Group Analytic Hierarchy Process Sorting II Method: an Application to Evaluate the Economic Value of a Wine Regions Landscape

In the ongoing context of climate change, there is an increasing need to support decision-making processes in the domain of landscape planning and management. Suitable evaluation techniques are needed to take into account the interests of actors and stakeholders in shared policy decisions. An important methodological contribution to the field is given by the Multicriteria Decision Analysis (MCDA), due to its ability to combine multiple aspects of a decision problem with the values and opinions expressed by different Decision Makers. The present paper develops the “Group Analytic Hierarchy Process Sorting II method” (GAHPSort II), which aims to sort a group of municipalities included in the UNESCO site “Vineyard Landscape of Piedmont: Langhe-Roero, and Monferrato” (Italy) according to the economic attractiveness of the landscape. Extending the previous versions AHPSort I, AHPSort II and GAHPSort, the GAHPSort II optimizes multi-stakeholder evaluations on large databases by reducing the number of comparisons. Moreover, the GAHPSort II method is proposed as a novel spatial decision support system because it combines a set of economic indicators for landscape and GIS methods for aiding the Decision Makers to better understand the case study and to support the definition and localization of policies and strategies of landscape planning and management

The Ahpsort II To Evaluate The High Level Instruction Performances

This paper aims to propose a model for ranking Italian high schools based on the several performance outputs. In order to analyze the performance of Italian public High Schools we consider the students’ school performance and their academic achievements; also the school characteristics may influence the performance evaluation of high schools, although the importance of these aspects is certainly less than the results achieved by students.Data are from Eduscopio and ScuolaInChiaro portals and refers to the 2019/20 school year. We analyze a sample of 263 high schools (HS) in all Italian Regions. For each school we consider nine outputs related to students' school and academic performance, and school characteristics. We assess the performance of high schools using a multi-criteria approach. Our analysis involves a high number of schools, so we apply the AHPSort II method which in addition to defining the ranking of schools also defines their classification. Our results show that scientific lyceums are all in the first class regardless the geographic area

Closing the gender gap at academic conferences: A tool for monitoring and assessing academic events

The importance of participation in academic conferences is well known for members of the scientific community. It is not only for the feedback and the improvement of the work, it is also about career development, building networks and increasing visibility. Nevertheless, women continue to be under-represented in these academic events and even more so in the most visible positions such as speaking roles. This paper presents the development of a tool based on performance indicators, which will allow monitoring and evaluating gender roles and inequalities in academic conferences in order to tackle the underrepresentation of women. The study identifies relevant perspectives (participation, organizational structure and attitudes) and designs specific lists of performance indicators for each of them. The tool is based on a combination of two multicriteria techniques, Analytic Hierarchy Process and Analytic Hierarchy Process Sort, and a qualitative analysis based on in-depth interviews and information gathered from a focus group. The use of the AHP multi-criteria decision technique has allowed us to weight the indicators according to the opinion of several experts, and with them to be able to generate from these weightings composite indicators for each of the three dimensions. The most relevant indicators were for the participation dimension. Additionally, the tool developed has been applied to an academic conference which has been monitored in real time. The results are shown as a traffic light visualization approach, where red means bad performance, yellow average performance and green good performance, helping us to present the results for each indicator. Finally, proposals for improvement actions addressed to the red indicators are explained. The work carried out highlights the need to broaden the study of gender equality in academic conferences, not only regarding the participation but also the performance of different roles and functions.Grant Number OR2019-60221 Funder: Open Society Foundations Programme: Open Society Initiative for Europe Award: Expanding the Female Talent Pipeline in Europe https://www.opensocietyfoundations.org

Sustainable warehouse evaluation with AHPSort traffic light visualisation and post-optimal analysis method

Sustainable warehousing is essential for organisations to achieve overall supply chain sustainability. Warehousing facilities have the greatest potential for reducing socio-environmental impact. Yet, both research and practice have given relatively less attention to considering all aspects of sustainability in warehouses. In order to address this gap, this study proposes combining both input from professionals and from a literature survey of triple-bottom-line theory in order to develop a sustainable warehouse criteria framework, thus contributing to sustainable organisational warehouse evaluation. The method supporting the evaluation of this framework is based on the integration of a multicriteria AHPSort traffic light visualisation technique and novel post-optimal analysis. Furthermore, the authors deployed this framework and integrated methodology in an Indian manufacturing company to evaluate and classify seven of their warehouses for decision making. The traffic light visualisation technique presents and conveys the results better than numbers. Finally, the new post-optimal analysis provides recommendations for cost efficient improvements. The findings of this study present valuable insights and guidelines for industrial managers and practitioners, especially those from the Indian manufacturing industry, for sustainable warehouse decision-making, and for improving their overall corporate sustainability performance

