cons of each of the studied options [12]. Yildirim et al. [13] used an MCDM model and geographical information system (GIS) to solve the problem of solid waste landfill selection. In another study, Dolui et al. [14] stated that an inappropriate selection of landfill sites may have many disadvantageous impacts on the local environment and public health, and face resistance from the political opposition and local community; thus, they used a combination of MCDM and GIS models to solve the above problems. Al-Anbari et al. [15] used AHP and fuzzy TOPSIS to rank landfill sites. Villacreses et al. [16] proposed MCDM and GIS for Wind farms suitability location. Ekmekçioğlu et al. [17] suggested fuzzy TOPSIS and FAHP for the selection of an appropriate disposal method and site for municipal solid waste, and they adopted an integrated system of GIS-based MCDM to provide an effective tool for solving the problem of landfill selection. Mallick’s paper [18] provided an integrated framework with a focus on structuring the decision-making process for landfill suitability site maps. Wichapa et al. [19] discussed using a combined method of FAHP and goal programming (GP) to maximize the satisfaction level regarding relevant impacts, such as social and environmental impacts, which is as important as minimization of the total cost. Hanine et al. [20] applied a combination of the fuzzy TODIM and FAHP methods for landfill location selection. Wang et al. [21] proposed fuzzy MCDM to optimize the site selection process for biomass power plants.
WASPAS is a well-known and efficient solution for solving problems, and it was proposed by Zavadskas [22]. Currently, there are many studies using the WASPAS method to solve multi-criteria problems. The following are some examples: Mishra et al. [23] introduced the WASPAS method with Fermatean fuzzy sets (FFSs) for the healthcare waste disposal location selection problem. Nie et al. [24] suggested a newly extended WASPAS technique, which involves three novel procedures and is utilized to handle MCDM issues in the interval number environment; Chakraborty et al. [25] applied the WASPAS method as a multi-criteria decision-making tool. Turskis et al. [26] used a hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. By combining AHP and WASPAS methods, Baušys et al. [27] solved the problem of choosing appropriate garage locations for residential houses. Bagočius et al. [28] proposed a hybrid MCDM model and WASPAS method to select and rank feasible locations for wind farms and to assess the types of wind turbines in the Baltic Sea offshore area. Mardani et al. [29] presented a study that presented a new fuzzy approach under the Hesitant Fuzzy Set (HFS) approach using Stepwise Weight Assessment Ratio Analysis (SWA-RA) and the WASPAS method to evaluate and rank the critical challenges of DT intervention in order to control the COVID-19 outbreak. Mihajlović et al. [30] implemented WASPAS and AHP methods when choosing a logistics distribution center location in Serbia. Table 1 provides an overview of studies on site selection and application of MCDM models.

3. Methodology

Multi-criteria decision making (MCDM) is emerging as a discipline in operations research. While the fuzzy theory has been included in MCDM research, both approaches have essentially been developed along the same lines. This has made the fuzzy MCDM model become an effective tool to assist decision makers in choosing the optimal solution. In this study, the author proposed a fuzzy MCDM model to assess and select a solid-waste-to-energy plant location in Vietnam. This study’s recommended approach consists of the following three key steps, and a research graph is shown in Figure 3:
Step 1. The criteria affecting the evaluation and selection of the optimal location are determined.
Step 2. The weights of the criteria are identified using the fuzzy analytic hierarchy process (FAHP) model.
Step 3: In the last stage, the CoCoSo approach is used to evaluate all potential locations based on the criteria.
Figure 3. Research graph.
Figure 3. Research graph.
Sustainability 14 06565 g003

