Energy has been an essential factor in determining the governments’ policies. The countries had to produce their own energy to decrease dependency on external resources. That also provided to gain a great importance on investment on power plants. In this study, a multi-objective Mixed-Integer Linear Programming (MILP) model synchronously optimizing ﬁve targets determined as decreasing carbon dioxide (CO ) emission, increasing power consumption, increasing power plants, increasing energy generation and installed capacity was used. In described model, it was solved by considering renewable power plants in Turkey and fossil fuel-based power plants having most share in Turkey. By trying to minimize deviation values of Turkey’s 2023 targets, it aimed to determine which power plants need to be increased. To determine the priorities of these targets, Ranking Approach for fuzzy numbers by Liou and Wang (1992) was used. Besides, Fuzzy Analytic Hierarchy Process (AHP) was used to prioritize investment planning of renewable power plants in Turkey and ﬁve different kinds of power plants under the visual pollution criteria based on amount of CO emission released, environmental damage, capital costs, space requirement and provided employment were evaluated. Keywords Fuzzy Analytic Hierarchy Process · Power plants · Goal programming · Turkey’s 2023 targets · Renewable energy Introduction In this article, Fuzzy Analytic Hierarchy Process (AHP), which is one of the multiple criteria decision-making meth- Energy is one of the most essential factors for a country’s ods, was used. By considering environmental damage of social welfare and sustainable development. Recently, pop- power plants, capital costs, space requirement, their advan- ulation growth, industry development, and income growth, tages and used technological development under the main especially in developing countries, cause the energy need criteria of social acceptance, the power plants in Turkey were increase. ranked and an order of preference was composed. In addi- In view of the distribution of power plants in Turkey and tion, how many power plants should be built was determined their effects of technologic, economic, socioeconomic and with fuzzy-weighted goal programming method by consid- life quality, decision makers have to choose the best alterna- ering Turkey’s installed capacity, number of power plants, tives to accomplish their goals to decide which power plant energy generation and targets of demands in 2023. The study will be built . consisted of an introduction, literature search, multiple cri- teria decision-making methods and an application. Analytic Hierarchy Process and goal programming that are multiple criteria decision-making methods and fuzzy conditions of B Mehmet Emin Baysal these methods were described. Later, it continued with an firstname.lastname@example.org application and the results were included. Nazlı Ceren Çetin email@example.com Department of Industrial Engineering, Selcuk University, Literature search Akademi qtr. Yeni Istanbul st. No: 365, 42130 Selcuklu, Konya, Turkey Kowalski et al. worked on determining economically best Department of Industrial Engineering, Aksaray University, Adana Road E-90 Highway 7th km, 68100 Aksaray, Turkey option for sustainable energy using multiple criteria decision- 123 Complex & Intelligent Systems making methods for several gas emissions released by power in Northern Spain by achieving seven targets about main- plants. According to the results obtained, natural gas was tenance, management, investment, distances between power determined as the best fuel . Pilavachi et al. made a mul- plants and energy generation . Ramanathan and Ganesh tiple criteria evaluation on nine types of power plants using used a goal programming model to ideally criticize seven hydrogen and natural gas as a fuel with AHP method under energy resources used for lighting at twelve targets-driven seven criteria. In this study, combined cycle plant with natural houses representing energy, economic and environmental gas was determined as a better alternative . While choos- systems . By considering ﬁnancial and proﬁt risks, Han ing the best alternative among renewable power