Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

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

Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs

[EN] Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution.Sanchis, R.; Díaz-Madroñero Boluda, FM.; López Jiménez, PA.; Pérez-Sánchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water. 11(12):1-22. https://doi.org/10.3390/w11122432S1221112Biswas, A. K. (2004). Integrated Water Resources Management: A Reassessment. Water International, 29(2), 248-256. doi:10.1080/02508060408691775Pahl-Wostl, C. (2006). Transitions towards adaptive management of water facing climate and global change. Water Resources Management, 21(1), 49-62. doi:10.1007/s11269-006-9040-4Wu, K., & Zhang, L. (2014). Progress in the Development of Environmental Risk Assessment as a Tool for the Decision-Making Process. Journal of Service Science and Management, 07(02), 131-143. doi:10.4236/jssm.2014.72011Hernández-Bedolla, J., Solera, A., Paredes-Arquiola, J., Pedro-Monzonís, M., Andreu, J., & Sánchez-Quispe, S. (2017). The Assessment of Sustainability Indexes and Climate Change Impacts on Integrated Water Resource Management. Water, 9(3), 213. doi:10.3390/w9030213Hunink, J., Simons, G., Suárez-Almiñana, S., Solera, A., Andreu, J., Giuliani, M., … Bastiaanssen, W. (2019). A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water, 11(10), 1976. doi:10.3390/w11101976Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2016). Modeling Irrigation Networks for the Quantification of Potential Energy Recovering: A Case Study. Water, 8(6), 234. doi:10.3390/w8060234Corominas, J. (2010). Agua y energía en el riego, en la época de la sostenibilidad. Ingeniería del agua, 17(3). doi:10.4995/ia.2010.2977Romero, L., Pérez-Sánchez, M., & Amparo López-Jiménez, P. (2017). Improvement of sustainability indicators when traditional water management changes: a case study in Alicante (Spain). AIMS Environmental Science, 4(3), 502-522. doi:10.3934/environsci.2017.3.502Davies, E. G. R., & Simonovic, S. P. (2011). Global water resources modeling with an integrated model of the social–economic–environmental system. Advances in Water Resources, 34(6), 684-700. doi:10.1016/j.advwatres.2011.02.010ALCAMO, J., DÖLL, P., HENRICHS, T., KASPAR, F., LEHNER, B., RÖSCH, T., & SIEBERT, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-337. doi:10.1623/hysj.48.3.317.45290Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327Rahaman, M. M., & Varis, O. (2005). Integrated water resources management: evolution, prospects and future challenges. Sustainability: Science, Practice and Policy, 1(1), 15-21. doi:10.1080/15487733.2005.11907961Markantonis, V., Reynaud, A., Karabulut, A., El Hajj, R., Altinbilek, D., Awad, I. M., … Bidoglio, G. (2019). Can the Implementation of the Water-Energy-Food Nexus Support Economic Growth in the Mediterranean Region? The Current Status and the Way Forward. Frontiers in Environmental Science, 7. doi:10.3389/fenvs.2019.00084Food and Agriculture Organization (FAO)www.fao.orgDirective 2000/60/EC of the European Parliament and of the Councilhttps://eur-lex.europa.eu/eli/dir/2000/60/ojNamany, S., Al-Ansari, T., & Govindan, R. (2019). Sustainable energy, water and food nexus systems: A focused review of decision-making tools for efficient resource management and governance. Journal of Cleaner Production, 225, 610-626. doi:10.1016/j.jclepro.2019.03.304Archibald, T. W., & Marshall, S. E. (2018). Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty. Environmental Modeling & Assessment, 23(6), 753-777. doi:10.1007/s10666-018-9628-0Chen, S., Shao, D., Gu, W., Xu, B., Li, H., & Fang, L. (2017). An interval multistage water allocation model for crop different growth stages under inputs uncertainty. Agricultural Water Management, 186, 86-97. doi:10.1016/j.agwat.2017.03.001Xie, Y. L., Xia, D. H., Huang, G. H., Li, W., & Xu, Y. (2015). A multistage stochastic robust optimization model with fuzzy probability distribution for water supply management under uncertainty. Stochastic Environmental Research and Risk Assessment, 31(1), 125-143. doi:10.1007/s00477-015-1164-8Heumesser, C., Fuss, S., Szolgayová, J., Strauss, F., & Schmid, E. (2012). Investment in Irrigation Systems under Precipitation Uncertainty. Water Resources Management, 26(11), 3113-3137. doi:10.1007/s11269-012-0053-xPereira-Cardenal, S. J., Mo, B., Riegels, N. D., Arnbjerg-Nielsen, K., & Bauer-Gottwein, P. (2015). Optimization of Multipurpose Reservoir Systems Using Power Market Models. Journal of Water Resources Planning and Management, 141(8), 04014100. doi:10.1061/(asce)wr.1943-5452.0000500Kumari, S., & Mujumdar, P. P. (2017). Fuzzy Set–Based System Performance Evaluation of an Irrigation Reservoir System. Journal of Irrigation and Drainage Engineering, 143(5), 04017002. doi:10.1061/(asce)ir.1943-4774.0001155Jairaj, P. G., & Vedula, S. (2000). Water Resources Management, 14(6), 457-472. doi:10.1023/a:1011117918943Li, M., Guo, P., Singh, V. P., & Zhao, J. (2016). Irrigation Water Allocation Using an Inexact Two-Stage Quadratic Programming with Fuzzy Input under Climate Change. JAWRA Journal of the American Water Resources Association, 52(3), 667-684. doi:10.1111/1752-1688.12415Bozorg-Haddad, O., Malmir, M., Mohammad-Azari, S., & Loáiciga, H. A. (2016). Estimation of farmers’ willingness to pay for water in the agricultural sector. Agricultural Water Management, 177, 284-290. doi:10.1016/j.agwat.2016.08.011Raju, K. S., & Duckstein, L. (2003). Multiobjective fuzzy linear programming for sustainable irrigation planning: an Indian case study. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 7(6), 412-418. doi:10.1007/s00500-002-0230-6Regulwar, D. G., & Gurav, J. B. (2012). Sustainable Irrigation Planning with Imprecise Parameters under Fuzzy Environment. Water Resources Management, 26(13), 3871-3892. doi:10.1007/s11269-012-0109-yMula, 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:10.1080/00207540701413912Díaz-Madroñero, M., Mula, J., Jiménez, M., & Peidro, D. (2016). A rolling horizon approach for material requirement planning under fuzzy lead times. 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Estrategias óptimas para la reducción de pérdidas de agua en sistemas de abastecimiento