3.1. Definition of a Fuzzy Number

Zadeh [31] showed that fuzzy sets are an extension of the traditional concept of sets. Fuzzy sets were thought to be a collection of components with varying degrees of membership. According to the traditional set theory [32,33], the membership of items in a set is evaluated in binary terms using a bivalent condition, which means that an element either belongs to or does not belong to the set. Hsieh, Lu, and Tzeng [34] are credited with the mathematical notion. The membership function of a fuzzy number, Triangular Fuzzy Number (TFN) H ˜ , is defined as µ H ˜   ( x ) : ℝ → [0, 1].
µ H ~ ( x ) = { ( x z ) / ( q z   ) ,               l   x   q ( k x ) / ( k q ) ,             q x   k 0 ,                                                               otherwise
According to Equation (1), z and k represent the lower and upper limits of the fuzzy number H ˜ , respectively, and q represents the modal value for e H ˜ (as Figure 4). The TFN is indicated by H ˜ = ( z ,   q ,   k ) . The operational rules of H ˜ 1 = ( z 1 ,   q 1 ,   k 1 ) and H ˜ 2 = ( z 2 ,   q 2 ,   k 2 ) are shown in Equations (2)–(6).
Fuzzy number addition
H ˜ 1   H ˜ 2 = ( z 1 ,   q 1 ,   k 1 )     ( z 2 ,   q 2 ,   k 2 ) = ( z 1 + z 2 ,   q 1 ,   + q 2 ,   k 1 + k 2 )
Fuzzy number multiplication ×
H ˜ 1 × H ˜ 2 = ( z 1 ,   q 1 ,   k 1 ) × ( z 2 ,   q 2 ,   k 2 ) = ( z 1 z 2 ,   q 1 ,   q 2 ,   k 1 k 2 ) for   z 1 , z 2 > 0 ; q 1 , q 2 > 0 ;   k 1 , k 2 > 0
The fuzzy number is subtracted Ꝋ
H ˜ 1   H ˜ 2 = ( z 1 ,   q 1 ,   k 1 ) ( z 2 ,   q 2 ,   k 2 ) = ( z 1   k 2 ,   q 1     q 2 ,   k 1 z 2   )
The division of a fuzzy number
H ˜ 1     H ˜ 2 = ( z 1 ,   q 1 , k 1 )   ( z 2 ,   q 2 , k 2 ) = ( z 1 / k 2 ,   q 1 , / q 2 , k 1 / z 2 ) for   z 1 , z 2 > 0 ; q 1 , q 2 > 0 ;   k 1 , k 2 > 0
The fuzzy number’s reciprocal
H ˜ 1 =   ( z 1 ,   q 1 ,   k 1 ) 1 = ( 1 / z 1 , 1 / q 1 ,   1 /   k 1   ) for   z 1 > 0 ;     q 1 > 0 ;   k 1 > 0
In this study, the authors compare the assessment dimension for biomass furnace providers using nine core language concepts with the fuzzy nine-level scale proposed by Gumus [35]. These linguistic variables are represented by positive triangular fuzzy integers.

3.2. Fuzzy AHP (FAHP)

The suggested fuzzy AHP implementation approach consists of the following two stages:
Stage 1: For each criterion, a pairwise comparison matrix is built. The linguistic words are then assigned to the pairwise comparisons, as seen in matrix H ˜ below:
H ˜ = [ 1 h ˜ 12 h ˜ 1 n h ˜ 21 1 h ˜ 2 n h ˜ n 1 h ˜ n 2 1 ] = [ 1 h ˜ 12 h ˜ 1 n 1 / h ˜ 21 1 h ˜ 2 n 1 / h ˜ 2 n h ˜ n 2 1 ]
Stage 2: Using the geometric mean approach, the fuzzy geometric mean and the fuzzy weights of each criterion are computed [36]:
  t   ˜ c = ( h ˜ c 1 × . ×   h ˜ c d ×   × h ˜ c d ) 1 / n p   ˜ c = t   ˜ c × ( t   ˜ 1     t   ˜ c     t   ˜ n ) 1
where h ˜ c d represents the fuzzy comparison value of dimension c to criteria d.
  • t   ˜ c is the geometric mean of the fuzzy comparison value of criterion c to each criteria.
  • s   ˜ c is the cth criterion’s fuzziness weight.

3.3. Combined Compromise Solution (CoCoSo)

CoCoSo is an MCDM technique that uses an integrated simple additive weighting and an exponentially weighted product model [37]:
Stage 1: Create the basic decision-making matrix:
x c d = [ x 11 x 12 x 1 n x 21 x 22 x 2 n x m 1 x m 2 x m n ] With   c = 1 , 2 , ,   m ;         d = 1 , 2 , , n
Stage 2: Normalize the criteria values:
For the advantageous criterion:
t c d = x c d   min x c d c         max x c d c       min x c d c
Regarding the cost criterion:
  t c d =   max x c d c     x c d         max x c d c       min x c d c
Stage 3: Calculate the total of the weighted comparability sequence ( S c ) and the total of the power weight of comparability sequences for each alternative, as well as the sum of the weighted comparability sequence ( P c ) for each choice:
S c = d = 1 n ( s d t c d )
Compute the S c value using the grey relational generation method:
P c = d = 1 n ( t c d s d )
Calculate the P c value using the WASPAS multiplicative attitude.
Stage 4: Determine the relative weights of each alternative.
Determine the arithmetic mean of the sums of the WSM and WPM scores:
k c a = P c + S c   c = 1 m ( P c + S c ) ,
Calculate the total of the relative scores of WSM and WPM in comparison to the best alternative:
k c b = S c     m i n S c c + P c     m i n P c c
Calculate the balanced compromise of the WSM and WPM model scores as follows:
k c c =   λ ( S c ) + ( 1     λ ) P c   λ m a x S c c +   ( 1     λ ) m a x P c c
Stage 5: Define the final ranking of the alternative k c :
k c = ( k c a k c b k c c ) 1 3 + 1 3 ( k c a + k c b + k c c )

4. Case Study

The incineration of waste to generate electricity is one of the current advanced methods that can take advantage of available raw materials, limit the use of fossil fuels, and, at the same time, reduce the area of land used for landfilling. According to the latest report from the Vietnam Ministry of Industry and Trade, domestic waste from urban and rural areas is discharged into the environment at about 70,000 tons per day; Hanoi and Ho Chi Minh City alone generate between 7000 and 8000 tons of garbage every day. With the large amount of waste in Vietnam, burning waste to generate electricity can generate hundreds and thousands of MW to supply the power system. However, this type of power generation has not really reached its potential [38]. Trash is piled atop each other on Tran Huu Duc Street in Hanoi is shown in Figure 5.
Typical studies on the site assessment selection process focus nearly entirely on how to determine the ideal choice with a single model, ignoring the management concept. That is, previous studies cannot be immediately integrated into project management due to a lack of actual operability. In this work, the author suggests a fuzzy MCDM model to analyze and select the location of a solid-waste-to-electricity facility in Vietnam. The FAHP technique is utilized to analyze the relative weights of the primary and secondary evaluation elements, and the WASPAS model is then used to rank the candidates in the final stage. All of the criteria in the first stage affecting the waste-to-energy plant site selection is presented in Table 2.
According to the statistics on the amount of solid waste and the opinions of experts, there are five places to consider for investment in a solid-waste-to-energy plant, namely, Ha Noi (DMUWE 1), Ho Chi Minh (DMUWE 2), Hue (DMUWE 3), Da Nang (DMUWE 4), and Hai Phong (DMUWE 5). The FAHP methodology is combined with the CoCoSo method in this work to create a unique algorithm–fuzzy multi-criteria decision-making model to evaluate the placement of solid-waste-to-energy plants. To accomplish this objective, the fuzzy AHP technique is used to examine fuzzy information from expert evaluations in order to determine priority weights. Table 3 displays the FAHP findings.
During this step, a decision-making matrix for the evaluation of solid-waste-to-energy plant locations is constructed. In this respect, five potential locations in Vietnam are selected as a case study. The problem is addressed using the CoCoSo method, as described in Section 3, and the results are shown in Table 4, Table 5 and Table 6.
According to Table 6 and Figure 6, the potential locations’ ranks are as follows: DMUWE 5 ≻ DMUWE 1 ≻ DMUW3 ≻ DMUW4 ≻ DMUWE 2. Thus, Hai Phong (DMUWE 5) is the optimal location. It is demonstrated that, in addition to Equation (16), which is a typical technique used for coefficient λ results, a fixed value in the range of 0.1, 0.2, 0.3,..., 1.0 may be employed. As a result, in the first stage of the sensitivity analysis, a change was made to the coefficient λ. Table 6 shows the ranking performance of the CoCoSo model for various λ values.
Table 7 and Figure 7 display the relative computed values of the alternatives based on the value of the coefficient λ. It should be noted that the coefficient λ values have no effect on the change in the rank of the alternatives. This study resulted in the effective development of a hybrid MCDM model that uses FAHP and CoCoSo to determine the supplier assessment and selection method in renewable energy projects.
The site selection of energy conversion plants requires a complex decision-making process, in which the decision maker must consider all quantitative and qualitative factors. In this case study, the author proposed and applied a fuzzy MCDM model, which included FAHP and CoCoSo. FAHP was used to determine the weight of all criteria, and the CoCoSo model was applied to rank five alternatives. Finally, a sensitivity analysis was conducted to evaluate the proposed model’s robustness.

