Columbia International Publishing American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 doi:10.7726/ajbb.2015.1003 Research Article A Fuzzy Analytic Hierarchy Process Model for Selecting the Best Biogas Usage: A Case Study of Tehran Province, Iran Maryam Nosratinia1, Ali Asghar Tofigh2, and Mehrdad Adl3* Received 9 February 2015; published online 25 April 2015 © The author(s) 2015. Published with open access at www.uscip.us Abstract Problems arising from global warming and increasing energy demand all around the world caused emerging alternative energy resources to be inevitable. Biomass is one of renewable energies that can help in decreasing such problems. Biogas as a popular bioenergy carrier can be used in different ways. In this paper, a decision making model based on fuzzy analytic hierarchy process is proposed for choosing best method based on technical, economic, social and environmental criteria in Tehran Province , Iran. The evaluated alternatives include direct burning for heating, conversion to electricity, upgrading to vehicle fuel quality, upgrading to bio-methane for injection into gas grid, and combined cooling, heating and power (CCHP) generation. The evaluation process is performed on experts’ answered questionnaires basis using fuzzy analytic hierarchy procedure. The results indicate that electricity is the best alternative among the proposed options for biogas usage methods in Tehran Province, Iran. Keywords: Biogas; Fuzzy analytic hierarchy process; Multi-criteria decision making 1. Introduction Policy making during energy planning for governmental purposes is important in different aspects and is affected by factors such as climate changing, increasing energy demand and cost of fossil fuels. Meantime, use of renewable energies is a good solution for these challenges and can be beneficial for environment protection and independency from fossil fuels. Since biogas is produced by fermentation of renewable raw materials, its utilization is a promising option to decrease such problems. In addition, due to its methane content, it has similar ______________________________________________________________________________________________________________________________ *Corresponding e-mail: [email protected] 1 Department of Industrial Engineering, Ph.D. student of Materials and Energy Research Center, P.O.box 31787/316, Karaj, Iran 2 Department of Industrial Engineering, Amir Kabir University of Technology, P.O.box15875-4413, Tehran, Iran 3 Department of Energy, Materials and energy Research Center, P.O.box31787/316, Karaj, Iran 28 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 characteristics to natural gas, but in lower extent (Papong et al., 2014). Anaerobic digestion process produces biogas through breakdown of natural organic materials with different industrial, agricultural and rural sources. Developed countries cultivate dedicated crops such as maize, barley, sunflower, Lucerne and sorghum for anaerobic digestion; and usually use different mixture of waste for better methane production rate. (Bauer et al., 2010). Biogas can be used in various ways such as generating power or heat, purifying it into bio-methane and use it as vehicle fuel, injecting to regional gas grid, and using it in CHP systems. Each way has its own advantages and disadvantages and choosing the best method of biogas using can be main factor in biogas success. In this study, we aim at comparing biogas utilization methods by defining relevant criteria and prioritizing them by applying a fuzzy multiple criteria decision-making model to experts' judgments. The investigation zone of this study is Tehran Province in which, the capital of Iran is located and contains around 17% of whole country’s population. A site selection study for the most appropriate locations for large and medium scale biogas plants was conducted using geographical information system (GIS) through which, a final map was concluded that shows the most appropriate zones in dark blue color (Fig. 1). The outcomes of the aforementioned study have been disseminated elsewhere (Nosratinia et al., 2015). Fig. 1. Demonstration of the most suitable locations for biogas plants in Tehran Province 2. Biogas Utilization Biogas technology can effectively decrease the harmful impacts of energy conversion and lead to 29 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 omit the Inappropriate effects of fossil fuels in two ways; first, we can consider it as a renewable source of energy, and second, its efficiency is high (Ou et al., 2009). If biogas is combusted in an on-site engine or CHP system, only a primary cleaning is needed (dehumidification and desulfurization) to remove the corrosive compounds that may damage the engines. The excess heat of the engines can be used for maintaining the fermentation process temperature and for the operator’s own purposes. In principle, it is also possible to feed the heat into a local district heating network, although this is rarely done in small or medium scales. The additional processing and upgrading of biogas into gas grid biomethane for using in allows a decentralized energy valorization: the biomethane can be drawn and used, anywherewithin the regional gas grid. The regulatory framework of each country usually states which purity of biomethane can be injected and the conditions of injection. The grid operators publish their technical specifications on bio-methane quality (Bordelanne et al., 2011). Fig. 2. Biogas usage methods The order of oil consumers globally in different sectors is as follows; transportation sector (51% of total consumption), industrial sector (34% of total consumption) and finally, residential (6% of total consumption) sectors (International Energy Agency, 2010). It should be noted that the energy consumption in transportation sector is increasing with a 20% growth rate since 1990 and has been twice between 1973 and 2006 (International Energy Agency, 2010). Bordelanne et al., (2011) predicted that it can be increased by 30% between 2010 and 2030. CNG (Compressed Natural Gas) is an alternative fuel to the diesel oil and gasoline, allowing a reduction of the dependency on crude oil and a diversification of the sources of fuel supply. This diversification is important not only from an economic point of view, but also in order to secure the energy supplies. For propose of utilizing biogas as a transportation fuel, raw biogas has to undergo 30 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 two major processes: cleaning and upgrading to achieve a quality near to natural gas. The upgraded biogas (so called biomethane) and its compressed form (bio-CNG) are considered as green fuel with respect to environment, climate, and human health. However, the resulting bio-CNG from the processes still needs to be evaluated in terms of greenhouse gas emissions and energy aspects (Bordelanne et al., 2011, Papong et al., 2014). Fig. 2 shows biogas utilization options in brief. 3. Proposed Method 3.1. Multiple Criteria Decision-Making and Analytic Hierarchy Process The general decision making process includes four steps: Defining decision objectives, Determining feasible alternatives, Evaluating the alternatives, Select and implement the best alternative. Multiple criteria decision making (MCDM) models have two major types; multiple-objective decision-making (MODM) models and multiple-criteria decision making (MADM) models. The decision problem that we consider in this paper is to determine the best alternative for biogas utilization. We use a fuzzy analytic hierarchy process model to do this. For selecting the best alternative we should determine criteria, each criterion has its impact and depend on decision makers point of view. Analytic hierarchy process is a popular method for producing subjective weights for the decision objective, criteria, sub criteria, and alternatives. In AHP, we decompose decision problem in to hierarchy of sub problems, with the objective at top, criteria and sub criteria at next levels, and the alternatives at the bottom (Szczypinska and Piotrowski, 2008; Tzeng et al., 2002; Yang and Lee, 1997). 