CoPs-Centered Knowledge Management

Rajiv Khosla is an Associate Professor at School of Business, La Trobe University. He is the director of externally funded Business Intelligence Institute-Business Systems and Knowledge Modelling research laboratory. Rajiv has a multi-disciplinary background in management, engineering and computer science. He has published over 120 refereed journal and conference papers. He has also authored four books (research monographs) in the area of Emotional Intelligence, Human-Centred e-Business, Multimedia based Socio-technical Information systems, Intelligent Hybrid Multi-agent Systems. Rajiv is the Associate editor of the International Journal of Pattern Recognition, Regional editor of Journal of Intelligent Manufacturing (Springer-verlag), and Action Editor of Journal of Cognitive Systems Research. He has been a project leader of over a dozen industry projects and has commercialised four IT products in Australia. Associate Professor Rajiv Khosla Business Intelligence Institute and Business Systems Knowledge Modeling Laboratory (http://www.latrobe.edu.au/bskm) School of Business, La Trobe University, Melbourne, Victoria – 3086, Australia E-Mail: [email protected] of the primary reasons identified for the failure of existing knowledge management solutions has been that knowledge management tools and research have primarily been designed around technology push-models as against strategy pull-models. In an era where organizations are undergoing rapid and discontinuous change it is imperative that knowledge management systems and organizational entities like CoPs that facilitate knowledge management and organizational transformation are more closely aligned with business strategies and goals of an organization. This would enable organizations to respond more quickly to changing business environments and corresponding change in their knowledge management needs from time to time. This seminar presents a strategy-pull approach for Modeling and Design of CoPs-centered Knowledge Management Systems to facilitate organizational transformation. Among other aspects the seminar will focus on definition of dimensions and criteria for defining CoPs in an organization, application of fuzzy integral techniques to rank 16 criteria employed by CoPs to engage in knowledge management. From a knowledge management and organizational transformation perspective this approach will enable a more direct relationship between business strategy, CoPs and Knowledge Management solutions.published_or_final_versionCentre for Information Technology in Education, University of Hong Kon

Development of a software application for machine tool reconfiguration using a knowledge-based engineering system approach

The automation processes industry has become increasingly expensive, which is why some small and medium sized enterprises are incapable of buying machine tools with automatic systems. This means that their processes are manual in many cases, and as a result they often have to rework their developed products due to the lack of precision and efficiency in their production processes. Considering that current manufacturing systems with variable machining and turning centers are gradually replacing dedicated systems for medium lot size production, the production systems' basic element, the machine tool, must be capable of working at high speeds with precision, and it must be reconfigurable. These systems must also be compatible and convertible in order to create economic benefits for customers. This article describes a specific software architecture designed to record all the data, information and knowledge concerning manufacturing systems. The software allows for the creation of a new knowledge database and works with it in the reconfiguration of machine tools depending on the rules, requirements and parameters needed to effectively modify production processes or products.Hincapie, M.; Guemes, D.; Contero González, MR.; Ramirez, M.; Diaz, C. (2016). Development of a software application for machine tool reconfiguration using a knowledge-based engineering system approach. International Journal of Knowledge-Based and Intelligent Engineering Systems. 20(1):49-63. doi:10.3233/KES-160334S496320

A bibliometric overview of how critical success factors influence on enterprise resource planning implementations