5. Conclusions

In this study, the author proposed a fuzzy MCDM model to assess and select a solid-waste-to-energy plant location in Vietnam. The FAHP technique was utilized to analyze the relative weight of the primary and secondary evaluation elements, and the WASPAS model was then used to rank the candidates in the final stage. A real problem of solid-waste-to-energy site selection in Vietnam was employed to examine the performance of the proposed algorithm. As a result, Hai Phong (DMUWE 5) was found to be the optimal location to build a solid-waste-to-energy plant.
The following are some of the most noteworthy contributions and achievements in this research:
  • ✓ The suggested model is the first fuzzy MCDM model used to evaluate and select solid-waste-to-energy plant locations in Vietnam, and it is based on expert interviews and literature research.
  • ✓ This is the first research to present a case study on the assessment of locations for the renewable energy industry, using a mix of fuzzy theory, the AHP model, and the CoCoSo model.
  • ✓ The findings of this study may be used as a beneficial reference to analyze and select the best sites for solid-waste-to-energy projects, as well as for decision makers and investors in other renewable energy initiatives.
For further research on this topic, the work may be expanded to other MCDM models, such as TOPSIS, data envelopment analysis (DEA), and the WASPAS model.


This research was funded by Van Lang University and the APC was funded by Van Lang University.