plants in et al. offered a multi-purpose optimization model for CO Turkey, Kahraman et al. used AHP method. At the end of the minimization infrastructure design and sustainable energy study, wind power plant was the best alternative for renew- generation . Chang recommended a multiple choice goal able energy . Liu et al. evaluated the activities of thermal programming model to deal with the problem of capacity power plants between the years of 2004 and 2006 with respect enhancement plan at the renewable energy sector . Jayara- to increasing electrical demands in Taiwan. According to man et al. offered a goal programming model integrating the results obtained, combined cycle plants were determined plentiful allotments of labor resources to reach sustainable, as the most effective plants . Shen et al. applied AHP economic, energy and environmental goals of United Arab Emirates . method for determining economically and environmentally the energy value of investment selection for renewable energy resources within the frame of Taiwan Government’s sustain- able energy policies . Kahraman and Kaya used fuzzy Multiple criteria decision making AHP method and VIKOR method to determine the choice of best alternative power plants for Istanbul . Wang et Multiple criteria decision making (MCDM), with the briefest al. applied AHP method for energy resources selection in deﬁnition, is a general name given for problem-solving China . Lee et al. used fuzzy AHP in making a strategic of multiple and conﬂicted criteria to be achieved. MCDM route map of energy technologies to use alternative energy explains a top-concept including designed techniques and technologies against high oil prices . Çebi et al. used methods to help people facing the problems being character- fuzzy AHP and axiomatic design techniques for choosing ized by different size of criteria, and multiple and conﬂicted the best alternative for investment and comparing renew- criteria . The methods of MCDM are divided into two as able energy resources . Jeberaj and Iniyan applied fuzzy multiple criteria and multi-objective. Multi-objective deci- AHP and AHP method for the choice of sustainable energy sion making is a model deﬁned by the alternatives as a . Liang et al. used the general fuzzy theory for energy mathematic model. Multiple criteria decision making is an planning, supply demand, assumption and renewable energy evaluation process using many criteria taking generally the modeling. In the article, especially on the basis of the current value of different criteria and weighted, conﬂicted and even situation, they improved a model for evaluating manufactur- qualitative values on the purpose of eliminating, prioritiz- ing projects about the usage of Analytic Hierarchy Process ing, classifying, sorting and selecting a ﬁnite number of the (AHP) and fuzzy evaluation. Later, while choosing manufac- options. turing projects in China, a sample study for the evaluation of optimum manufacturing project showing the activity of the Goal programming (GP) model was presented . Anane et al. explained an inno- vator method to range the proper lands for watering using Goal programming was extended by Ijiri in the middle of fuzzy AHP based on geographical information systems in 1960s, but it was improved by Ignizio and Lee in 1970s the basin of TWW Nabeul-Hammamet (Tunisia) chosen as a [8,9]. With a different viewpoint from linear programming, target region . Nixon et al. evaluated the main current col- goal programming, instead of minimizing or maximizing an lection of thermal solar energy technologies within the frame objective function, minimizes the deviations from the targets of AHP. These technologies were compared with techni- determined within the frame of current limitations. These cal, economic and environmental criteria . Jinturkar and deviations are shown as a negative deviation and a positive Deshmukh. Improved a fuzzy mixed-integer goal program- deviation. Since the ratio of these variables’ objective func- ming method for rural sintering and heating energy planning tions is 0, they cannot affect the results. Namely, the purpose in the center of India . Daim et al., improved a fuzzy of the problem in goal