[EN] This work conducts bibliometric research into publications during the period 1999 to early 2018. The aim of this study is to help gain a better understanding of the publications covering CSF and ERP implementations all over the world. The study includes the most cited articles, most cited authors and most influential institutions as well as the most prolific countries. A database of 301 articles from 86 different institutions and 48 countries has been documented and analyzed. The results indicate that this field is growing significantly over time and a small number of US institutions are currently the most productive in this field.Vicedo Payà, P.; Gil Gómez, H.; Oltra Badenes, RF.; Guerola-Navarro, V. (2020). A bibliometric overview of how critical success factors influence on enterprise resource planning implementations. Journal of Intelligent & Fuzzy Systems. 38(5):5475-5487. https://doi.org/10.3233/JIFS-179639S54755487385Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225. doi:10.1016/s1467-0895(03)00026-5Broadus, R. N. (1987). Toward a definition of «bibliometrics». Scientometrics, 12(5-6), 373-379. doi:10.1007/bf02016680Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. doi:10.1073/pnas.0507655102Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420-433. doi:10.1016/j.asoc.2014.10.035Dereli, T., Durmuşoğlu, A., Delibaş, D., & Avlanmaz, N. (2011). An analysis of the papers published inTotal Quality Management & Business Excellencefrom 1995 through 2008. Total Quality Management & Business Excellence, 22(3), 373-386. doi:10.1080/14783363.2010.532337Petersen, C. G., Aase, G. R., & Heiser, D. R. (2011). Journal ranking analyses of operations management research. International Journal of Operations & Production Management, 31(4), 405-422. doi:10.1108/01443571111119533Maloni, M., Carter, C. R., & Kaufmann, L. (2012). Author affiliation in supply chain management and logistics journals: 2008‐2010. International Journal of Physical Distribution & Logistics Management, 42(1), 83-101. doi:10.1108/09600031211202481Hsieh, P.-N., & Chang, P.-L. (2009). An assessment of world-wide research productivity in production and operations management. International Journal of Production Economics, 120(2), 540-551. doi:10.1016/j.ijpe.2009.03.015Merino, M. T. G., do Carmo, M. L. P., & Álvarez, M. V. S. (2006). 25 Years of Technovation: Characterisation and evolution of the journal. Technovation, 26(12), 1303-1316. doi:10.1016/j.technovation.2005.11.005Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly Influence in the Field of Management: A Bibliometric Analysis of the Determinants of University and Author Impact in the Management Literature in the Past Quarter Century. Journal of Management, 34(4), 641-720. doi:10.1177/0149206308319533Goh, C.-H., Holsapple, C. W., Johnson, L. E., & Tanner, J. R. (1997). Evaluating and classifying POM journals. Journal of Operations Management, 15(2), 123-138. doi:10.1016/s0272-6963(96)00102-7Pilkington, A., & Meredith, J. (2008). The evolution of the intellectual structure of operations management-1980-2006: A citation/co-citation analysis. Journal of Operations Management, 27(3), 185-202. doi:10.1016/j.jom.2008.08.001Stonebraker, J. S., Gil, E., Kirkwood, C. W., & Handfield, R. B. (2011). Impact factor as a metric to assess journals where OM research is published. Journal of Operations Management, 30(1-2), 24-43. doi:10.1016/j.jom.2011.05.002Fagerberg, J., Fosaas, M., & Sapprasert, K. (2012). Innovation: Exploring the knowledge base. Research Policy, 41(7), 1132-1153. doi:10.1016/j.respol.2012.03.008Shiau, W.-L., Dwivedi, Y. K., & Tsai, C.