Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the Non-Dominated Sorting Genetic Algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria

Contingency plan selection under interdependent risks

Managing supply chain risks (SCRs) has become an increasingly strategic key factor over the last decade, aimed at pursuing and maintaining business success. These types of risks clearly pose an important challenge to managers nowadays, and evaluating uncertainty affecting business scenarios is crucial. Indeed, COVID-19 has been dangerously affecting supply chains of global manufacturers, and is indicated as a main trigger cause of supply chain disruptions for a huge number of enterprises. Major effects derived from epidemic outbreaks on supply chains should be further adequately investigated since enterprises have been adopting poor risk management plans [1] to face them. Many companies, for instance, have been assuming a passive attitude towards the management of pandemic effects, simply waiting for the situation to come back to normality at hopefully short notice. On the other side, those companies that are more proactively reacting to the pandemic have been encountering countless difficulties in implementing risk management plans at operational levels [2]. Given these preliminaries, the present contribution is aimed at proposing a way for managing risks due to COVID-19. The main objectives of the present contribution can be formalised as follows: 1. analysing critical supply chain risks and related interdependence relationships to establish priorities on mitigation/prevention actions and most influential risks; 2. proposing a structured method capable to get the vector of risks’ weights and ease the selection of the most suitable contingency strategy on the basis of companies’ needs. These objectives are herein addressed by means of a Multi-Criteria Decision-Making (MCDM) approach based on the use of the Analytic Network Process (ANP), suggested to analyse and weight risks by taking into account relations of dependence existing among the same risks and effects. Results will be formalised in the field of automotive industry as offering a significant input for the process of contingency strategy selection while simultaneously considering uncertainty affecting evaluations on the basis of the specific business context features

Designing supplier selection strategies under COVID-19 constraints for industrial environments

COVID-19 has been impacting worldwide supply chains causing interruption, closure of production and distribution. This impact has been drastic on the supplier side and, as a consequence of disruptions, strong reductions of production have been estimated. Such a circumstance forces companies to propose innovative best practices of supply chain risk management aimed at facing vulnerability generated by COVID-19 and pursuing industrial improvements in manufacturing and production environments. As a part of supply chain strategy, supplier selection criteria should be revised to include pandemic-related risks. This article aims to propose an answer to such a problem. In detail, a comprehensive tool designed as a hybrid combination of multi-criteria decision-making (MCDM) methods is suggested to manage important stages connected to the production development cycle and to provide companies with a structured way to rank risks and easily select their suppliers. The main criteria of analysis will be first identified from the existent literature. Risks related to COVID-19 will be then analysed in order to elaborate a comprehensive list of potential risks in the field of interest. The Best Worst Method (BWM) will be first used to calculate criteria weights. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) will be then applied to rank and prioritize risks affecting suppliers. The effectiveness of the approach will be tested through a case study in the sector of automotive industry. The applicability of the designed MCDM framework can be extended also to other industrial sectors of interest

A strategic approach to safeguard global supply chains against COVID-19 disruptions

The global system of supply chains has been dramatically disrupted over the last years due to the outbreak of the COVID-19 pandemic. In these current chal lenging times, this paper proposes a methodological approach for managing dependence and uncertainty in dynamic industrial scenarios. A detailed study of epidemic effects is carried out according to an operational management-based perspective. We proceed by analyzing connections among effects and risks potentially leading to significant supply chain disturbances through a multicri teria approach. Risks and effects are weighted by applying the Analytic Network Process (ANP). Weighted risks are then assumed as criteria for selecting the most suitable contingency strategy. To this aim, the Fuzzy Technique for Order of Pref erence by Similarity to Ideal Solution (FTOPSIS) is able to rank a set of strategies by addressing and quantifying uncertainty. A case study on the sector of the automotive industry is implemented to validate the proposed methodological approach

Constrained consistency enforcement in AHP

Decision-making in the presence of intangible elements must be based on a robust, but subtle, balance between expert know-how and judgment consistency when eliciting that know-how. This balance is frequently achieved as a trade-off reached after a feedback process softens the tension frequently found between one force steadily pulling towards (full) consistency, and another force driven by expert feeling and opinion. The linearization method, developed by the authors in the framework of the analytic hierarchy process, is a pull-towards-consistency mechanism that shows the path from an inconsistent body of judgment elicited from an expert towards consistency, by suggesting optimal changes to the expert opinions. However, experts may be reluctant to alter some of their issued opinions, and may wish to impose constraints on the adjustments suggested by the consistency-enforcement mechanism. In this paper, using the classical Riesz representation theorem, the linearization method is accommodated to consider various types of constraints imposed by experts during the abovementioned feedback process

Funciones test para optimización mono-objetivo

Para resolver la mayor parte de problemas de optimización del mundo real son necesarias técnicas sofisticadas, tales como los algoritmos evolutivos, que no se basan en el Cálculo Infinitesimal y que, en consecuencia, nunca caben en los planes de estudios de los grados en ciencia e ingeniería. No obstante, el ingeniero tendrá que utilizar tales técnicas antes o después. Para poner a prueba la habilidad de tales técnicas de optimización se suelen utilizar problemas de benchmarking que exhiben algunas de las características de los problemas del mundo real. En este artículo enumeramos brevemente algunas de tales características y presentamos una colección de problemas de optimización mono-objetivo, no condicionada, en varias variables.Izquierdo Sebastián, J.; Carpitella, S. (2018). Funciones test para optimización mono-objetivo. http://hdl.handle.net/10251/105210DE

