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

A Fuzzy inference expert system to support the decision of deploying a military naval unit to a mission

Naval military units are complex systems required to operate in xed time frames in o shore tasks where maintenance operations are drastically limited. A failure during a mission is a critical event that can drastically in uence the mission success. The decision of switching a unit to a mission hence requires complex judgments involving information about the health status of machineries and the environmental conditions. The present procedure aims to support the decision about switching a unit to a mission considering the vagueness and uncertainty of information by means of fuzzy theory and emulates the decision process of a human expert by means of a rule-based inference engine. A numerical application is presented to prove the e ectiveness of the approach

A new innovative cooling law for simulated annealing algorithms

The present paper proposes an original and innovative cooling law in the field of Simulated Annealing (SA) algorithms. Particularly, such a law is based on the evolution of different initial seeds on which the algorithm works in parallel. The efficiency control of the new proposal, executed on problems of different kind, shows that the convergence quickness by using such a new cooling law is considerably greater than that obtained by traditional laws. Furthermore, it is shown that the effectiveness of the SA algorithm arising from the proposed cooling law is independent of the problem type. This last feature reduces the number of parameters to be initially fixed, so simplifying the preliminary calibration process necessary to optimize the algorithm efficiency

Transport policy and climate change: how to decide when experts disagree

Transport is the sector with the fastest growth of greenhouse gases emissions in many countries. Accumulation of these emissions may cause uncertain and irreversible adverse climate change impacts. In this context, we use the analytic hierarchy process (AHP) to face the question on how to select the best transport policy if the experts have different opinions and beliefs on the occurrence of these impacts. Thus, both the treatment of uncertainty and dissent are examined for the ranking of transport policies. The opinions of experts have been investigated by a means of a survey questionnaire. A sensitivity analysis of the experts’ weights and the criteria’ weights confirms the robustness of the results

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

Machine Learning approach towards real time assessment of hand-arm vibration risk

In industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed