Common Fixed Point Theorems on Complete and Weak GG-Complete Fuzzy Metric Spaces

Motivated by Gopal and Vetro [Iranian Journal of Fuzzy Systems, 11(3), 95-107], we introduce a symmetric pair of $\beta$-admissible mappings and obtain common fixed point theorems for such a pair in complete and weak $G$-complete fuzzy metric spaces. In particular, we rectified, generalize and improve the common fixed point theorem obtained by Turkoglu and Sangurlu [Journal of Intelligent & Fuzzy Systems, 26(1), 137-142] for two fuzzy $\psi$-contractive mappings. We include non-trivial examples to exhibit the generality and demonstrate our results.Comment: 19 page

Some geometrical methods for constructing contradiction measures on Atanassov's intuitionistic fuzzy sets

Trillas et al. (1999, Soft computing, 3 (4), 197–199) and Trillas and Cubillo (1999, On non-contradictory input/output couples in Zadeh's CRI proceeding, 28–32) introduced the study of contradiction in the framework of fuzzy logic because of the significance of avoiding contradictory outputs in inference processes. Later, the study of contradiction in the framework of Atanassov's intuitionistic fuzzy sets (A-IFSs) was initiated by Cubillo and Castiñeira (2004, Contradiction in intuitionistic fuzzy sets proceeding, 2180–2186). The axiomatic definition of contradiction measure was stated in Castiñeira and Cubillo (2009, International journal of intelligent systems, 24, 863–888). Likewise, the concept of continuity of these measures was formalized through several axioms. To be precise, they defined continuity when the sets ‘are increasing’, denominated continuity from below, and continuity when the sets ‘are decreasing’, or continuity from above. The aim of this paper is to provide some geometrical construction methods for obtaining contradiction measures in the framework of A-IFSs and to study what continuity properties these measures satisfy. Furthermore, we show the geometrical interpretations motivating the measures