Management of uncertain pairwise comparisons in AHP through probabilistic concepts

[EN] Fast and judicious decision-making is paramount for the success of many activities and processes. However, various degrees of difficulty may affect the achievement of effective and optimal solutions. Decisions should ideally meet the best trade-off among as many of the involved factors as possible, especially in the case of complex problems. Substantial cognitive and technical skills are indispensable, while not always sufficient, to carry out optimal evaluations. One of the most common causes of wrong decisions derives from uncertainty and vagueness in making forecasts or attributing judgments. The literature shows numerous efforts towards the optimization and modeling of uncertain contexts by means of probabilistic approaches. This paper proposes the use of probability theory to estimate uncertain expert judgments within the framework of the analytic hierarchy process and, more specifically, within a linearization scheme developed by the authors. After describing the necessary probabilistic concepts of interest, the main results are developed. These results can be summarized as using various kinds of random variables with uncertainty embodied in undecided pairwise comparisons. A case study focused on the maintenance management of an industrial water distribution system exemplifies the approach.Benítez López, J.; Carpitella, S.; Certa, A.; Izquierdo Sebastián, J. (2019). Management of uncertain pairwise comparisons in AHP through probabilistic concepts. Applied Soft Computing. 78:274-285. https://doi.org/10.1016/j.asoc.2019.02.020S2742857

A hybrid multicriteria approach to GPR image mining applied to water supply system maintenance

[EN] Data processing techniques for Ground Penetrating Radar (GPR) image mining provide essential information to optimize maintenance management of Water Supply Systems (WSSs). These techniques aim to elaborate on radargrams in order to produce meaningful graphical representations of critical buried components of WSSs. These representations are helpful non-destructive evaluation tools to prevent possible failures in WSSs by keeping them adequately monitored. This paper proposes an integrated multi-criteria decision making (MCDM) approach to prioritize various data processing techniques by means of ranking their outputs, namely their resulting GPR image representations. The Fuzzy Analytic Hierarchy Process (FAHP) is applied to weight various evaluation criteria, with the purpose of managing vagueness and uncertainty characterizing experts' judgments. Then, the Elimination Et Choix Traduisant la REalite III (ELECTRE III) method is used to obtain the final ranking of alternatives. A real case study, focusing on a set of four GPR images as outputs of different data processing techniques, is presented to prove the usefulness of the proposed hybrid approach. In particular, the GPR images are ranked according the evaluation of five criteria namely visualization, interpretation, identification of features, extraction of information and affordability. The findings offer a structured support in selecting the most suitable data processing technique(s) to explore WSS underground. In the presented case, two options, namely the variance filter and the subtraction methods, offer the best results. (C) 2018 Elsevier B.V. All rights reserved.Part of this work has been developed under the support of the Universitat Politecnica de Valencia, Valencia (Spain), grant: UPV mobility program for PhD students, awarded to the first author, and of Fundacion Carolina PhD, within its short stage scholarship program awarded to the second author.Carpitella, S.; Ocaña-Levario, SJ.; Benítez López, J.; Certa, A.; Izquierdo Sebastián, J. (2018). A hybrid multicriteria approach to GPR image mining applied to water supply system maintenance. Journal of Applied Geophysics. 159:754-764. https://doi.org/10.1016/j.jappgeo.2018.10.021S75476415

Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCR) is crucial, and is currently a very active field of research. Failure Mode Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the risk priority number (RPN) is proposed within the FMECA framework by means of an integrated multi-criteria decision-making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated to input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: when planning interventions of prevention/mitigation, primary importance should be given to 1) supply chain disruptions due to natural disasters, 2) manufacturing facilities, human resources, policies and breakdown processes, and 3) inefficient transport

Managing Human Factors to Reduce Organisational Risk in Industry

[EN] Human factors are intrinsically involved at virtually any level of most industrial/business activities, and may be responsible for several accidents and incidents, if not correctly identified and managed. Focusing on the significance of human behaviour in industry, this article proposes a multi-criteria decision-making (MCDM)-based approach to support organizational risk assessment in industrial environments. The decision-making trial and evaluation laboratory (DEMATEL) method is proposed as a mathematical framework to evaluate mutual relationships within a set of human factors involved in industrial processes, with the aim of highlighting priorities of intervention. A case study related to a manufacturing process of a real-world winery is presented, and the proposed approach is applied to rank human factors resulting from a previous organisational risk evaluation from which suitable inference engines may be developed to better support risk management.This research was funded by Universitat Politècnica de València: 114417.Carpitella, S.; Carpitella, F.; Certa, A.; Benítez López, J.; Izquierdo Sebastián, J. (2018). Managing Human Factors to Reduce Organisational Risk in Industry. Mathematical and Computational Applications. 23(4):1-17. https://doi.org/10.3390/mca23040067S11723