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics, 35(1-3), 293-297. doi:10.1016/0925-5273(94)90095-7Scala, N. M., Rajgopal, J., & Needy, K. L. (2014). Managing Nuclear Spare Parts Inventories: A Data Driven Methodology. IEEE Transactions on Engineering Management, 61(1), 28-37. doi:10.1109/tem.2013.2283170Hadad, Y., & Keren, B. (2013). ABC inventory classification via linear discriminant analysis and ranking methods. International Journal of Logistics Systems and Management, 14(4), 387. doi:10.1504/ijlsm.2013.052744Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research, 105(1), 29-37. doi:10.1016/s0377-2217(97)00039-8Rezaei, J., & Dowlatshahi, S. (2010). A rule-based multi-criteria approach to inventory classification. International Journal of Production Research, 48(23), 7107-7126. doi:10.1080/00207540903348361Hatefi, S. M., Torabi, S. A., & Bagheri, P. (2013). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Bruno, G., Esposito, E., Genovese, A., & Simpson, M. (2016). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment. Expert Systems with Applications, 43, 271-285. doi:10.1016/j.eswa.2015.07.016Poza, C. (2020). A Conceptual Model to Measure Football Player’s Market Value. A Proposal by means of an Analytic Hierarchy Process. [Un modelo conceptual para medir el valor de mercado de los futbolistas. Una propuesta a través de un proceso analítico jerárquico]. RICYDE. Revista internacional de ciencias del deporte, 16(59), 24-42. doi:10.5232/ricyde2020.05903Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. Journal of Cleaner Production, 96, 209-219. doi:10.1016/j.jclepro.2014.05.040Ishizaka, A., & Labib, A. (2011). Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method. Expert Systems with Applications, 38(6), 7317-7325. doi:10.1016/j.eswa.2010.12.004Partovi, F. Y., & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering, 41(4), 389-404. doi:10.1016/s0360-8352(01)00064-xAlonso-Manzanedo, M., De-la -Fuente-Aragon, M. V., & Ros-McDonnell, L. (2013). A Proposed Collaborative Network Enterprise Model in the Fruit-and-Vegetable Sector Using Maturity Models. Annals of Industrial Engineering 2012, 359-366. doi:10.1007/978-1-4471-5349-8_42Augusto, M., Lisboa, J., Yasin, M., & Figueira, J. R. (2008). Benchmarking in a multiple criteria performance context: An application and a conceptual framework. European Journal of Operational Research, 184(1), 244-254. doi:10.1016/j.ejor.2006.10.05

Fundamentos de las metodologías AHP y ANP. Aplicación al problema de selección de proveedores para la elaboración de una cerveza artesanal

El presente trabajo desarrolla un caso práctico de selección de proveedores de materias primas a través de las herramientas AHP (Analytic Hierarchy Process) y ANP (Analytic Network Process), que como veremos más adelante se tratan de unas metodologías muy útiles para la resolución de problemas de toma de decisión en los que intervienen más de un criterio de decisión y existen varias alternativas. En primer lugar, empezaremos definiendo los Problemas de Decisión Multicriterio y estudiando en profundidad alguna de las herramientas existentes para abordar este tipo de problemas, la Metodología AHP y ANP. Veremos el marco teórico de ambas metodologías así como la base matemática y los axiomas principales. A continuación veremos las diferencias existentes entre ambas, analizando las principales ventajas y desventajas que presentan cada una de ellas. Posteriormente presentaremos un caso real en el que aplicaremos las metodologías AHP y ANP a la selección de proveedores de materias primas de una empresa de elaboración artesanal de cerveza. Haremos uso de los softwares comerciales específicos que existen para la implantación y resolución de este tipo de problemas y finalizaremos comparando los resultados obtenidos con cada uno de ellos. Finalmente se incluye unos anexos sobre los dos softwares, Expert Choice más apropiado para la resolución de problemas AHP, y SuperDecisions que permite la resolución de ambos problemas, siendo más apropiado para los problemas ANP. Estos anexos sobre los softwares son incluidos como ayuda a la utilización de dichos programas.Universidad de Sevilla. Grado en Ingeniería de Tecnologías Industriale