Decision making with Dempster-Shafer belief structure and the OWAWA operator

[EN] A new decision making model that uses the weighted average and the ordered weighted averaging (OWA) operator in the Dempster-Shafer belief structure is presented. Thus, we are able to represent the decision making problem considering objective and subjective information and the attitudinal character of the decision maker. For doing so, we use the ordered weighted averaging ¿ weighted average (OWAWA) operator. It is an aggregation operator that unifies the weighted average and the OWA in the same formulation. This approach is generalized by using quasi-arithmetic means and group decision making techniques. An application of the new approach in a group decision making problem concerning political management of a country is also developed.We would like to thank the anonymous reviewers for valuable comments that have improved the quality of the paper. Support from the Spanish Ministry of Education under project JC2009-00189 , the University of Barcelona (099311) and the European Commission (PIEFGA-2011-300062) is gratefully acknowledgedMerigó, JM.; Engemann, KJ.; Palacios Marqués, D. (2013). Decision making with Dempster-Shafer belief structure and the OWAWA operator. Technological and Economic Development of Economy. 19(sup 1):S100-S118. https://doi.org/10.3846/20294913.2013.869517SS100S11819sup 1Antuchevičienė, J., Zavadskas, E. K., & Zakarevičius, A. (2010). MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ. Technological and Economic Development of Economy, 16(1), 109-125. doi:10.3846/tede.2010.07Brauers, W. K. M., & Zavadskas, E. K. (2010). PROJECT MANAGEMENT BY MULTIMOORA AS AN INSTRUMENT FOR TRANSITION ECONOMIES / PROJEKTŲ VADYBA SU MULTIMOORA KAIP PRIEMONĖ PEREINAMOJO LAIKOTARPIO ŪKIAMS. Technological and Economic Development of Economy, 16(1), 5-24. doi:10.3846/tede.2010.01Dempster, A. P. (1967). Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics, 38(2), 325-339. doi:10.1214/aoms/1177698950ENGEMANN, K. J., MILLER, H. E., & YAGER, R. R. (1996). DECISION MAKING WITH BELIEF STRUCTURES: AN APPLICATION IN RISK MANAGEMENT. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(01), 1-25. doi:10.1142/s0218488596000020ENGEMANN, K. J., FILEV, D. P., & YAGER, R. R. (1996). MODELLING DECISION MAKING USING IMMEDIATE PROBABILITIES. International Journal of General Systems, 24(3), 281-294. doi:10.1080/03081079608945123Engemann, K. J., & Miller, H. E. (2009). Critical infrastructure and smart technology risk modelling using computational intelligence. International Journal of Business Continuity and Risk Management, 1(1), 91. doi:10.1504/ijbcrm.2009.028953Fodor, J., Marichal, J.-L., & Roubens, M. (1995). Characterization of the ordered weighted averaging operators. IEEE Transactions on Fuzzy Systems, 3(2), 236-240. doi:10.1109/91.388176Han, Z., & Liu, P. (2011). A FUZZY MULTI-ATTRIBUTE DECISION-MAKING METHOD UNDER RISK WITH UNKNOWN ATTRIBUTE WEIGHTS / NERAIŠKUSIS MAŽESNĖS RIZIKOS DAUGIATIKSLIS SPRENDIMŲ PRIĖMIMO METODAS SU NEŽINOMAIS PRISKIRIAMAIS REIKŠMINGUMAIS. Technological and Economic Development of Economy, 17(2), 246-258. doi:10.3846/20294913.2011.580575Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). SELECTION OF RATIONAL DISPUTE RESOLUTION METHOD BY APPLYING NEW STEP‐WISE WEIGHT ASSESSMENT RATIO ANALYSIS (SWARA). Journal of Business Economics and Management, 11(2), 243-258. doi:10.3846/jbem.2010.12Liu, P. (2009). MULTI‐ATTRIBUTE DECISION‐MAKING METHOD RESEARCH BASED ON INTERVAL VAGUE SET AND TOPSIS METHOD. Technological and Economic Development of Economy, 15(3), 453-463. doi:10.3846/1392-8619.2009.15.453-463Liu, P. (2011). A weighted aggregation operators multi-attribute group decision-making method based on interval-valued trapezoidal fuzzy numbers. Expert Systems with Applications, 38(1), 1053-1060. doi:10.1016/j.eswa.2010.07.144Merigó, J. M. (2011). A unified model between the weighted average and the induced OWA operator. Expert Systems with Applications, 38(9), 11560-11572. doi:10.1016/j.eswa.2011.03.034Merigó, J. M. (2012). The probabilistic weighted average and its application in multiperson decision making. International Journal of Intelligent Systems, 27(5), 457-476. doi:10.1002/int.21531Merigó, J. M., & Casanovas, M. (2009). Induced aggregation operators in decision making with the Dempster-Shafer belief structure. International Journal of Intelligent Systems, 24(8), 934-954. doi:10.1002/int.20368Merigó, J. M., & Casanovas, M. (2010). The uncertain induced quasi-arithmetic OWA operator. International Journal of Intelligent Systems, 26(1), 1-24. doi:10.1002/int.20444MERIGÓ, J. M., & CASANOVAS, M. (2011). THE UNCERTAIN GENERALIZED OWA OPERATOR AND ITS APPLICATION TO FINANCIAL DECISION MAKING. International Journal of Information Technology & Decision Making, 10(02), 211-230. doi:10.1142/s0219622011004300MERIGÓ, J. M., CASANOVAS, M., & MARTÍNEZ, L. (2010). LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18(03), 287-304. doi:10.1142/s0218488510006544MERIGO, J., & GILLAFUENTE, A. (2009). The induced generalized OWA operator. Information Sciences, 179(6), 729-741. doi:10.1016/j.ins.2008.11.013Merigó, J. M., & Gil-Lafuente, A. M. (2010). New decision-making techniques and their application in the selection of financial products. Information Sciences, 180(11), 2085-2094. doi:10.1016/j.ins.2010.01.028Merigó, J. M., & Wei, G. (2011). PROBABILISTIC AGGREGATION OPERATORS AND THEIR APPLICATION IN UNCERTAIN MULTI-PERSON DECISION-MAKING / TIKIMYBINIAI SUMAVIMO OPERATORIAI IR JŲ TAIKYMAS PRIIMANT GRUPINIUS SPRENDIMUS NEAPIBRĖŽTOJE APLINKOJE. Technological and Economic Development of Economy, 17(2), 335-351. doi:10.3846/20294913.2011.584961Podvezko, V. (2009). Application of AHP technique. Journal of Business Economics and Management, 10(2), 181-189. doi:10.3846/1611-1699.2009.10.181-189Reformat, M., & Yager, R. R. (2007). Building ensemble classifiers using belief functions and OWA operators. Soft Computing, 12(6), 543-558. doi:10.1007/s00500-007-0227-2Srivastava, R. P., & Mock, T. J. (Eds.). (2002). Belief Functions in Business Decisions. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-7908-1798-0Torra, V. (1997). The weighted OWA operator. International Journal of Intelligent Systems, 12(2), 153-166. doi:10.1002/(sici)1098-111x(199702)12:23.0.co;2-pWei, G.-W. (2011). Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making. Computers & Industrial Engineering, 61(1), 32-38. doi:10.1016/j.cie.2011.02.007Wei, G., Zhao, X., & Lin, R. (2010). Some Induced Aggregating Operators with Fuzzy Number Intuitionistic Fuzzy Information and their Applications to Group Decision Making. International Journal of Computational Intelligence Systems, 3(1), 84-95. doi:10.1080/18756891.2010.9727679Xu, Z. (2005). An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 20(8), 843-865. doi:10.1002/int.20097Xu, Z. (2009). A Deviation-Based Approach to Intuitionistic Fuzzy Multiple Attribute Group Decision Making. Group Decision and Negotiation, 19(1), 57-76. doi:10.1007/s10726-009-9164-zXu, Z. S., & Da, Q. L. (2003). An overview of operators for aggregating information. International Journal of Intelligent Systems, 18(9), 953-969. doi:10.1002/int.10127Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183-190. doi:10.1109/21.87068YAGER, R. R. (1992). DECISION MAKING UNDER DEMPSTER-SHAFER UNCERTAINTIES. International Journal of General Systems, 20(3), 233-245. doi:10.1080/03081079208945033Yager, R. R. (1993). Families of OWA operators. Fuzzy Sets and Systems, 59(2), 125-148. doi:10.1016/0165-0114(93)90194-mYager, R. R. (1998). Including importances in OWA aggregations using fuzzy systems modeling. IEEE Transactions on Fuzzy Systems, 6(2), 286-294. doi:10.1109/91.669028Yager, R. R. (2004). Generalized OWA Aggregation Operators. Fuzzy Optimization and Decision Making, 3(1), 93-107. doi:10.1023/b:fodm.0000013074.68765.97Yager, R. R., Engemann, K. J., & Filev, D. P. (1995). On the concept of immediate probabilities. International Journal of Intelligent Systems, 10(4), 373-397. doi:10.1002/int.4550100403Yager, R. R., & Kacprzyk, J. (Eds.). (1997). The Ordered Weighted Averaging Operators. doi:10.1007/978-1-4615-6123-1Yager, R. R., Kacprzyk, J., & Beliakov, G. (Eds.). (2011). Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-642-17910-5Yager, R. R., & Liu, L. (Eds.). (2008). Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-540-44792-4Zavadskas, E. K., & Turskis, Z. (2011). MULTIPLE CRITERIA DECISION MAKING (MCDM) METHODS IN ECONOMICS: AN OVERVIEW / DAUGIATIKSLIAI SPRENDIMŲ PRIĖMIMO METODAI EKONOMIKOJE: APŽVALGA. Technological and Economic Development of Economy, 17(2), 397-427. doi:10.3846/20294913.2011.593291Zavadskas, E. K., Vilutienė, T., Turskis, Z., & Tamosaitienė, J. (2010). CONTRACTOR SELECTION FOR CONSTRUCTION WORKS BY APPLYING SAW‐G AND TOPSIS GREY TECHNIQUES. Journal of Business Economics and Management, 11(1), 34-55. doi:10.3846/jbem.2010.03Zeng, S., & Su, W. (2011). Intuitionistic fuzzy ordered weighted distance operator. Knowledge-Based Systems, 24(8), 1224-1232. doi:10.1016/j.knosys.2011.05.013Zhang, X., & Liu, P. (2010). METHOD FOR AGGREGATING TRIANGULAR FUZZY INTUITIONISTIC FUZZY INFORMATION AND ITS APPLICATION TO DECISION MAKING / NUMANOMŲ NEAPIBRĖŽTŲJŲ AIBIŲ TEORIJA IR JOS TAIKYMAS PRIIMANT SPRENDIMUS. Technological and Economic Development of Economy, 16(2), 280-290. doi:10.3846/tede.2010.18Zhao, H., Xu, Z., Ni, M., & Liu, S. (2010). Generalized aggregation operators for intuitionistic fuzzy sets. International Journal of Intelligent Systems, 25(1), 1-30. doi:10.1002/int.20386Zhou, L.-G., & Chen, H. (2010). Generalized ordered weighted logarithm aggregation operators and their applications to group decision making. International Journal of Intelligent Systems, n/a-n/a. doi:10.1002/int.20419Zhou, L.-G., & Chen, H.-Y. (2011). Continuous generalized OWA operator and its application to decision making. Fuzzy Sets and Systems, 168(1), 18-34. doi:10.1016/j.fss.2010.05.009Zhou, L., & Chen, H. (2012). A generalization of the power aggregation operators for linguistic environment and its application in group decision making. Knowledge-Based Systems, 26, 216-224. doi:10.1016/j.knosys.2011.08.004Zhou, L., Chen, H., & Liu, J. (2011). Generalized Multiple Averaging Operators and their Applications to Group Decision Making. Group Decision and Negotiation, 22(2), 331-358. doi:10.1007/s10726-011-9267-1Zhou, L., Chen, H., & Liu, J. (2012). Generalized power aggregation operators and their applications in group decision making. Computers & Industrial Engineering, 62(4), 989-999. doi:10.1016/j.cie.2011.12.025Zhou, L.-G., Chen, H.-Y., Merigó, J. M., & Gil-Lafuente, A. M. (2012). Uncertain generalized aggregation operators. Expert Systems with Applications, 39(1), 1105-1117. doi:10.1016/j.eswa.2011.07.11

Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.Nuria Ortigosa acknowledges the support of Universidad Polit'ecnica de Valencia under grant FPI-UPV 2008. Samuel Morillas acknowledges the support of Spanish Ministry of Education and Science under grant MTM 2009-12872-C02-01.Ortigosa Araque, N.; Morillas Gómez, S.; Peris Fajarnes, G.; Dunai Dunai, L. (2012). Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 20(2):245-259. doi:10.1142/S0218488512500122S245259202Grosso, E., & Tistarelli, M. (1995). Active/dynamic stereo vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(9), 868-879. doi:10.1109/34.406652Wedel, A., Badino, H., Rabe, C., Loose, H., Franke, U., & Cremers, D. (2009). B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation. IEEE Transactions on Intelligent Transportation Systems, 10(4), 572-583. doi:10.1109/tits.2009.2027223Bloch, I. (2005). Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing, 23(2), 89-110. doi:10.1016/j.imavis.2004.06.013Keller, J. M., & Wang, X. (2000). A Fuzzy Rule-Based Approach to Scene Description Involving Spatial Relationships. Computer Vision and Image Understanding, 80(1), 21-41. doi:10.1006/cviu.2000.0872Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., & López, M. T. (2010). Video sequence motion tracking by fuzzification techniques. Applied Soft Computing, 10(1), 318-331. doi:10.1016/j.asoc.2009.08.002Morillas, S., Gregori, V., & Hervas, A. (2009). Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images. IEEE Transactions on Image Processing, 18(7), 1452-1466. doi:10.1109/tip.2009.2019305Poloni, M., Ulivi, G., & Vendittelli, M. (1995). Fuzzy logic and autonomous vehicles: Experiments in ultrasonic vision. Fuzzy Sets and Systems, 69(1), 15-27. doi:10.1016/0165-0114(94)00237-2Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocaña, M., & Flores, R. (2007). Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles. Journal of Intelligent and Robotic Systems, 48(4), 539-566. doi:10.1007/s10846-006-9125-6McFetridge, L., & Ibrahim, M. Y. (2009). A new methodology of mobile robot navigation: The agoraphilic algorithm. Robotics and Computer-Integrated Manufacturing, 25(3), 545-551. doi:10.1016/j.rcim.2008.01.008Sun, H., & Yang, J. (2001). Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Network and Distributed Processing. doi:10.1117/12.441696Ortigosa, N., Morillas, S., & Peris-Fajarnés, G. (2010). Obstacle-Free Pathway Detection by Means of Depth Maps. Journal of Intelligent & Robotic Systems, 63(1), 115-129. doi:10.1007/s10846-010-9498-4Picton, P. D., & Capp, M. D. (2008). Relaying scene information to the blind via sound using cartoon depth maps. Image and Vision Computing, 26(4), 570-577. doi:10.1016/j.imavis.2007.07.005Zhang, Z. (2000). A flexible new technique for camera calibration. 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Fuzzy Partial Metric Spaces