[ES] La justificada y creciente preocupación por mejorar el aprovechamiento del agua en los sistemas de abastecimiento exige aumentar la calidad y rendimiento de dichos sistemas. El presente artículo presenta una serie de técnicas encaminadas a reducir las pérdidas de agua e incrementar la eficiencia de los abastecimientos. La metodología propuesta propone criterios de selección y optimización de las estrategias que deben ser aplicadas para conseguir estos objetivos de forma óptima. En este sentido se establece una jerarquía en base a criterios técnicos y de rentabilidad económica, que con la ayuda de modelos matemáticos y la debida coordinación con las demás funciones de la gestión y explotación del abastecimiento, permitirá confeccionar un programa integral para alcanzar y mantener un estado óptimo de funcionamiento del sistema. Asimismo, se recomienda efectuar un análisis completo del sistema hidráulico que, a través de su diagnóstico, determine las causas que disminuyen su seguridad y rendimiento, de modo que puedan optimizarse económicamente las medidas necesarias para cumplir las restricciones de riesgo máximo aceptable y eficiencia mínima exigida en el aprovechamiento de los recursos hídricos y energéticos, cada vez más escasos.Vela, A.; Martínez, F.; García-Serra, J.; Pérez, R. (1994). Estrategias óptimas para la reducción de pérdidas de agua en sistemas de abastecimiento. Ingeniería del Agua. 1(1):35-54. https://doi.org/10.4995/ia.1994.2630SWORD355411A.E.A.S. (1990) "El Suministro de Agua Potable en España -1987" Documento técnico publicado por la Asociación Española de Abastecimiento y Saneamiento.A.E.A.S. (1992) "El Suministro de Agua Potable en España -1990" Documento técnico publicado por la Asociación Española de Abastecimiento y Saneamiento.Cabrera E., Andrés M., Planells F. (1994) "Optimal Management of Pipe Network Maintenance by Cost Analysis of Water Losses". Journal AWWA, aceptado para su publicación, Julio 1994Cabrera, E. (1989) "Régimen de Explotación Óptimo en un Abastecimiento de Agua" El Agua en la Comunidad Valenciana. Generalitat Valenciana.Cascetta F., Vigo P. (1992) "Location and assessment of water leakage" Measurement + Control, Volume 25, November 1992.Cesario, Al (1991) "Network Analysis for Planning, Engineering, Operations and Management Perspectives" Journal AWWA, pp 38-42, February 1991.Coulbeck, B. (1988) "Computer Applications in Water Supply" Volume 1 - Systems Analysis and Simulations.Germanopoulos G. (1989) "Leakage reduction by excess pressure minimization in a water supply network" Proc. Instn. Civil Engrs. Part. 2. Paper 9404.Goulter I.C., Kazemi A. (1989). "Analysis of water distribution pipe failure types in Winnipeg, Canadá" Journal of Transportation Engineering. Vol. 115. n°2.Jowitt P. (1990) "Optimal valve control in water distribution networks" Journal of Water Resources Planning and Management. Vol. 116, No. 4.Khadam M., Shammas N. (1991) "Water Losses from Municipal Utilities and their impacts" 1WRA, Water International, 16, No 4, 254-261.Koelle, E. (1989) "Modelización y control de redes de distribución de Agua Potable" El Agua en la Comunidad Valenciana. Generalitat Valenciana y UPV.Liggett, J. (1992) "Network monotoring and the algorithmic location of leaks under steady conditions" Los abastecimientos de agua urbanos. Estado actual y tendencias futuras. UIMP. Valencia.Lord W., Chase J. (1983)."Choosing the optimal water conservation policy" Journal AWWA. Management and Operations. pp 324-329.July 1983.Male J.W. Noss R.R. (1985). "Identifying and reducing losses in water distribution systems" Noyes Publications. New Jersey.Martínez F. (1989) "Modelización y diseño de redes de distribución de agua" El agua en la Comunidad Valenciana. Generalitat Valenciana.Shamir U., Howard C.D.D. (1979) "An Analytical Approach to Scheduling Pipe Replacement" Journal AWWA, 71:5:248.Valerio A., Giles H. (1988) "A Simple Economic Model For The Selection Of The Optimum Method of Leakage Control" Water International, 13 92-97.Vela A. (1993) Contribución a la diagnosis y optimización del mantenimiento integral de sistemas de distribución de agua. Tesis Doctoral. Universidad Politécnica de Valencia.Walski T.M. (1984) Analysis of Water Distribution Systems. Van Nostrand Reinhold. New York.Walski T.M. (1987) "Replacement Rules for Water Mains " Journal AWWA. Management and Operations. pp 33-37. November 198