3.2. Fuzzy Analytic Hierarchy Process Evaluation criteria in decision making process could be qualitative or quantitative. When they are stated in linguistic terms, we should convert them to quantitative way. To do this, we can use fuzzy sets theory and fuzzy numbers decision makers’ judgments to numbers. In this paper, we use triangular fuzzy numbers (TFN) defined on [0, 1] range for evaluating alternatives against criteria. We use pairwise comparisons to find the relative importance of the criteria and sub-criteria. In this way, scales proposed by Kahraman et al. (2006) are utilized and are given in Table 1. We use fuzzy analytic hierarchy process (Fuzzy AHP) for determining relative importance of criteria, sub criteria, and alternatives. In literature, there are several types of fuzzy AHP (see for example, Laarhoven and Pedrycz, 1983; Buckley, 1985; Chang, 1996; Leung and Cao, 2000). Here, 31 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 we use Chang (1992, 1996) proposed method. Because steps of it are easier to implement in practical cases than other proposed methods and its results are similar to crisp AHP. Table 1 Fuzzy scales used for pairwise comparisons Relative importance Equal importance Moderate importance Strong importance Very strong importance Extreme importance Fuzzy scale (1, 1, 1) ( 1, 3/2,2) (3/2, 2, 5/2) (2, 5/2, 3) (5/2, 4, 7/2) Reciprocal scale (1, 1, 1) (1/2, 2/3, 1) (2/5, 1/2, 2/3) (1/3, 2/5, 1/2) (2/7, 1/4, 2/5) Here, we describe some of applications of Chang (1992, 1996) proposed method; this method was used by Bozdag et al. (2003) in the evaluation of computer integrated manufacturing alternatives. Kahraman et al. (2003, 2004) used the method for evaluation of the catering firms in Turkey and selection of best facility location. Buyukozkan et al. (2004) utilized this approach for selecting the best software development strategy. Kwong and Bai (2003) and Kahraman et al. (2006) used it to evaluate Quality Function deployment (QFD) for customer requirements. Here, we describe Chang (1992, 1996) method. The triangular fuzzy numbers are defined with boundaries. Assume that we have , and then the membership function is: : R We define [0, 1] of the triangular fuzzy number defined on R is given by on R that is given by Eq. (1): l x m l m l u ~ x M ( x) x m u m u 0 otherwise x [l , m] x [m, u ] (1) where l and m is the median and most possible value of fuzzy number is l and u is the lower and upper limits of . The value of the fuzzy synthetic extent of the ith object is defined as: 1 ~ n m ~ Si M ij M ij j 1 i 1 j 1 m m m m ~ M ij lij , mij , uij j 1 j 1 j 1 j 1 m m m m m ~ M ij lij , mij , uij i 1 j 1 j 1 j 1 j 1 m (2) (3) (4) 32 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 1 n m ~ 1 1 1 , n , n M ij n i 1 j 1 uij mij lij i 1 i 1 i 1 (5) The triangular fuzzy number value of (li, mi, ui) is calculated using Equations (2) to (5). After calculating the values of Si, we can calculate degree of possibility of Sj=(lj, mj, uj) Si=(li, mi, ui) using equation 6: 1 if m j mi V(S j Si ) height ( Si S j ) S j ( d ) 0 if li ui li u j otherwise ( mi ui ) ( mi li ) (6) After calculating both the values of V (Sj ), we should compare Si and Sj. The i) and V ( i minimum degree possibility d (i) of V (Sj Si) (i, j=1, 2, …, k) can be expressed as: V( (7) The normalized local weight vector is expressed as follows: W min V (S1 S k ),V (S 2 S k ),...., V (S 4 S k ) T (8) where k=1, 2,…, k and W is a crisp number. 3.3. Alternatives and Evaluation Criteria We identified five alternative ways to us biogas for producing heats. The alternatives are given in Table 2. Table2 The alternatives Alternative 1 2 3 4 5 Description CCHP Direct Heating Electricity Injection to Gas grid Upgrading Vehicle fuel There are variety of criteria for system evaluation and decision making in energy fields. For instance, Jing et al., (2012), Feng and Jin (2005), and Pilavachi et al., (2006) identified four main criteria for evaluation of a special kind of energy system that are utilized in this paper. 