programming is to minimize the sum of goal programming in the state of Oregon to make a renew- variables showing the deviation . In addition to this, goal able portfolio giving an answer to 25% of energy demand programming combines multiple goals conﬂicting with deci- obtained by renewable resources in 2025 . San Cristo- sion maker’s options. The targets determined in the results bal applied a goal programming method for layout of ﬁve may not be reached, so even if there are no optimal results, different renewable power plants in the region of Cantabria the most acceptable ones can be obtained. In our article, a 123 Complex & Intelligent Systems Table 1 Linguistic variables for weight/importance of decision makers Suppose that A , d (x ) is a triangle fuzzy number having and different goals L R membership function d and d are the right and left mem- ˜ ˜ A A Linguistic variables Triangle Fuzzy numbers bership functions of the triangle fuzzy number. At last, the right and left integral values of A are deﬁned as below. Very low (VL) (0%, 5%, 10%) Low (L) (5%, 10%, 25%) 1 1 Medium–low (ML) (20%, 32.5%, 45%) L R ˜ ˜ Medium (M) (40%, 50%, 60%) I ( A) = g (y) dy and I A = g (y) dy (3.2) L R ˜ ˜ A A Medium–high (MH) (55%, 67.5%, 80%) 0 0 High (H) (75%, 85%, 95%) L R Very high (VH) (90%, 100%, 100%) g (y)ve g (y), respectively, shows inverse functions of ˜ ˜ A A L R d ve d . These inverse functions are formulated like the ˜ ˜ A A preemptive goal programming model which is a variety of following equation. goal programming was used. L R Preemptive GP formulation: g (y) = a (b − a) y and g (y) = c + (b − c) y (3.3) ˜ ˜ A A + + + − While y ∈ [0, 1], a ∈ [0, 1] as an optimistic index, the Min Z = W δ + W δ ˙ ˙ ˙ ˙ I I I I i =1 total integral value of A is calculated below. s.t . + − f (x ) + δ − δ = g , i = 1 ··· p; i i a ˙ ˙ ˜ ˜ ˜ I I I A = aI A + (1 − a) I A R L x ∈ D; + − = [a b + c + 1 − a a + b ] δ ,δ ≥ 0, i = 1 ··· p ( ) ( )( ) ˙ ˙ I I − + = [ac + b + (1 − a) a] (3.4) W and W are priority values being associated with nega- ˙ ˙ I I 2 tive and positive deviations. The numerical value of the target While a = 0, the total integral value represents optimistic achieved by decision maker is showed with g .The num- decision maker and it is calculated in the following equation. ber of the targets determined by decision maker is showed with p. While being done an order of priority of a hierarchi- cal structure by decision makers, Liou and Wang’s Sorting ˜ I A = [b + a] (3.5) Approach determining to reach different goals for fuzzy num- bers was used here . Linguistic variables are given for Total integral value of a = 0.5 represents moderate decision weight/importance of decision makers and different goals in maker and it is calculated in the following equation. Table 1. As showed in Table, these linguistic variables are charac- 0.5 I A = [0.5c + b + 0.5a] (3.6) terized by triangle fuzzy numbers. In the method of Liou and Wang’s total integral value, a ∈ [0, 1] as an optimism index; Total integral value of a = 0.5 represents optimistic decision for fuzzy numbers given as A = (a, b, c), total integral maker and it is calculated in the following equation. value is calculated in this way . I A = [c + b] (3.7) ˜ ˜ ˜ T I A = aI A + (1 − a) I A R L 1 1 I A = a ith for decision maker and j. for fuzzy goal are R L ik = a g (y) dy + (1 − a) g (y) ˜ ˜ A A the performances . 0 0 Analytic Hierarchy Process (AHP) = a [c + (b − c) y] dy AHP is a decision-making technique determining the order of importance by ﬁnding the priorities according to each other’s criteria and making paired comparisons with objective and + (1 − a) [a + (b − a) y] dy subjective criteria . In these paired comparisons, it is pre- ferred in terms of which one of them is more important than = [a.c + b + (1 − a) a (3.1) the other. By determining them, it is based on a numerical 123 Complex & Intelligent Systems Table 2 Comparison matrix 1st Decision maker 2nd Decision maker 3rd Decision maker according to the weights given by decision makers Installed capacity increased MH M L Energy generation increased H VH M Power plants increased M H ML Power consumption increased VH H L CO emission decreased M VH VH Table 3 