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In this paper, we consider the evaluation of the best tender in a public procurement process. According to the Most Economic Advantageous Tender criterion, in order to analyze a number of tenders, a group of experts has to consider different criteria, both of qualitative and quantitative nature. Selecting the best tender involves the solution of a multi-criteria choice problem. Indeed, in Italy, the National Anti-Corruption Authority suggested the use of traditional multiple criteria methods to evaluate the qualitative component and provided details on how aggregate the evaluations related to single criteria to derive the ranking of tenders. However, they are rarely used in practice and may have some application drawbacks. This paper aims at proposing the use of ELECTRE (ELimination Et Choix Traduisant the Realité) III and P-AHP (parsimonious analytic hierarchy process) as the more effective methods for analyzing public tenders when a great number of alternatives and/or the heterogeneity of scales are involved. The use of the proposed methodologies is completely innovative in the practice, in the Italian context. Furthermore, we carry out a comparative study among these multi-criteria methods and analyze the strengths and the weaknesses of each method in order to help the DMs to identify the more appropriate method. Moreover, we aim to develop easier and user-friendly models that sound practical in the public tender context. The potential advantages of the proposed approach are validated by a real-life contractor-selection case. The results show that the rankings obtained by ELECTRE III and P-AHP are almost the same. [ABSTRACT FROM AUTHOR]

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a problem of particular complexity. It is therefore appropriate to consider decision support tools that take into account the characteristics of the products, the needs and requirements of producers, sellers and final consumers. The paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. In particular, we propose a dynamic decision-making approach with a "moving" horizon that advances over time taking into account the more recent available information. This approach allows future decisions to be rescheduled in relation to new data, over a planning horizon of the same duration. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Southern Italy show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Coronavirus disease 19 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, which is responsible for the ongoing global pandemic. Stringent measures have been adopted to face the pandemic, such as complete lockdown, shutting down businesses and trade, as well as travel restrictions. Nevertheless, such solutions have had a tremendous economic impact. Although the use of recent vaccines seems to reduce the scale of the problem, the pandemic does not appear to finish soon. Therefore, having a forecasting model about the COVID-19 spread is of paramount importance to plan interventions and, then, to limit the economic and social damage. In this paper, we use Genetic Programming to evidence dependences of the SARS-CoV-2 spread from past data in a given Country. Namely, we analyze real data of the Campania Region, in Italy. The resulting models prove their effectiveness in forecasting the number of new positives 10/15 days before, with quite a high accuracy. The developed models have been integrated into the context of SVIMAC-19, an analytical-forecasting system for the containment, contrast, and monitoring of Covid-19 within the Campania Region. [ABSTRACT FROM AUTHOR]

Bayesian inference shows that the distribution of the future event not only depends on the past events (prior), but also depends on the relation between the past and the future events (likelihood). However, the classical Bayesian methods do not consider the important contributions of recent data. In this paper, we propose a new Bayesian inference-based training method, which can be used as online training for Bayesian methods. We give the training methods for the exponential and the normal models. We successfully apply this method for the seismic parameter prediction using the data of central Italy from 2014 to 2017. Comparisons show our method is more effective than the other Bayesian methods. [ABSTRACT FROM AUTHOR]