33 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 Pollutant emission reduction and energy saving are the main factors evaluating the benefits of biogas usage. For evaluation of biogas usage, technological and economic criteria should also be taken into consideration. Conclusively, criteria selected in this paper are technology, economy, environment and society. Based on the hierarchical structure of evaluation model, these criteria can be divided into some sub criteria, Table 3 presents these criteria and sub criteria. Table3 Criteria and sub criteria definition (Jing et al., 2012) Criteria Sub criteria Definition Primary energy consumption ratio (A1) The primary energy consumption divided to the users’ demand energy. Its goal is to reduce the amount of energy required to provide products and services Controllability is an important property of a control system Is the technology well developed or not? Is it easy for user to work with it? The cost of the procurement and set up. The length of time required to recover the cost of an investment The total system cost per year Is defined as the sum of the present values (PVs) of incoming and outgoing cash flows over a period of time A key ingredient in smog that can cause acid rain. It can cause greenhouse effect and global warming. The most important greenhouse gas. Noise can bother people and affect their life Is the system or technology advanced? Can be upgraded in the future? Can the system or equipment be maintained easily? Is the system safe for people or not? The system needs the land area or space Energy efficiency(A2) Technology (A) Controllability(A3) Maturity (A4) Regulation property(A5) Investment cost(B1) Investment recovery period(B2) Total annual cost(B3) Economy (B) Net present value(NPV) (B4) NOX emission (C1) CO emission(C2) CO2 emission (C3) Noise (C4) Advanced performance(D1) Maintenance convenience(D2) Safeguards (D3) Footmark (D4) Environment (C) Society (D) 3.4. Results Table 4 Pairwise comparisons of criteria. A B C D A (1,1,1) (2/5,1/2,2/3) (2/5,1/2,2/3) (2/5,1/2,2/3) B (3/2,2,5/2) (1,1,1) (2/5,1/2,2/3) (2/5,1/2,2/3) C (3/2,2,5/2) (3/2,2,5/2) (1,1,1) (2/3,1,2) D (3/2,2,5/2) (3/2,2,5/2) (2/3,1,3/2) (1,1,1) Table 5 Pairwise comparisons of technology sub criteria A1 A2 A3 A4 A5 A1 (1,1,1) (3/2,2,5/2) (1,3/2,2) (2/3,1,2) (1,3/2,2) A2 (2/5,1/2,2/3) (1,1,1) (2/5,1/2,2/3) (1/3,2/5,1/2) (2/5,1/2,2/3) A3 (1/2,2/3,1) (3/2,2,5/2) (1,1,1) (1/2,2/3,1) (1/2,2/3,1) A4 (1/2,1,3/2) (2,5/2,3) (1,3/2,2) (1,1,1) (1,3/2,2) A5 (1/2,2/3,1) (3/2,2,5/2) (1,3/2,2) (1/2,2/3,1) (1,1,1) We used judgments of 10 Iranian biogas experts in order to form pairwise comparisons and extract the weights based on fuzzy AHP method. It should be noticed that the results in decision making strongly depend on decision makers, if the governments wants make decision, in most cases the 34 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 policy makers give more weight to social and environmental criteria by considering their people benefits in long term, but in private sector they usually emphasize on technical and economic aspects. Our decision is based on private sector and we chose our experts from faculty member and industry and pairwise comparisons for criteria and sub criteria according to their opinion are given in table 4 and Tables 5-8 respectively. Table 9 demonstrates the weights of hierarchy elements. Table 6 Pairwise comparisons of economy sub criteria B1 (1,1,1) (2/3,1,3/2) (2/3,1,3/2) (3/2,2,5/2) B1 B2 B3 B4 B2 (2/3,1,3/2) (1,1,1) (2/3,1,3/2) (3/2,2,5/2) B3 (2/3,1,3/2) (2/3,1,3/2) (1,1,1) (3/2,2,5/2) B4 (2/5,1/2,2/3) (2/5,1/2,2/3) (2/5,1/2,2/3) (1,1,1) Table 7 Pairwise comparisons of environment sub criteria C1 (1,1,1) (2/3,1,3/2) (3/2,2,5/2) (3/2,2,5/2) C1 C2 C3 C4 C2 (2/3,1,3/2) (1,1,1) (1,1,1) (2/3,1,3/2) C3 (2/5,1/2,2/3) (1,1,1) (1,1,1) (2/5,1/2,2/3) C4 (2/5,3,7/2) (2/3,1,3/2) (3/2,2,5/2) (1,1,1) D3 (5/2,3,7/2) (2/5,1/2,2/3) (1,1,1) (2/5,1/2,2/3) D4 (3/2,2,5/2) (2/3,1,3/2) (3/2,2,5/2) (1,1,1) Table 8 Pairwise comparisons of society sub criteria D1 D2 D3 D4 D1 (1,1,1) (3/2,2,5/2) (2/7,1/3,2/5) (3/2,2,5/2) D2 (2/5,1/2,2/3) (1,1,1) (3/2,2,5/2) (2/3,1,3/2) Table 9 calculated weights for criteria and sub criteria Factors Weight A 0.535 B 0.385 C 0.040 D 0.040 Sub factors A1 A2 A3 A4 A5 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4 Normalized Weight 0.062 00.44 0.239 0.084 0.175 0.164 0.164 0.164 0.507 0.072 0.216 0.430 0.281 0.420 0.56 