"This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 01 Dec 2018, available online: https://doi.org/10.1080/03081079.2018.1552687"[EN] In this paper we provide a concept of fuzzy partial metric space (X, P, ¿) as an extension to fuzzy setting in the sense of Kramosil and Michalek, of the concept of partial metric due to Matthews. This extension has been defined using the residuum operator ¿¿ associated to a continuous t-norm ¿ and without any extra condition on ¿. Similarly, it is defined the stronger concept of GV -fuzzy partial metric (fuzzy partial metric in the sense of George and Veeramani). After defining a concept of open ball in (X, P, ¿), a topology TP on X deduced from P is constructed, and it is showed that (X, TP) is a T0-space.Valentin Gregori acknowledges the support of the Ministry of Economy and Competitiveness of Spain under Grant MTM2015-64373-P (MINECO/Feder, UE). Juan Jose Minana acknowledges the partially support of the Ministry of Economy and Competitiveness of Spain under Grant TIN2016-81731-REDT (LODISCO II) and AEI/FEDER, UE funds, by the Programa Operatiu FEDER 2014-2020 de les Illes Balears, by project ref. PROCOE/4/2017 (Direccio General d'Innovacio i Recerca, Govern de les Illes Balears), and by project ROBINS. The latter has received research funding from the European Union framework under GA 779776. This publication reflects only the authors views and the European Union is not liable for any use that may be made of the information contained therein.Gregori Gregori, V.; Miñana, J.; Miravet-Fortuño, D. (2018). Fuzzy Partial Metric Spaces. International Journal of General Systems. https://doi.org/10.1080/03081079.2018.1552687SBukatin, M., Kopperman, R., & Matthews, S. (2014). Some corollaries of the correspondence between partial metrics and multivalued equalities. Fuzzy Sets and Systems, 256, 57-72. doi:10.1016/j.fss.2013.08.016Camarena, J.-G., Gregori, V., Morillas, S., & Sapena, A. (2010). Two-step fuzzy logic-based method for impulse noise detection in colour images. Pattern Recognition Letters, 31(13), 1842-1849. doi:10.1016/j.patrec.2010.01.008Demirci, M. (2012). The order-theoretic duality and relations between partial metrics and local equalities. Fuzzy Sets and Systems, 192, 45-57. doi:10.1016/j.fss.2011.04.014George, A., & Veeramani, P. (1994). On some results in fuzzy metric spaces. Fuzzy Sets and Systems, 64(3), 395-399. doi:10.1016/0165-0114(94)90162-7Grabiec, M. (1988). Fixed points in fuzzy metric spaces. Fuzzy Sets and Systems, 27(3), 385-389. doi:10.1016/0165-0114(88)90064-4Grečova, S., & Morillas, S. (2016). Perceptual similarity between color images using fuzzy metrics. Journal of Visual Communication and Image Representation, 34, 230-235. doi:10.1016/j.jvcir.2015.04.003Gregori, V., Miñana, J.-J., & Morillas, S. (2012). Some questions in fuzzy metric spaces. Fuzzy Sets and Systems, 204, 71-85. doi:10.1016/j.fss.2011.12.008Gregori, V., Morillas, S., & Sapena, A. (2010). On a class of completable fuzzy metric spaces. Fuzzy Sets and Systems, 161(16), 2193-2205. doi:10.1016/j.fss.2010.03.013Gregori, V., & Romaguera, S. (2000). Some properties of fuzzy metric spaces. Fuzzy Sets and Systems, 115(3), 485-489. doi:10.1016/s0165-0114(98)00281-4Gregori, V., & Sapena, A. (2002). On fixed-point theorems in fuzzy metric spaces. Fuzzy Sets and Systems, 125(2), 245-252. doi:10.1016/s0165-0114(00)00088-9Gutiérrez García, J., Rodríguez-López, J., & Romaguera, S. (2018). On fuzzy uniformities induced by a fuzzy metric space. Fuzzy Sets and Systems, 330, 52-78. doi:10.1016/j.fss.2017.05.001Höhle, U., & Klement, E. P. (Eds.). (1995). Non-Classical Logics and their Applications to Fuzzy Subsets. doi:10.1007/978-94-011-0215-5Klement, E. P., Mesiar, R., & Pap, E. (2000). Triangular Norms. Trends in Logic. doi:10.1007/978-94-015-9540-7MATTHEWS, S. G. (1994). Partial Metric Topology. Annals of the New York Academy of Sciences, 728(1 General Topol), 183-197. doi:10.1111/j.1749-6632.1994.tb44144.xMenger, K. (1942). Statistical Metrics. Proceedings of the National Academy of Sciences, 28(12), 535-537. doi:10.1073/pnas.28.12.535Miheţ, D. (2008). Fuzzy -contractive mappings in non-Archimedean fuzzy metric spaces. Fuzzy Sets and Systems, 159(6), 739-744. doi:10.1016/j.fss.2007.07.006Schweizer, B., & Sklar, A. (1960). Statistical metric spaces. Pacific Journal of Mathematics, 10(1), 313-334. doi:10.2140/pjm.1960.10.313Shukla, S., Gopal, D., & Roldán-López-de-Hierro, A.-F. (2016). Some fixed point theorems in 1-M-complete fuzzy metric-like spaces. International Journal of General Systems, 45(7-8), 815-829. doi:10.1080/03081079.2016.1153084Ying, M. (1991). A new approach for fuzzy topology (I). Fuzzy Sets and Systems, 39(3), 303-321. doi:10.1016/0165-0114(91)90100-5Yue, Y. (2015). Separated ▵+-valued equivalences as probabilistic partial metric spaces. Journal of Intelligent & Fuzzy Systems, 28(6), 2715-2724. doi:10.3233/ifs-15154