Fuzzy evaluation 1st Decision maker 2nd Decision 3rd Decision maker matrix for decision makers (moderate) (a) maker (optimistic) (b) (pessimistic) (c) Installed capacity 0.55,67.5,80 40,50,60 5,15,25 increased Energy generation 75,85,95 90,100,100 40,50,60 increased Power plants 40,50,60 75,85,95 20,32.5,45 increased Power consumption 90,100,100 75,85,95 5,15,25 increased Installed capacity 0.55,67.5,80 40,50,60 5,15,25 increased Energy generation 75,85,95 90,100,100 40,50,60 increased Power plants 40,50,60 75,85,95 20,32.5,45 increased Power consumption 90,100,100 75,85,95 5,15,25 increased CO emission 40,50,60 90,100,100 90,100,100 decreased evaluation of them. AHP enables to make an order between Table 4 Fuzzy numbers used in criteria comparisons options as well as determining the best option for a person Linguistic variable Fuzzy values Reciprocal values who is about to make a decision. For the reason that this Equally important (1, 1, 1) (1/1, 1/1, 1/1/) method which considers both quantitative and qualitative fac- Weakly important (1, 3, 5) (1/5, 1/3, 1) tors is used widely and is applied simply, it is applied easily Essentially important (3, 5, 7) (1/7, 1/5, 1/3) even in the most complicated problems. In that being widely Very strongly important (5, 7, 9) (1/9, 1/7, 1/5) and ﬂexible, AHP makes it a great convenient . Absolutely important (7, 9, 11) (1/11, 1/9, 1/7) Fuzzy Analytic Hierarchy Process (Fuzzy AHP) An enhanced fuzzy AHP method suggested by Chang was will be made using fuzzy triangle numbers in Table 1. These used in many problems, which fuzzy AHP was used. In this fuzzy numbers were developed to be based on Saaty’s 1–9 method, the cutting levels of “a” were not necessary. Besides importance scale by Prakash [25,27] (Tables 2, 3, 4). using the values of artiﬁcial ratings, this method comes to The Algorithm of Chang’s fuzzy AHP where the disadvan- the forefront with simple level sequencing and integrated tages of traditional fuzzy AHP methods are not valid is used sequencing. The most advantageous side of this method is and calculations are made with the techniques of intersec- that calculation requirement is low and it does not need any tions of fuzzy numbers. additional process by following the steps of classical AHP. X composes the object cluster and G composes a target The disadvantage of it is that it only uses fuzzy triangle cluster. According to Chang’s enlarged analysis method, g numbers . Pairwise comparisons matrices are arranged values were composed for each target. Thus, enlarged values to determine the weights of criteria and these comparisons of m’s enlarged analysis for each object are below. 123 Complex & Intelligent Systems 1 2 m M , M ,..., M , i = 1, 2,..., n (3.8) gi gi gi All values of M ( j = 1, 2,... m) given here are fuzzy num- gi bers. The steps of Chang’s enlarged analysis method are below; Step 1 The value of fuzzy artiﬁcial size is deﬁned according to the object i. −1 m n m j j S = M ⊗ M (3.9) gi gi j =1 i =1 j =1 m j To obtain M , we carry on the addition on fuzzy num- j =1 gi bers on m values for a determined matrix; Fig. 1 M and M the intersection of triangle fuzzy numbers  1 2 m m m m M = l , m , u (3.10) j j j gi j =1 j =1 j =1 l Step 3 That the degree of probability of a convex fuzzy −1 n m number is bigger than the convex number of M (I = 1, 2, To obtain M , Fuzzy additions are made I j =1 j =1 gi ..., k) is deﬁned below. on the values of M ( j = 1, 2,..., m) gi V (M ≥ M , M ,..., M ) 1 2 k n m n n n M = l , m , u i i i = V [(M ≥ M ) and (M ≥ M ) and ... and = (M ≥ M )] gi 1 2 k I=1 j =1 i =1 i =1 l = min V (M ≥ M ) , i = 1, 2, 3,..., k (3.15) (3.11) For k = 1, 2,..., n; k = i, calculated as d , the weighting And vector’s reverse in the equation is calculated below. vector is obtained below. −1 n m 1 1 1 M = , , T gi n n n I=1 j =1 W = d ( A ) , d ( A ) ,..., d ( A ) (3.16) u m l 1 2 n i i i i =1 i =1 i =1 (3.12) Here A i = 1, 2,..., n consists of the members. ( ) Step 4 When normalizing the weighting vector given Step 2 M = l , m , u and M = l , m , u are two ( ) ( ) 1 1 1 1 2 2 2 2 above, triangle fuzzy numbers. The degree of probability is deﬁned below; W = (d ( A ) , d ( A ) ,..., d ( A )) (3.17) 1 