0.314 0.032 Total Weight 0.03317 0.2354 0.127865 0.04494 0.093625 0.06314 0.06314 0.06314 0.195195 0.00288 0.00864 0.0172 0.01124 0.0168 0.0224 0.01256 0.00128 35 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 Based on Fuzzy Analytic Hierarchy Process in section 3.2, we calculated criteria's weight and the results show technology criterion has the highest weight than other criteria, then, environment and society have equal importance, and finally, economy has the lowest weight than other criteria. In sub criteria level, energy efficiency, net present value, and controllability play the most important roles in the selection of best biogas usage. On other hand, footmark, CO emission, and NOX emission play the least important roles in the selection of best biogas usage. Considering technology criterion, energy efficiency is the most important sub criteria and Primary energy consumption ratio is the least important sub criteria. Considering economy criterion, net present value is the most important sub criterion and investment cost, investment recovery period, and total annual cost are the least important sub criterion. Considering environment criterion, CO2 emission is the most important sub criterion and NOX emission is the least important sub criterion. Considering society criterion, Maintenance convenience is the most important sub criterion and footmark is the least important sub criterion. In last part we used calculated weights and experts' knowledge for ranking alternative and table 10 presents this ranking. As seen in the table, electricity is the most preferable alternative and vehicle fuel is the least preferable one. This ranking is reasonable according to technology level in Iran and required costs. Although cruel oil can increase environmental problems of Iran, it has not reached to required technology for vehicle fuel. Table 10 Ranking Results for the alternatives Alternative 1 2 Description CCHP Direct heating Rank 3 2 3 Electricity generation 1 4 Upgrading/Injection to gas grid 4 5 Upgrading to vehicle fuel 5 4. Conclusion and Future Research Directions In this paper, we aimed at selecting the best usage of biogas using a fuzzy analytic hierarchy process model. Despite the importance of decision making in renewable energy using methods , this field have been neglected and this study can be a guideline for decision makers in biogas field so a decision making model based on fuzzy analytic hierarchy process is proposed for choosing the best method of biogas usage in Tehran Province , Iran. This model can be adopted in other countries too. In this paper we defined criteria and sub criteria, and then identified five alternatives. Our decision problem consisted of four levels; decision objective, criteria, sub criteria, and alternatives. Criteria included technology, economy, environment and society and alternative included CCHP, heating, electricity, gas grid, and vehicle fuel. In order to select the best alternative, we asked 10 Iranian biogas experts to do pairwise comparisons. We found that electricity generation is the best usage of biogas among other alternatives. This study can be further developed by adding more details related to Iran’s regulations and conditions to it. We also should be noted that current results should have expire date and need to be updated time by time. That’s because by expanding knowledge and passing the time, the importance of criteria can changed, for example for Iran, US sanctions to energy filed may causes to 36 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 change the importance of environment criteria. Updating the decision making process from time to time can help governments in making right policy, especially for Iran that suffers from wrong decision making in renewable energy field. References Bauer, A.,Leonhartsberger,C., Bösch,P.,Amon,B.,Friedl,A. and Amon, T. (2010).Analysis of methane yields from energy crops and agricultural by-products and estimation of energy potential from sustainable crop rotation systems in EU-27.Clean TechnologiesandEnvironmentalPolicy(Vol.12,pp153–161). http://dx.doi.org/10.1007/s10098-009-0236-1 Bordelanne, O., Montero, M., Bravin, F., Prieur-Vernat, A., Oliveti-Selmia, O., Pierrea, Papadopoulob, P.M. and Muller, T. (2011). Bio methane CNG hybrid: A reduction by more than 80% of the greenhouse gases emissions compared to gasoline.Journal of Natural Gas Science and Engineering.