-H. (2015). Supply chain management: exploring the intellectual structure. Scientometrics, 105(1), 215-230. doi:10.1007/s11192-015-1680-9Merigó, J. M., Cancino, C. A., Coronado, F., & Urbano, D. (2016). Academic research in innovation: a country analysis. Scientometrics, 108(2), 559-593. doi:10.1007/s11192-016-1984-4Cancino, C., Merigó, J. M., Coronado, F., Dessouky, Y., & Dessouky, M. (2017). Forty years of Computers & Industrial Engineering: A bibliometric analysis. Computers & Industrial Engineering, 113, 614-629. doi:10.1016/j.cie.2017.08.033Laengle, S., Merigó, J. M., Miranda, J., Słowiński, R., Bomze, I., Borgonovo, E., … Teunter, R. (2017). Forty years of the European Journal of Operational Research: A bibliometric overview. European Journal of Operational Research, 262(3), 803-816. doi:10.1016/j.ejor.2017.04.027Martínez-López, F. J., Merigó, J. M., Valenzuela-Fernández, L., & Nicolás, C. (2018). Fifty years of the European Journal of Marketing: a bibliometric analysis. European Journal of Marketing, 52(1/2), 439-468. doi:10.1108/ejm-11-2017-0853Merigó, J. M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information Sciences: A bibliometric overview. Information Sciences, 432, 245-268. doi:10.1016/j.ins.2017.11.054Merigó, J. M., & Yang, J.-B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37-48. doi:10.1016/j.omega.2016.12.004Tur-Porcar, A., Mas-Tur, A., Merigó, J. M., Roig-Tierno, N., & Watt, J. (2018). A Bibliometric History of the Journal of Psychology Between 1936 and 2015. The Journal of Psychology, 152(4), 199-225. doi:10.1080/00223980.2018.1440516Valenzuela, L. M., Merigó, J. M., Johnston, W. J., Nicolas, C., & Jaramillo, J. F. (2017). Thirty years of the Journal of Business & Industrial Marketing: a bibliometric analysis. Journal of Business & Industrial Marketing, 32(1), 1-17. doi:10.1108/jbim-04-2016-0079Merigó, J. M., Blanco-Mesa, F., Gil-Lafuente, A. M., & Yager, R. R. (2016). Thirty Years of theInternational Journal of Intelligent Systems: A Bibliometric Review. International Journal of Intelligent Systems, 32(5), 526-554. doi:10.1002/int.21859Wang, W., Laengle, S., Merigó, J. M., Yu, D., Herrera-Viedma, E., Cobo, M. J., & Bouchon-Meunier, B. (2018). A Bibliometric Analysis of the First Twenty-Five Years of the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(02), 169-193. doi:10.1142/s0218488518500095Yu D. , Xu Z. , Kao Y. , Lin C.T. , “The Structure and Citation Landscape of IEEE Transactions on Fuzzy Systems (1994–2015)”, IEEE Transactions on Fuzzy Systems 26(2) (2018).Tang, M., Liao, H., & Su, S.-F. (2018). A Bibliometric Overview and Visualization of the International Journal of Fuzzy Systems Between 2007 and 2017. International Journal of Fuzzy Systems, 20(5), 1403-1422. doi:10.1007/s40815-018-0484-5LÓPEZ-HERRERA, A. G., HERRERA-VIEDMA, E., COBO, M. J., MARTÍNEZ, M. A., KOU, G., & SHI, Y. (2012). A CONCEPTUAL SNAPSHOT OF THE FIRST DECADE (2002–2011) OF THE INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING. International Journal of Information Technology & Decision Making, 11(02), 247-270. doi:10.1142/s0219622012400020Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3-13. doi:10.1016/j.knosys.2014.12.035Yu, D., & Shi, S. (2015). Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis. Applied Soft Computing, 32, 189-198. doi:10.1016/j.asoc.2015.03.027Yu, D., Xu, Z., & Wang, W. (2018). Bibliometric analysis of fuzzy theory research in China: A 30-year perspective. Knowledge-Based Systems, 141, 188-199. doi:10.1016/j.knosys.2017.11.018Yu, D. (2015). A scientometrics review on aggregation operator research. Scientometrics, 105(1), 115-133. doi:10.1007/s11192-015-1695-2Zhang, Y., Chen, H., Lu, J., & Zhang, G. (2017). Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133, 255-268. doi:10.1016/j.knosys.2017.07.011Muhuri, P. K., Shukla, A. K., Janmaijaya, M., & Basu, A. (2018). Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016). Applied Soft Computing, 69, 381-392. doi:10.1016/j.asoc.2018.03.041Van Eck, N. J., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. doi:10.1007/s11192-009-0146-

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

A MAS-based infrastructure for negotiation and its application to a water-right market

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-013-9443-8This paper presents a MAS-based infrastructure for the specification of a negotiation framework that handles multiple negotiation protocols in a coherent and flexible way. Although it may be used to implement one single type of agreement mechanism, it has been designed in such a way that multiple mechanisms may be available at any given time, to be activated and tailored on demand (on-line) by participating agents. The framework is also generic enough so that new protocols may be easily added. This infrastructure has been successfully used in a case study to implement a simulation tool as a component of a larger framework based on an electronic market of water rights.This paper was partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation; the MICINN projects TIN2011-27652-C03-01 and TIN2009-13839-C03-01; and the Valencian Prometeo project 2008/051.Alfonso Espinosa, B.; Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers. 16(2):183-199. https://doi.org/10.1007/s10796-013-9443-8S183199162Alberola, J.M., Such, J.M., Espinosa, A., Botti, V., García-Fornes, A. (2008). Magentix: a multiagent platform integrated in linux. In EUMAS (pp. 1–10).Alfonso, B., Vivancos, E., Botti, V., García-Fornes, A. (2011). Integrating jason in a multi-agent platform with support for interaction protocols. 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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). Mathematical programming representation of pull controlled single-product serial manufacturing systems. Journal of Intelligent Manufacturing, 23(1), 23-35. doi:10.1007/s10845-009-0371-xAloulou, M. A., Dolgui, A., & Kovalyov, M. Y. (2013). A bibliography of non-deterministic lot-sizing models. International Journal of Production Research, 52(8), 2293-2310. doi:10.1080/00207543.2013.855336Barba-Gutiérrez, Y., & Adenso-Díaz, B. (2009). Reverse MRP under uncertain and imprecise demand. The International Journal of Advanced Manufacturing Technology, 40(3-4), 413-424. doi:10.1007/s00170-007-1351-yBookbinder, J. H., McAuley, P. T., & Schulte, J. (1989). Inventory and Transportation Planning in the Distribution of Fine Papers. Journal of the Operational Research Society, 40(2), 155-166. doi:10.1057/jors.1989.20Chiang, W. K., & Feng, Y. (2007). The value of information sharing in the presence of supply uncertainty and demand volatility. International Journal of Production Research, 45(6), 1429-1447. doi:10.1080/00207540600634949Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2013). A Modified Approach Based on Ranking Fuzzy Numbers for Fuzzy Integer Programming with Equality Constraints. Annals of Industrial Engineering 2012, 225-233. doi:10.1007/978-1-4471-5349-8_27DOLGUI, A., BEN AMMAR, O., HNAIEN, F., & LOULY, M. A. O. (2013). A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty. Studies in Informatics and Control, 22(3). doi:10.24846/v22i3y201302Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Grabot, B., Geneste, L., Reynoso-Castillo, G., & V�rot, S. (2005). Integration of uncertain and imprecise orders in the MRP method. Journal of Intelligent Manufacturing, 16(2), 215-234. doi:10.1007/s10845-004-5890-xGuillaume, R., Thierry, C., & Grabot, B. (2010). Modelling of ill-known requirements and integration in production planning. Production Planning & Control, 22(4), 336-352. doi:10.1080/09537281003800900Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy Sets and Systems, 47(1), 81-86. doi:10.1016/0165-0114(92)90062-9Hnaien, F., Dolgui, A., & Ould Louly, M.-A. (2008). Planned lead time optimization in material requirement planning environment for multilevel production systems. Journal of Systems Science and Systems Engineering, 17(2), 132-155. doi:10.1007/s11518-008-5072-zHung, Y.-F., & Chang, C.-B. (1999). Determining safety stocks for production planning in uncertain manufacturing. International Journal of Production Economics, 58(2), 199-208. doi:10.1016/s0925-5273(98)00124-8Inderfurth, K. (2009). How to protect against demand and yield risks in MRP systems. International Journal of Production Economics, 121(2), 474-481. doi:10.1016/j.ijpe.2007.02.005JIMÉNEZ, M. (1996). RANKING FUZZY NUMBERS THROUGH THE COMPARISON OF ITS EXPECTED INTERVALS. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(04), 379-388. doi:10.1142/s0218488596000226Jiménez, M., Arenas, M., Bilbao, A., & Rodrı´guez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609. doi:10.1016/j.ejor.2005.10.002Jones, D. (2011). A practical weight sensitivity algorithm for goal and multiple objective programming. European Journal of Operational Research, 213(1), 238-245. doi:10.1016/j.ejor.2011.03.012Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13-21. doi:10.1016/j.ijpe.2010.01.009Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087-2106. doi:10.1016/j.compchemeng.2004.06.006Koh, S. C. L. (2004). MRP-controlled batch-manufacturing environment under uncertainty. Journal of the Operational Research Society, 55(3), 219-232. doi:10.1057/palgrave.jors.2601710Lai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLee, H. L., & Billington, C. (1993). Material Management in Decentralized Supply Chains. Operations Research, 41(5), 835-847. doi:10.1287/opre.41.5.835Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46. doi:10.1080/09537280110061566Li, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Louly, M.