The authors wish to express their gratitude to Van Lang University, Vietnam, for financial support for this research.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Tăng Cường Sản Xuất điện Năng Từ chất Thải, Góp Phần Phát Triển Năng Lượng Và Bảo Vệ Môi Trường. Available online: (accessed on 10 December 2021).
  2. CHẤT THẢI RẮN, THÀNH PHẦN PHÂN LOẠI VÀ CÁC QUY TRÌNH XỬ LÝ. Available online: (accessed on 13 December 2021).
  3. Một Số Công Nghệ Lò đốt Chất Thải Phát điện. Available online: (accessed on 10 December 2021).
  4. Wang, C.N.; Nguyen, V.T.; Duong, D.H.; Thai, H.T.N. A hybrid fuzzy analysis network process (FANP) and the technique for order of preference by similarity to ideal solution (TOPSIS) approaches for solid waste to energy plant location selection in Vietnam. Appl. Sci. 2018, 8, 1100. [Google Scholar] [CrossRef] [Green Version]
  5. Benmoussa, N.; Elyamami, A.; Mansouri, K.; Qbadou, M.; Illoussamen, E. A multi-criteria decision making approach for enhancing university accreditation process. Eng. Technol. Appl. Sci. Res. 2019, 9, 3726–3733. [Google Scholar] [CrossRef]
  6. Al-Ansari, A.; Al-Hanbali, A.; Knutsson, S. Locating solid waste landfills in Mafraq city, Jordan. J. Adv. Sci. Eng. Res. 2012, 2, 40–51. [Google Scholar]
  7. Cheng, S.; Chan, C.W.; Huang, G.H. Using multiple criteria decision analysis for supporting decisions of solid waste management. J. Environ. Sci. Health 2002, 37, 975–990. [Google Scholar] [CrossRef]
  8. Pandey, P.C.; Sharma, L.K.; Nathawat, M.S. Geospatial strategy for sustainable management of municipal solid waste for growing urban environment. Environ. Monit. Assess. 2011, 184, 2419–2431. [Google Scholar] [CrossRef]
  9. Vaverková, M.D. Landfill Impacts on the environment-review. Geosciences 2019, 9, 431. [Google Scholar] [CrossRef] [Green Version]
  10. Zamorano, M.; Molero, E.; Hurtado, A.; Grindlay, A.; Ramos, A. Evaluation of a municipal landfill site in Southern Spain with GIS-aided methodology. J. Hazard. Mater. 2008, 160, 473–481. [Google Scholar] [CrossRef]
  11. Singh, C.K.; Kumar, A.; Roy, S.S. Estimating Potential Methane Emission from Municipal Solid Waste and a Site Suitability Analysis of Existing Landfills in Delhi, India. Technologies 2017, 5, 62. [Google Scholar] [CrossRef] [Green Version]
  12. Beskese, A.; Demir, H. Landfill site selection using fuzzy AHP and fuzzy TOPSIS: A case study for Istanbul. Environ. Earth Sci. 2015, 73, 3513–3521. [Google Scholar] [CrossRef]
  13. Yildirim, V.; Memisoglu, T.; Bediroglu, S.; Colak, H.E. Municipal solid waste landfill site selection using multi-criteria decision making and GIS: Case study of Bursa province. J. Environ. Eng. Landsc. Manag. 2018, 26, 107–119. [Google Scholar] [CrossRef]
  14. Dolui, S.; Sarkar, S. Identifying potential landfill sites using multicriteria evaluation modeling and GIS techniques for Kharagpur city of West Bengal, India. Environ. Chall. 2021, 5, 100243. [Google Scholar] [CrossRef]
  15. Al-Anbari, A.; Thameer, Y.; Al-Ansari, N.; Knutsson, S. Ranking landfill sites in Najaf Governorate, Iraq ssing AHP and fuzzy TOPSIS methods. J. Civ. Eng. Archit. 2016, 10, 815–821. [Google Scholar]
  16. Villacreses, G.; Gaona, G.; Martínez-Gómez, J.; Jijón, D.J. Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renew. Energy 2017, 109, 275–286. [Google Scholar] [CrossRef]
  17. Ekmekçioğlu, M.; Kaya, T.; Kahraman, C. Fuzzy multicriteria disposal method and site selection for municipal solid waste. Waste Manag. 2010, 30, 1729–1736. [Google Scholar] [CrossRef]
  18. Mallick, J. Municipal solid waste landfill site selection based on fuzzy-AHP and geoinformation techniques in Asir region Saudi Arabia. Sustainability 2021, 13, 1538. [Google Scholar] [CrossRef]
  19. Wichapa, N.; Khokhajaikiat, P. Solving multi-objective facility location problem using the fuzzy analytical hierarchy process and goal programming: A case study on infectious waste disposal centers. Oper. Res. Perspect. 2017, 4, 39–48. [Google Scholar] [CrossRef]
