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Summary This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.

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Process industries have the talent of emerging high levels of turbulent behaviors and uncertainties, such as the leakage of toxic substances and explosive materials. Resilience engineering, as a novel approach, can run the effects of such actions. Resilience engineering factors involve culture, change management, knowledge acquisition, risk assessment, readiness, plasticity, reportage, the obligation of a top manager, consciousness, safety procedures, incident survey, employee participation, and competence. The present study aims to investigate resilience engineering in process industries and analyze its efficiency using the data envelopment analysis (DEA) technique. Since there are high levels of uncertainty in the factors, Type-2 fuzzy sets that have a high capability of considering uncertainty is used to analyze the efficiency. The results of this work, which is the first case in evaluating the efficiency of resilience engineering in process industries by DEA and Type-2 fuzzy sets, indicate a robust approach for analyzing the efficiency and identifying the opportunities in process industries.INDEX TERMS Process industries, resilience engineering, data envelopment analysis, Type-2 fuzzy sets.

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PurposePrepaid mobile Internet is one of the most profitable services that are composed of multiple attributes. The overall utility of Internet service can be broken down into the sum of the utility of individual attribute levels. Based on the multi-attribute theory, rational consumers choose the service that yields the highest utility from a number of possible alternatives. Determining the optimal attribute levels that satisfy consumers' preferences and maximize the total revenue of the firm is a challenging multi-attribute decision problem for any mobile operator. When designing mobile Internet services, adopting a robust composition of services against different realizations of competitors' strategies can bring advantages for network operators. The purpose of this study is to determine the optimal attribute levels of prepaid mobile Internet packages with the aim of maximizing the total revenue of the firm by considering the paradigms of multi-attribute utility theory about consumer choices and the issue of uncertainty in counterpart services offered by the competitors.Design/methodology/approachThis paper formulates the problem of multi-attribute pricing and design of mobile Internet plans in a competitive environment by developing deterministic and robust scenario-based mathematical models and considering the paradigms of multi-attribute utility theory about consumer choices. The proposed robust scenario-based models are based on three different paradigms, including maximizing expected revenue, minimizing the negative deviation from expected revenue and minimizing the maximum regret. A comprehensive numerical analysis is conducted to evaluate and compare the efficiency of the proposed models.FindingsThe evaluations reveal that deploying recourse policy can result in higher revenue for the firm when facing uncertainty. By doing sensitivity analysis, this paper shows that consumer preferences for brand attribute and consumers' purchase frequency can influence the revenue of network operators.Originality/valueThis paper develops a novel deterministic multi-attribute product line design (PLD) model to address the problem of determining the price and composition of prepaid mobile Internet plans. Furthermore, the issue of uncertainty in counterpart services offered by the competitors is studied for the first time in the PLD literature.