Intelligent Systems Research in the Construction Industry

YesWith the increasing complexity of problems in the construction industry, researchers are investigating computationally rigorous intelligent systems with the aim of seeking intelligent solutions. The purpose of this paper is therefore to analyse the research published on ‘intelligent systems in the construction industry’ over the past two decades. This is achieved to observe and understand the historical trends and current patterns in the use of different types of intelligent systems and to exhibit potential directions of further research. Thus, to trace the applications of intelligent systems to research in the construction industry, a profiling approach is employed to analyse 514 publications extracted from the Scopus database. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material by examining variables (such as yearly publications, geographic location of each publication, etc.). This has been achieved by synthesising existing publications using 14 keywords2 ‘Intelligent Systems’, ‘Artificial Intelligence’, ‘Expert Systems’, ‘Fuzzy Systems’, ‘Genetic Algorithms’, ‘Knowledge-Based Systems’, ‘Neural Networks’, ‘Context Aware Applications’, ‘Embedded Systems’, ‘Human–Machine Interface’, ‘Sensing and Multiple Sensor Fusion’, ‘Ubiquitous and Physical Computing’, ‘Case-based Reasoning’ and ‘Construction Industry’. The prime contributions of this research are identified by associating (a) yearly publication and geographic location, (b) yearly publication and the type of intelligent systems employed/discussed, (c) geographic location and the type of research methods employed, and (d) geographic location and the types of intelligent systems employed. These contributions provide a comparison between the two decades and offer insights into the trends in using different intelligent systems types in the construction industry. The analysis presented in this paper has identified intelligent systems studies that have contributed to the development and accumulation of intellectual wealth to the intelligent systems area in the construction industry. This research has implications for researchers, journal editors, practitioners, universities and research institutions. Moreover, it is likely to form the basis and motivation for profiling other database resources and specific types of intelligent systems journals in this area

Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

"This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). 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