Comprehensive environmental impact evaluation for concrete mixing station (CMS) based on improved TOPSIS method

Abstract This study presents an approach based on the improved technique for order preference by similarity to an ideal solution (TOPSIS) to perform comprehensive environmental impact evaluation for concrete mixing station (CMS) and to determine the optimal location of CMS. An evaluation framework consisting of four indicators and 12 sub-indicators from the environmental and sustainable aspects is established. Entropy method is adopted to determine the weights of indicators and sub-indicators. The improved TOPSIS method is employed to rank candidate locations according to the values of closeness coefficient index (CCI). A case study regarding on CMS location selection of a tunnel project is demonstrated the performance of developed approach. Results show that factors, i.e. environmental impact and pollution reduction management, are top two indicators which should be paid more attention. Moreover, the ranking order of candidate location is presented. Sensitivity and comparison analysis indicate that ranking results considering only profitability indicators is opposite that the results in only consumption indicators. The developed approach provides a guideline in MCS management to increase the sustainability of city.

[1]  H. Lyu,et al.  Assessment and management of lake eutrophication: A case study in Lake Erhai, China. , 2021, The Science of the total environment.

[2]  Mostafa Jafari,et al.  Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS , 2012, Expert Syst. Appl..

[3]  Renuka Mahajan,et al.  New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle , 2008, Inf. Sci..

[4]  A. Çagri Tolga,et al.  Portfolio allocation with the TODIM method , 2019, Expert Syst. Appl..

[5]  Lyès Benyoucef,et al.  Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem , 2012, Eng. Appl. Artif. Intell..

[6]  Mohsen Sadeghi-Dastaki,et al.  A fully fuzzy best-worst multi attribute decision making method with triangular fuzzy number: A case study of maintenance assessment in the hospitals , 2020, Appl. Soft Comput..

[7]  Gülçin Büyüközkan,et al.  A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry , 2012, Expert Syst. Appl..

[8]  Guillaume Habert,et al.  Transportation matters – Does it? GIS-based comparative environmental assessment of concrete mixes with cement, fly ash, natural and recycled aggregates , 2018, Resources, Conservation and Recycling.

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  Li Yang,et al.  Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method , 2017 .

[11]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[12]  Mohammad Izadikhah,et al.  An algorithmic method to extend TOPSIS for decision-making problems with interval data , 2006, Appl. Math. Comput..

[13]  Gülfem Isiklar Alptekin,et al.  A hybrid MCDM methodology for ERP selection problem with interacting criteria , 2012, Decis. Support Syst..

[14]  Marek Reformat,et al.  Choquet based TOPSIS and TODIM for dynamic and heterogeneous decision making with criteria interaction , 2017, Inf. Sci..

[15]  Jhareswar Maiti,et al.  Z-number integrated weighted VIKOR technique for hazard prioritization and its application in virtual prototype based EOT crane operations , 2020, Appl. Soft Comput..

[16]  Zhongliang Yue,et al.  TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting , 2014, Inf. Sci..

[17]  M. L. Menendez.  Shannon's entropy in exponential families: Statistical applications , 2000, Appl. Math. Lett..

[18]  S. Shen,et al.  Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels. , 2020, Water research.

[19]  Lucian C. Coroianu,et al.  Nearest interval, triangular and trapezoidal approximation of a fuzzy number preserving ambiguity , 2012, Int. J. Approx. Reason..

[20]  Yixiong Feng,et al.  An integrated approach for multi-objective optimisation and MCDM of energy internet under uncertainty , 2019, Future Gener. Comput. Syst..

[21]  Ali Osman Atahan,et al.  AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles , 2020, Appl. Soft Comput..

[22]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[23]  H. Lyu,et al.  Sustainable development and environmental restoration in Lake Erhai, China , 2020, Journal of Cleaner Production.

[24]  Kittisak Jermsittiparsert,et al.  Modeling and prioritizing dynamic demand response programs in the electricity markets , 2020 .