  20. Hanine, M.; Boutkhoum, O.; Tikniouine, A.; Agouti, T. Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection. SpringerPlus 2016, 5, 1–30. [Google Scholar] [CrossRef] [Green Version]
  21. Wang, C.N.; Tsai, T.T.; Huang, Y.F. A model for optimizing location selection for biomass energy power plants. Processes 2019, 7, 353. [Google Scholar] [CrossRef] [Green Version]
  22. Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J.; Zakarevicius, A. Optimization of weighted aggregated sum product assessment. Elektron. Elektrotechnika 2012, 122, 3–6. [Google Scholar] [CrossRef]
  23. Mishra, A.R.; Rani, P. Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex Intell. Syst. 2021, 7, 2469–2484. [Google Scholar] [CrossRef]
  24. Nie, R.X.; Wang, J.Q.; Zhang, H.Y. Solving solar-wind power station location problem using an extended weighted aggregated sum product assessment (WASPAS) technique with interval neutrosophic sets. Symmetry 2017, 9, 106. [Google Scholar] [CrossRef]
  25. Chakraborty, S.; Zavadskas, E.K.; Antucheviciene, J. Applications of WASPAS method as a multi-criteria decision-making tool. Econ. Comput. Econ. Cybern. Stud. Res. 2015, 49, 5–22. [Google Scholar]
  26. Turskis, Z.; Zavadskas, E.K.; Antucheviciene, J.; Kosareva, N. A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. Int. J. Comput. Commun. Control. 2015, 10, 113. [Google Scholar] [CrossRef]
  27. Baušys, R.; Juodagalvienė, B. Garage location selection for residential house by WASPAS-SVNS method. J. Civ. Eng. Manag. 2017, 23, 421–429. [Google Scholar] [CrossRef]
  28. Bagočius, V.; Zavadskas, E.K.; Turskis, Z. Multi-person selection of the best wind turbine based on the multi-criteria integrated additive-multiplicative utility function. J. Civ. Eng. Manag. 2014, 20, 590–599. [Google Scholar] [CrossRef]
  29. Mardani, A.; Saraji, M.K.; Mishra, A.R.; Rani, P. A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak. Appl. Soft Comput. 2020, 96, 106613. [Google Scholar] [CrossRef] [PubMed]
  30. Mihajlović, J.; Rajković, P.; Petrović, G.; Ćirić, D. The selection of the logistics distribution center location based on MCDM methodology in southern and eastern region in Serbia. Oper. Res. Eng. Sci. Theory Appl. 2019, 2, 72–85. [Google Scholar] [CrossRef]
  31. Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef] [Green Version]
  32. Liou, J.J.H.; Yen, L.; Tzeng, G.H. Building an effective safety management system for airlines. J. Air Transp. Manag. 2008, 14, 20–26. [Google Scholar] [CrossRef]
  33. Wu, W.W.; Lee, Y.T. Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Syst. Appl. 2007, 32, 499–507. [Google Scholar] [CrossRef]
  34. Hsieh, T.Y.; Lu, S.T.; Tzeng, G.H. Fuzzy MCDM approach for planning and design tenders selection in public office buildings. Int. J. Proj. Manag. 2004, 22, 573–584. [Google Scholar] [CrossRef]
  35. Gumus, A.T. Evaluation of hazardous waste transportation firms by using a two-step Fuzzy-AHP and TOPSIS methodology. Expert Syst. Appl. 2009, 36, 4067–4074. [Google Scholar] [CrossRef]
  36. Feyzi, S.; Khanmohammadi, M.; Abedinzadeh, N.; Aalipour, M. Multi-criteria decision analysis FANP based on GIS for siting municipal solid waste incineration power plant in the north of Iran. Sustain. Cities Soc. 2019, 47, 101513. [Google Scholar] [CrossRef]
  37. Yazdani, M.; Zarate, P.; Zavadskas, E.K.; Turskis, Z. A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. 2019, 57, 2501–2519. [Google Scholar] [CrossRef]
  38. Biến Rác Thải Thành điện: Cần có Cơ Chế Thúc đẩy đầu Tư. Available online: (accessed on 13 December 2021).
  39. Wu, Y.; Chen, K.; Zeng, B.; Yang, M.; Geng, S. Cloud-based decision framework for waste-to-energy plant site selection–A case study from China. Waste Manag. 2016, 48, 593–603. [Google Scholar] [CrossRef]
  40. Gao, J.; Li, X.; Guo, F.; Huang, X.; Men, H.; Li, M. Site selection decision of waste-To-Energy projects based on an extended cloud-TODIM method from the perspective of low-carbon. J. Clean. Prod. 2011, 303, 127036. [Google Scholar] [CrossRef]