-A., & Dolgui, A. (2011). Optimal time phasing and periodicity for MRP with POQ policy. International Journal of Production Economics, 131(1), 76-86. doi:10.1016/j.ijpe.2010.04.042Louly, M. A., Dolgui, A., & Hnaien, F. (2008). Optimal supply planning in MRP environments for assembly systems with random component procurement times. International Journal of Production Research, 46(19), 5441-5467. doi:10.1080/00207540802273827Mohapatra, P., Benyoucef, L., & Tiwari, M. K. (2013). Integration of process planning and scheduling through adaptive setup planning: a multi-objective approach. International Journal of Production Research, 51(23-24), 7190-7208. doi:10.1080/00207543.2013.853890Mula, J., & Díaz-Madroñero, M. (2012). Solution Approaches for Material Requirement Planning* with Fuzzy Costs. Industrial Engineering: Innovative Networks, 349-357. doi:10.1007/978-1-4471-2321-7_39Mula, J., Poler, R., & García, J. P. (2006). Evaluación de Sistemas para la Planificación y Control de la Producción/[title] [title language=en]Evaluation of Production Planning and Control Systems. Información tecnológica, 17(1). doi:10.4067/s0718-07642006000100004Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Mula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271-285. doi:10.1016/j.ijpe.2005.09.001Noori, S., Feylizadeh, M. R., Bagherpour, M., Zorriassatine, F., & Parkin, R. M. (2008). Optimization of material requirement planning by fuzzy multi-objective linear programming. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(7), 887-900. doi:10.1243/09544054jem1014Olhager, J. (2013). Evolution of operations planning and control: from production to supply chains. International Journal of Production Research, 51(23-24), 6836-6843. doi:10.1080/00207543.2012.761363Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research, 50(11), 3011-3020. doi:10.1080/00207543.2011.588267Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80. doi:10.1016/j.ejor.2009.11.031Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPeidro, D., Mula, J., Poler, R., & Verdegay, J.-L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657. doi:10.1016/j.fss.2009.02.021Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. doi:10.1016/s0305-0483(99)00080-8Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Suwanruji, P., & Enns, S. T. (2006). Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies. International Journal of Production Research, 44(21), 4607-4629. doi:10.1080/00207540500494527Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Torabi, S. A., & Moghaddam, M. (2012). Multi-site integrated production-distribution planning with trans-shipment: a fuzzy goal programming approach. 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Assembly line balancing by using axiomatic design principles: An application from cooler manufacturing industry

[EN] The philosophy of production without waste is the fundamental belief behind lean manufacturing that should be adopted by enterprises. One of the waste elimination methods is assembly line balancing for lean manufacturing, i.e. Yamazumi. The assembly line balancing is to assign tasks to the workstations by minimizing the number of workstations to the required values. There should be no workstation with the excessively high or low workload, and all workstations must ideally work with balanced workloads. Accordingly, in this study, the axiomatic design method is applied for assembly line balancing in order to achieve maximum output with the installed capacity. In order to achieve this aim, all improvement opportunities are defined and utilized as an output of the study. Computational results indicate that the proposed method is effective to reduce operators’ idle time by 12%, imbalance workload between workstations by 38%, and the total number of workers by 12%. As a result of these improYilmaz, ÖF.; Demirel, ÖF.; Zaim, S.; Sevim, S. (2020). Assembly line balancing by using axiomatic design principles: An application from cooler manufacturing industry. International Journal of Production Management and Engineering. 