A Review of Multicriteria Assessment Techniques Applied to Sustainable Infrastructure Design

[EN] Given the great impacts associated with the construction and maintenance of infrastructures in both the environmental, the economic and the social dimensions, a sustainable approach to their design appears essential to ease the fulfilment of the Sustainable Development Goals set by the United Nations. Multicriteria decision-making methods are usually applied to address the complex and often conflicting criteria that characterise sustainability. The present study aims to review the current state of the art regarding the application of such techniques in the sustainability assessment of infrastructures, analysing as well the sustainability impacts and criteria included in the assessments. The Analytic Hierarchy Process is the most frequently used weighting technique. Simple Additive Weighting has turned out to be the most applied decision-making method to assess the weighted criteria. Although a life cycle assessment approach is recurrently used to evaluate sustainability, standardised concepts, such as cost discounting, or presentation of the assumed functional unit or system boundaries, as required by ISO 14040, are still only marginally used. Additionally, a need for further research in the inclusion of fuzziness in the handling of linguistic variables is identified.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project no. BIA2017-85098-R).Navarro, IJ.; Yepes, V.; Martí, JV. (2019). A Review of Multicriteria Assessment Techniques Applied to Sustainable Infrastructure Design. Advances in Civil Engineering. 2019(6134803):1-16. https://doi.org/10.1155/2019/6134803S11620196134803Kyriacou, A. P., Muinelo-Gallo, L., & Roca-Sagalés, O. (2019). The efficiency of transport infrastructure investment and the role of government quality: An empirical analysis. 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Sustainability assessment tool for the decision making in selection of energy system—Bosnian case. Energy, 32(10), 1979-1985. doi:10.1016/j.energy.2007.02.006Cartelle Barros, J. J., Lara Coira, M., de la Cruz López, M. P., & del Caño Gochi, A. (2015). Assessing the global sustainability of different electricity generation systems. Energy, 89, 473-489. doi:10.1016/j.energy.2015.05.110Klein, S. J. W., & Whalley, S. (2015). Comparing the sustainability of U.S. electricity options through multi-criteria decision analysis. Energy Policy, 79, 127-149. doi:10.1016/j.enpol.2015.01.007Montajabiha, M. (2015). An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning. Group Decision and Negotiation, 25(2), 221-244. doi:10.1007/s10726-015-9440-zFetanat, A., & Khorasaninejad, E. (2015). A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. 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