  41. Tavares, G.; Zsigraiová, Z.; Semiao, V. Multi-criteria GIS-based siting of an incineration plant for municipal solid waste. Waste Manag. 2011, 31, 1960–1972. [Google Scholar] [CrossRef]
  42. World Bank. Waste Management in China: Issues and Recommendation. Urban Development Working Papers East Asia Infrastructure Department World Bank; World Bank: Washington, DC, USA, 2005. [Google Scholar]
Figure 1. Solid waste in Vietnam.
Figure 1. Solid waste in Vietnam.
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Figure 2. General process of MCDM model.
Figure 2. General process of MCDM model.
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Figure 4. A fuzzy integer with a triangular shape.
Figure 4. A fuzzy integer with a triangular shape.
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Figure 5. Trash is piled atop each other on Tran Huu Duc Street in Hanoi.
Figure 5. Trash is piled atop each other on Tran Huu Duc Street in Hanoi.
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Figure 6. Ranking list.
Figure 6. Ranking list.
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Figure 7. Sensitivity analysis.
Figure 7. Sensitivity analysis.
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Table 1. Overview of some work on site selection and application of MCDM model.
Table 1. Overview of some work on site selection and application of MCDM model.
No.AuthorsMCDM ModelsMain Findings
1Yildirim et al.Geographical information system (GIS); TOPSISCombined GIS and TOPSIS models for municipal solid waste landfill site selection
2Dolui et al.AHP, fuzzy AHP, SRS and RSW weightage methodsIdentified potential landfill sites
3Al-Anbari et al.AHP, fuzzy TOPSISSite capacity criterion was found to be more important than land price and land elevation
4Tavares et al.Geographical information system (GIS); AHPSystem effectiveness was provided in ranking potential locations
5Ekmekçioğlu et al.AHP, TOPSISIllustrated the importance of weights on various criteria when choosing the optimized location
6Mallick’s et al.GIS-based fuzzy-AHP-MCDA methodFindings can provide an appropriate guideline to assist decision makers in selecting an optimal landfill site
7Wichapa et al.FAHP; goal programming (GP)The proposed model can lead to selection of optimal locations for infectious waste disposals
8Hanine et al.Fuzzy AHP; fuzzy TODIMComparisons of two MCDM methods were made
9Wang et al.FAHP, TOPSISThe proposed MCDM model can address the complex problems in location selection
10ZavadskasWeighted sum model (WSM); weighted product model (WPM)The proposed MCDM method increased the ranking accuracy of alternatives
11Mishra et al.WASPAS with Fermatean fuzzy setsThe proposed MCDM model can handle the ambiguity and inaccuracy in decision-making processes
12Nie et al.WASPASSolved location selection problem in wind power projects
13Chakraborty et al.WASPASApplied WASPAS method as a multi-criteria decision-making tool
14Turskis et al.Fuzzy AHP; fuzzy WASPASApplied MCDM model for construction site selection
Table 2. List of main and sub-criteria.
Table 2. List of main and sub-criteria.
No.Main CriteriaSub-CriteriaSource
Literature ReviewExperts
1Economic factorConstruction cost (WE1)Sadaf Feyzi et al. [36]
Yunna Wu et al. [39]
Operation and maintenance cost (WE2)Jianwei Gao et al. [40]
Yunna Wu et al. [39]
Potential demand (WE3)Jianwei Gao et al. [40]X
Land use (WE4)Sadaf Feyzi et al. [36]
Tavares et al. [41]
Yunna Wu et al. [39]
2Technical factorSolid waste quantity (WE5)Jianwei Gao et al. [40]X
Distance to the city (WE6)World bank (2005) [42]X
Distance to landfills (WE7)Jianwei Gao et al. [40]
Yunna Wu et al. [39]
Distance from electric grid (WE8)Sadaf Feyzi et al. [36]
Yunna Wu et al. [39]
3Environment factorImpact on life quality of resident (WE9)Sadaf Feyzi et al. [36]X
Elevation (WE10)Jianwei Gao et al. [40]X
Solid texture (WE11)World bank (2005) [42]X
4Social factorGrowth of GDP (WE12)Jianwei Gao et al. [40]X