( Vol. 3, pp 617-624). http://dx.doi.org/10.1016/j.jngse.2011.07.007 Bozdag, C.E., Kahraman, C. andRuan, D., (2003). Fuzzy group decision making for selection among computer integrated manufacturing systems. Computers in Industry (Vol.51 (1), pp.13–29). http://dx.doi.org/10.1016/S0166-3615(03)00029-0 Buckley, J.J., (1985). Fuzzy hierarchical analysis.Fuzzy Sets and Systems (Vol.17 (3), pp 233–247). http://dx.doi.org/10.1016/0165-0114(85)90090-9 Buyukozkan, G., Kahraman, C., Ruan, D., (2004). A fuzzy multi-criteria decision approach for software development strategy selection.International Journal of General Systems, (Vol.33 (2–3), pp.259–280). http://dx.doi.org/10.1080/03081070310001633581 Chang, D.Y., (1996). Applications of the extent analysis method on fuzzy AHP.European Journal of Operational Research (Vol.95, pp649–655). http://dx.doi.org/10.1016/0377-2217(95)00300-2 Feng, Z.B., Jin, H.G., (2005). Analysis of technology and economics of gas turbine cooling–heating–power system.Engineering for Thermal Energy and Power (vol.20, pp.425). International Energy Agency (2010).Energy Technology Perspectives. Jing,Y.Y., Bai N, H., Wang, J.J.,(2012) .A fuzzy multi-criteria decision-making model for CCHP systems driven by different energy sources. Energy Policy (Vol.42,pp.286–296). http://dx.doi.org/10.1016/j.enpol.2011.11.085 Kahraman, C., Ertay, T., Buyukozkan, G., (2006).A fuzzy optimization model for QFD planning process using analytic network approach.European Journal of Operational Research (Vol.171, pp.390–411). http://dx.doi.org/10.1016/j.ejor.2004.09.016 Kahraman, C., Cebeci, U. and Ulukan Z., (2003) "Multi‐criteria supplier selection using fuzzy AHP",Logistics Information Management, Vol. 16 (6), pp.382 - 394 http://dx.doi.org/10.1108/09576050310503367 Kahraman, C., Cebeci, U. and Ruan, D., (2004) Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, Vol. 87, (2), pp 171-184 http://dx.doi.org/10.1016/S0925-5273(03)00099-9 Kwong, C.K., Bai, H., (2003). Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach.IIE Transactions (Vol.35, pp.619–626). http://dx.doi.org/10.1080/07408170304355 Laarhoven, P.J.M., Pedrycz, W., (1983). A fuzzy extension of Saaty's priority theory.Fuzzy Sets and Systems (Vol.11 (3), pp.229–241). Leung, L.C., Cao, D., (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research (Vol.124, pp.102–113). http://dx.doi.org/10.1016/S0377-2217(99)00118-6 37 Maryam Nosratinia, Ali AsgharTofigh, and Mehrdad Adl / American Journal of Biomass and Bioenergy (2015) Vol. 4 No. 1 pp. 28-38 Ou, X., Zhang, X., Chang, S., Guo, Q., (2009).Energy consumption and GHG emissions of six biofuel pathways by LCA in (the) People's Repulic of China. Applied Energy (vol.86, pp.5197–5208) http://dx.doi.org/10.1016/j.apenergy.2009.04.045 Papong ,S., Rotwiroon ,P., Chatchupong ,T., Malakul ,P.,(2014) .Life cycle energy and environmental assessment of bio-CNG utilization from cassava starch wastewater treatment plants in Thailand .Renewable Energy 65 64e69. http://dx.doi.org/10.1016/j.renene.2013.07.012 Pilavachi, P.A., Roumpeas, C.P., Minett, S., Afgan, N.H., (2006). Multi-criteria evaluation for CHP system options.Energy Conversion and Management (Vol.47, pp.3519–3529. 429). http://dx.doi.org/10.1016/j.enconman.2006.03.004 Szczypinska, A., Piotrowski, E.W., (2008). Projective market model approach to AHP decision making.Physica a-Statistical Mechanics and Its Applications (Vol.387, pp.3982–3986). http://dx.doi.org/10.1016/j.physa.2008.01.053 Tzeng, G.H., Teng, M.H., Chen, J.J., Opricovic, S., (2002). Multi criteria selection for a restaurant location in Taipei.International Journal of Hospitality Management (Vol.21, pp.171–187). http://dx.doi.org/10.1016/S0278-4319(02)00005-1 Yang, J., Lee, H., (1997).An AHP decision model for facility location selection.Facilities (Vol.15, pp.241–254). http://dx.doi.org/10.1108/02632779710178785 38
© Copyright 2024