8(1):31-43. https://doi.org/10.4995/ijpme.2020.11953OJS314381Ağpak, K , Gökçen, H , Saray, N , Özel, S . (2013). Stokastik Görev Zamanlı Tek Modelli U Tipi Montaj Hattı Dengeleme Problemleri İçin Bir Sezgisel. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi , 17 (4). Retrieved from https://dergipark.org.tr/en/pub/gazimmfd/issue/6654/89311Alcorta, L. (1999). Flexible automation and location of production in developing countries. The European Journal of Development Research, 11(1), 147-175. https://doi.org/10.1080/09578819908426731Babic, B. (1999). Axiomatic design of flexible manufacturing systems. International Journal of Production Research, 37(5), 1159-1173. https://doi.org/10.1080/002075499191454Black, J. T., Schroer, B. J. (1988). Decouplers in integrated cellular manufacturing systems. Journal of Engineering for Industry, 110(1), 77-85. https://doi.org/10.1115/1.3187846Cakir, B. (2006). A simulation Annealing Algoirthm for Stochastic Process Time based Assembly Line Balancing, M.S. Thesis, Gazi University.Celek, O. E., Yurdakul, M., Ic, T. (2019). Axiomatic Design of a Reconfigurable Assembly System for Aircraft Fuselages (No. 2019-01-1359). SAE Technical Paper. https://doi.org/10.4271/2019-01-1359Cevikcan, E., Durmusoglu, M. B. (2011). Minimising utility work and utility worker transfers for a mixed-model assembly line. International Journal of Production Research, 49(24), 7293-7314. https://doi.org/10.1080/00207543.2010.537385Chen, S. J. G., Chen, L. C., Lin, L. (2001). Knowledge-based support for simulation analysis of manufacturing cells. Computers in Industry, 44(1), 33-49. https://doi.org/10.1016/S0166-3615(00)00071-3Chakraborty, K., Mondal, S., Mukherjee, K. (2017). Analysis of product design characteristics for remanufacturing using Fuzzy AHP and Axiomatic Design. Journal of Engineering Design, 28(5), 338-368. https://doi.org/10.1080/09544828.2017.1316014Cochran, D. S., Eversheim, W., Kubin, G., Sesterhenn, M. L. (2000). The application of axiomatic design and lean management principles in the scope of production system segmentation. International Journal of Production Research,38(6), 1377-1396. https://doi.org/10.1080/002075400188906Dolgui, A., Ihnatsenka, I. (2009). Branch and bound algorithm for a transfer line design problem: Stations with sequentially activated multi-spindle heads.European Journal of Operational Research, 197(3), 1119-1132. https://doi.org/10.1016/j.ejor.2008.03.028Durmusoglu, M. B., Satoglu, S. I. (2011). Axiomatic design of hybrid manufacturing systems in erratic demand conditions. International Journal of Production Research, 49(17), 5231-5261. https://doi.org/10.1080/00207543.2010.510487Ertay, T., Satoğlu, S. I. (2012). System parameter selection with information axiom for the new product introduction to the hybrid manufacturing systems under dual-resource constraint. International Journal of Production Research, 50(7), 1825-1839. https://doi.org/10.1080/00207543.2011.560205Ghosh, S., Gagnon, R. J. (1989). A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems. The International Journal of Production Research, 27(4), 637-670. https://doi.org/10.1080/00207548908942574Graves, S. C., Lamar, B. W. (1983). An integer programming procedure for assembly system design problems. Operations Research, 31(3), 522-545. https://doi.org/10.1287/opre.31.3.522Gunasekera, J. S., Ali, A. F. (1995). A three-step approach to designing a metal-forming process. JOM, 47(6), 22-25. https://doi.org/10.1007/BF03221198Guschinskaya, O., Dolgui, A., Guschinsky, N., Levin, G. (2008). A heuristic multi-start decomposition approach for optimal design of serial machining lines. European Journal of Operational Research, 189(3), 902-913. https://doi.org/10.1016/j.ejor.2006.03.072Hager, T., Wafik, H., Faouzi, M. (2017). Manufacturing system design based on axiomatic design: Case of assembly line. Journal of Industrial Engineering and Management, 10(1), 111-139. https://doi.org/10.3926/jiem.728Han, W. M., Zhao, J. L., Chen, Y. (2013). A Virtual Cellular Manufacturing System Design Model Based on Axiomatic Design Theory. In Applied Mechanics and Materials (Vol. 271, pp. 1478-1484). Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMM.271-272.1478Holzner, P., Rauch, E., Spena, P. R., Matt, D. T. (2015). Systematic Design of SME Manufacturing and Assembly Systems Based on Axiomatic Design.Procedia CIRP, 34, 81-86. https://doi.org/10.1016/j.procir.2015.07.010Houshmand, M., Jamshidnezhad, B. (2002). Conceptual design of lean production systems through an axiomatic approach. In Proceedings of ICAD2002 Second International Conference on Axiomatic Design.Houshmand, M., Jamshidnezhad, B. (2004). A lean manufacturing roadmap for an automotive body assembly line within axiomatic design framework. International Journal of Engineering Transactions, 17(1), 51-72.Houshmand, M., Jamshidnezhad, B. (2006). An extended model of design process of lean production systems by means of process variables. Robotics and Computer-Integrated Manufacturing, 22(1), 1-16. https://doi.org/10.1016/j.rcim.2005.01.004Khandekar, A. V., Chakraborty, S. (2016). Application of fuzzy axiomatic design principles for selection of non-traditional machining processes. The International Journal of Advanced Manufacturing Technology, 83(1-4), 529-543.Kulak, O., Durmusoglu, M. B., Tufekci, S. (2005). A complete cellular manufacturing system design methodology based on axiomatic design principles. 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Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry. Advances in Production Engineering & Management, 11(3). https://doi.org/10.14743/apem2016.3.22

Towards the development of the framework for inter sensing enterprise architecture

[EN] Inter-enterprise architecture (IEA) is a new concept that seeks to apply the tools and methodologies of enterprise architecture (EA) in a collaborative context, in order to model collaborative organizations in an inclusive manner. According to the main enterprise architectures proposed to this point, an EA should be conformed at least for a framework, a methodology and a modelling language. Sensing enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the Internet. These fields have come into focus recently, and there is not evidence of the use of IEA for modelling a SE, while finding an interesting gap to work on. Thus, this paper proposes an initial framework for inter sensing enterprise architecture (FISEA), which seeks to classify, organize, store and communicate, at the conceptual level, all the elements for inter-sensing enterprise architectures and their relationships, ensuring their consistency and integrity. This FISEA provides a clear idea about the elements and views that create collaborative network and their inter-relationships, based on the support of Future Internet.This work was supported by the European Commission FP7 UNITE Project, through its Secondment Programme and the Universitat Politecnica de Valencia ADENPRO-PJP project (ref. SP20120703).Vargas, A.; Cuenca, L.; Boza, A.; Sacala, I.; Moisescu, M. (2016). Towards the development of the framework for inter sensing enterprise architecture. Journal of Intelligent Manufacturing. 27(1):55-72. https://doi.org/10.1007/s10845-014-0901-zS5572271Adaba, G., Rusu, L., & Mekawy, M. (2010). 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Geometric and harmonic means based priority dispatching rules for single machine scheduling problems

[EN] This work proposes two new prority dispatching rules (PDRs) for solving single machine scheduling problems. These rules are based on the geometric mean (GM) and harmonic mean (HM) of the processing time (PT) and the due date (DD) and they are referred to as GMPD and HMPD respectively. Performance of the proposed PDRs is evaluated on the basis of five measures/criteria i.e. Total Flow Time (TFT), Total Lateness (TL), Number of Late Jobs (TNL), Total Earliness (TE) and Number of Early Parts (TNE). It is found that GMPD performs better than other PDRs in achieving optimal values of multiple performance measures. Further, effect of variation in the weight assigned to PT and DD on the combined performance of TFT and TL is also examined which reveals that for deriving optimal values of TFT and TL, weighted harmonic mean (WHMPD) rule with a weight of 0.105 outperforms other PDRs. 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