Government policy (WE13)Jianwei Gao et al. [40]
Yunna Wu et al. [39]
Public support (WE14)Jianwei Gao et al. [40]
Yunna Wu et al. [39]
Available employee (WE15)Yunna Wu et al. [39]X
Table 3. Result of FAHP.
Table 3. Result of FAHP.
CriteriaFuzzy Sum of Each RowFuzzy Synthetic ExtentDegree of PossibilityNormalization
WE 112.6540817.7345624.110350.035830.068350.129650.670810.06695
WE 212.1340817.3228524.018520.034360.066760.129160.661830.06605
WE 315.5209621.6008228.496930.043950.083250.153240.862750.08610
WE 414.1768720.1479226.855830.040140.077650.144420.809280.08077
WE 518.7057525.7397733.404290.052970.099200.179631.000000.09980
WE 611.3835015.4083120.487690.032230.059380.110170.571440.05703
WE 79.4986513.2395018.792100.026900.051030.101050.499530.04985
WE 813.6686619.2726925.628670.038700.074280.137820.772950.07714
WE 914.1553920.5126428.070080.040080.079060.150950.829450.08278
WE 1010.0372413.9530319.888570.028420.053780.106950.543030.05420
WE 119.0660012.4974617.775920.025670.048170.095590.455080.04542
WE 1216.2607923.1305131.123860.046040.089150.167370.919200.09174
WE 139.5926413.0389818.301960.027160.050250.098420.481470.04805
WE 148.8931111.9804416.851370.025180.046170.090620.415200.04144
WE 1510.2119713.8854119.347780.028920.053520.104040.527840.05268
Table 4. Weighted comparability sequence and Si.
Table 4. Weighted comparability sequence and Si.
WE 10.000000.033470.033470.066950.03347
WE 20.000000.066050.066050.000000.06605
WE 30.000000.043050.086100.043050.08610
WE 40.040380.000000.040380.080770.04038
WE 50.099800.049900.099800.000000.04990
WE 60.057030.019010.000000.057030.03802
WE 70.049850.000000.024930.049850.00000
WE 80.038570.000000.038570.077140.03857
WE 90.082780.041390.041390.000000.04139
WE 100.054200.054200.000000.054200.05420
WE 110.022710.000000.022710.045420.02271
WE 120.091740.091740.045870.000000.04587
WE 130.048050.024030.024030.000000.04805
WE 140.020720.000000.020720.041440.02072
WE 150.026340.052680.000000.026340.02634
Table 5. Exponentially weighted comparability sequence and Pi.
Table 5. Exponentially weighted comparability sequence and Pi.
WE 10.00000.95470.95471.00000.9547
WE 20.00001.00001.00000.00001.0000
WE 30.00000.94211.00000.94211.0000
WE 40.94560.00000.94561.00000.9456
WE 51.00000.93321.00000.00000.9332
WE 61.00000.93930.00001.00000.9771
WE 71.00000.00000.96601.00000.0000
WE 80.94790.00000.94791.00000.9479
WE 91.00000.94420.94420.00000.9442
WE 101.00001.00000.00001.00001.0000
WE 110.96900.00000.96901.00000.9690
WE 121.00001.00000.93840.00000.9384
WE 131.00000.96720.96720.00001.0000
WE 140.97170.00000.97171.00000.9717
WE 150.96411.00000.00000.96410.9641
Table 6. Final ranking from CoCoSo.
Table 6. Final ranking from CoCoSo.
DMUWE 10.209522.548220.876721.9879
DMUWE 20.171152.000050.716351.5884
DMUWE 30.204732.342830.856931.8783
DMUWE 40.176142.163540.736941.6803
DMUWE 50.238612.685810.998612.1694
Table 7. Rankings of robots for various λ values.
Table 7. Rankings of robots for various λ values.
Alternativesλ Values
λ = 0.1λ = 0.2λ = 0.3λ = 0.4λ = 0.5λ = 0.6λ = 0.7λ = 0.8λ = 0.9λ = 1
DMUWE 11.98471.98521.98581.98671.98791.98951.99221.99702.00812.0637
DMUWE 21.58751.58761.58781.58811.58841.58891.58961.59101.59431.6106
DMUWE 31.87821.87821.87821.87831.87831.87841.87841.87861.87891.8806
DMUWE 41.67711.67771.67831.67911.68031.68191.68451.68921.70011.7545
DMUWE 52.17022.17002.16992.16972.16942.16902.16832.16712.16432.1501
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Van Thanh, N. Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management. Sustainability 2022, 14, 6565.

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Van Thanh N. Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management. Sustainability. 2022; 14(11):6565.

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Van Thanh, Nguyen. 2022. "Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management" Sustainability 14, no. 11: 6565.

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