2 n sup V (M ≥ M ) = min μ (x ) ,μ (y) (3.13) 2 1 M M 1 2 y ≥ x The vector above is obtained. Now, this W weighting vector is not a fuzzy number . And expressed below. V (M ≥ M ) = hgt(M ∩ M ) = μ (d) 2 1 1 2 M Application 1 M ≥ M ⎨ 2 1 0 l ≥ u = 1 2 (3.14) The Preemptive Goal Programming application l −u 1 2 diger ˘ (m −u )−(m −l ) 2 2 1 1 In our study, seven power plants were analyzed; S as a coal V (M ≥ M ) , M = (l , m , u ) and M = (l , m , u ) plant, S as a natural gas combined cycle power plant, S 1 2 1 1 1 1 2 2 2 2 2 3 are the ordinates of junction points of triangle fuzzy num- as a hydropower plant, S as wind plant, S as a geother- 4 5 bers. In other words, these are the values of membership mal plant, S as a solar plant and S as a biomass plant 6 7 the function. To compare M and M , the values of both were deﬁned. Turkey’s installed capacity, energy generation, 1 2 V (M ≥ M ) andV (M ≥ M ) are required to be found. energy demand and greenhouse gas emission deﬁned as its 1 2 2 1 The intersection of triangle fuzzy numbers is given in sectorial indicators were divided into the numbers of power Fig. 1 plants and the obtained values are given in Table 5 . 123 Complex & Intelligent Systems Table 5 Energy indicators Power plants The number of current Installed capacity/the Energy generation/ Energy demand/the Greenhouse gas emission/ power plants number of current the number of current number of current the number of current power plants power plants power plants power plants Coal 38 0.423 2282.6 1.2 1.4 Natural gas 213 0.109 672.6 0.109 0.419 Hydro 562 0.047 124.7 0.106 0.0026 Wind 130 0.035 93.4 0.010 0.00066 Geothermal 22 0.029 158 0.0025 0.004 Solar 113 0.0036 5.28 0.067 – Biomass 68 0.0049 23.69 0.012 0.00058 It was calculated using (3.4), (3.5) and (3.6)’s priority Table 6 Lindo outputs values of goals and fuzzy evaluation matrix in Table 4 below. Variable Value Variable Value 1 1 x 71,00000 d 0,000000 1 11 [0.5c + b + 0.5a] + [c + b] + [b + a] x 340,0000 d 7,088600 2 2 2 12 x 766,0000 d 0,000000 3 21 Goal Programming Model: x 572,0000 d 133184,0 4 22 x 22,00000 d 47,92500 Objective Function; 5 31 Min x 834,0000 d 0,000000 6 32 x 68,00000 d 0,000000 7 41 0, 441d + 0, 441d + 0, 766d + 0, 766d d 525,0000 11 12 21 22 42 d 0,000000 +0, 523d + 0, 523d + 0, 875d 31 32 41 d 4,356560 +0, 875d + 0, 816d + 0, 816d 42 51 52 Constraints; x ≥ 130 x ≥ 22 0, 423x + 0, 109x + 0, 047x + 0, 035x 1 2 3 4 x ≥ 113 +0, 029x + 0, 0036x + 0, 0049x + d − d = 120 5 6 7 11 12 x ≥ 68 2282, 6x + 672, 6x + 124, 7x + 93, 4x 1 2 3 4 x ≥ 0 +158x + 5, 28x + 23, 69x + d − d = 416000 5 6 7 21 22 d ≥ 0 1, 2x + 0, 109x + 0, 106x + 0, 010x 1 2 3 4 +0, 0025x + 0, 067x + 0, 012x + d − d = 218 5 6 7 31 32 Priority values obtained were used in minimization row. x + x + x + x + x + x + x + d − d = 2148 1 2 3 4 5 6 7 41 42 The values obtained in Table 6 were used in constraints under 1, 4x + 0, 419x + 0, 0026x + 0, 00066x 1 2 3 4 2023 targets and it was solved in Lindo Software. Lindo out- +0, 004x + 0, 00058x + d − d = 240 5 7 51 52 put is shown in Table 6. According to the results, in view of the numbers of the 0, 423x ≥ 30 available power plant, the power plants that are required to 0, 109x ≥ 37 be built must have been the solar power plant with the unit 0, 047x ≥ 36 834 and wind power plant with the unit 572. The ﬁrst goal 0, 035x ≥ 20 shows that the deviation in d stayed under the targeted installed capacity. d , i.e., high deviation value at energy 0, 029x ≥ 0, 6 5 22 generation, is shown that energy need will be increased in the 0, 0036x ≥ 3 long term until 2023. Non-renewable fossil resources cannot x ≥ 38 supply energy demands in the long term. According to 2023 x ≥ 213 targets, the deviation demand in d will stay under 47,92500 x ≥ 562 toe (tone of oil equivalent). The deviation in fourth goal, d 3 42 123 Complex & Intelligent Systems Table 7 The pairwise comparison matrix among the criteria  Criteria ED IC SR PE VP Environmental damage (ED) (1, 1, 1) (1/5, 1/3, 1) (5, 7, 9) (3, 5, 7) (1, 3, 5) Investment cost (IC) (1, 3, 5) (1, 1, 1) (7, 9, 11) (3, 5, 7) (3, 5, 7) Space requirement (SR) (1/9, 1/7, 1/5) (1/11, 1/9, 1/7) (1, 1, 1) (1, 3, 5) (1/5, 1/3, 1) Provided employment (PE) (1/7, 1/5, 1/3) (1/7, 1/5, 1/3) (1/5, 1/3, 1) (1, 1, 1) (3, 5, 7) Visual pollution (VP) (1/5, 1/3, 1) (1/7, 1/5, 1/3) (1, 3, 5) (1/7, 1/5, 1/3) (1, 1, 1) Table 8 The synthesis values Table 9 The membership function values according to pairwise com- Criteria l m u related to criteria parisons of criteria SED 0.117 0.295 0.665 V SED SIC SSR SPE SVP SIC 0.190 0.420 0.890 SED – 0.79 1 1 1 SSR 0.030 0.080 0.210 SIC 1 –1 11 SPE 0.060 0.120 0.280 SSR 0.3 0.06 – 0.79 0.95 SVP 0.030 0.090 0.220 SPE 0.48 0.23 1 – 1 SVP 0.33 0.08 1 0.84 – shows that the numbers of power plants are required to be increased 525 units. Finally, the deviation value in d shows Table 10 The weights of alternatives for each criterion that greenhouse gas emission is deﬁcient almost 4,356560 Criteria ED IC SR PE VP Tone-CO /Gwh and these are needed to be reduced using renewable energy resources. Hydro 0.244 0.311 0.423 0 0 Wind 0.381 0.338 0.017 0.104 0.581 Fuzzy AHP application Geothermal 0.068 0.017 0.309 0.313 0 Solar 0.091 0.196 0.237 0.336 0.419 After the application of project investment, according to Biomass 0.213 0.135 0.012 0.245 0 the model results of available energy alternatives in Turkey, it was seen to make an investment in renewable energy resources. In the second stage, it was analyzed which renew- To calculate the vector in Table 9, the minimum one of able energy resources were made an investment on and it was priority values related to the alternatives from the obtained ordered. The used power plants are below. values is taken. Priority vector was given below. S : Hydro W= (0,06; 0,08; 0,23; 0,79; 1) S :Wind When the result of the calculation of priority vector, the S : Geothermal vector below is obtained. S : Solar W = (0,365; 0,463; 0,027; 0,106; 0,037) S :Biomass After determining the weights belonging to the criteria, taking an expert opinion working at an energy company and To compare these power plants, at ﬁrst, the vectors were verbalization of numeric data of criteria, pairwise compar- determined. Later, the vectors were compared dynamically isons of ﬁve power plants were made. with a point scoring system obtained with an expert opinion After all calculations, by making a matrix applied by mul- working at department of energy. Here below, it was given tiplication the criteria weights and alternatives weights, and the statement of fuzzy values of the pairwise comparisons total superiority weights of alternatives are calculated. In matrix. The pairwise comparison matrix among the criteria Table 10, the weights of alternatives were shown according is given in Table 7. to each criterion. Main criteria weights are given in Table According to the matrix above, the synthesis value of each 11. Total superiority weights of alternatives are given in criterion was calculated. The Eq. (3.9) was used (Table 8). Table 12. Using the values obtained, the comparisons of fuzzy val- After determining evaluation results of alternatives under ues were made with Eq. (3.14) and the values below were the criteria 5, by making a multiplication value related to found. each criterion with the obtained values, the weights were 123 Complex & Intelligent Systems Table 11 Criteria weights according to pairwise comparison matrix country’s geopolitical position, inter-countries treaties, and ﬁnancial status are considered for the decision of investment. Criteria Weights 0.365 0.463 0.027 0.106 0.037 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm Table 12 Total superiority weights of alternatives ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit Total superiority weights of alternatives to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Hydro 0.244 Wind 0.327 Geothermal 0.072 Solar 0.179 References Biomass 0.167 1. Anane M, Bouziri L, Limam A, Jellali S (2012) Ranking suitable sites for irrigation with reclaimed water in the Nabeul-Hammamet region (Tunisia) using GIS and AHS-multicriteria decision analy- calculated. So, total superiority weights were calculated. sis. Resour Conserv Recycl 65:36–46 2. 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Complex & Intelligent Systems – Springer Journals
Published: Jun 4, 2018
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