[25]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[26]  Limao Zhang,et al.  An adaptive decision making method with copula Bayesian network for location selection , 2021, Inf. Sci..

[27]  Fei Ye,et al.  An extended TOPSIS model based on the Possibility theory under fuzzy environment , 2014, Knowl. Based Syst..

[28]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[29]  Ting Zhang,et al.  Optimal location selection for offshore wind-PV-seawater pumped storage power plant using a hybrid MCDM approach: A two-stage framework , 2019, Energy Conversion and Management.

[30]  Pengyu Chen,et al.  Effects of normalization on the entropy-based TOPSIS method , 2019, Expert Syst. Appl..

[31]  Huchang Liao,et al.  A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India , 2019, Journal of Cleaner Production.

[32]  Michael Haist,et al.  Assessment of the sustainability potential of concrete and concrete structures considering their environmental impact, performance and lifetime , 2014 .

[33]  Rob Law,et al.  Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space , 2010, Expert Syst. Appl..

[34]  Farhad Samaie,et al.  Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method , 2020 .

[35]  Rajesh Joshi,et al.  A novel decision-making method using R-Norm concept and VIKOR approach under picture fuzzy environment , 2020, Expert Syst. Appl..

[36]  H. Lyu,et al.  Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen , 2020 .

[37]  Jelena Dragaš,et al.  Environmental assessment of green concretes for structural use , 2017 .

[38]  Gwo-Hshiung Tzeng,et al.  A service selection model for digital music service platforms using a hybrid MCDM approach , 2016, Appl. Soft Comput..

[39]  Sheng-Yi Jiang,et al.  A note on information entropy measures for vague sets and its applications , 2008, Inf. Sci..

[40]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[41]  Anjali Awasthi,et al.  A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning , 2012 .

[42]  Zhaojun Li,et al.  New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method , 2017, Reliab. Eng. Syst. Saf..

[43]  Dan Ding,et al.  A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems , 2016, Appl. Soft Comput..

[44]  H. Lyu,et al.  Data in flood risk assessment of metro systems in a subsiding environment using the interval FAHP–FCA approach , 2019, Data in brief.

[45]  Taehoon Hong,et al.  An integrated assessment of the environmental, human health, and economic impacts based on life cycle assessment: A case study of the concrete and steel sumps , 2019 .

[46]  Ronald R. Yager,et al.  Ranking Fuzzy Numbers Using a-Weighted Valuations , 2000, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[47]  L. Suganthi,et al.  Multi expert and multi criteria evaluation of sectoral investments for sustainable development: An integrated fuzzy AHP, VIKOR / DEA methodology , 2018, Sustainable Cities and Society.

[48]  Davy Janssens,et al.  Improved hierarchical fuzzy TOPSIS for road safety performance evaluation , 2012, Knowl. Based Syst..

[49]  Hossein Nezamabadi-pour,et al.  MFS-MCDM: Multi-label feature selection using multi-criteria decision making , 2020, Knowl. Based Syst..

[50]  Miroslaw J. Skibniewski,et al.  Bayesian-network-based safety risk analysis in construction projects , 2014, Reliab. Eng. Syst. Saf..

[51]  Renato A. Krohling,et al.  IF-TODIM: An intuitionistic fuzzy TODIM to multi-criteria decision making , 2013, Knowl. Based Syst..

[52]  F. Hosseinzadeh Lotfi,et al.  Extension of TOPSIS for decision-making problems with interval data: Interval efficiency , 2009, Math. Comput. Model..

[53]  Ravi Kant,et al.  A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers , 2014, Expert Syst. Appl..

[54]  Gwo-Hshiung Tzeng,et al.  Multicriteria selection for a restaurant location in Taipei , 2002 .

[55]  Xianguo Wu,et al.  Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[56]  Manpreet Singh,et al.  Environmental impact assessment of fly ash and silica fume based geopolymer concrete , 2020 .