Selecting a proper maintenance strategy in an attempt to preclude failures is of critical significance in system engineering due to its fallbacks in the safety and economics of plants operation. This process is a typical multiple-criteria decision-making (MCDM) problem that involves both tangible and intangible parameters that are often in conflict with each other. In this paper, an integrated analytic hierarchy process (AHP)–fuzzy MCDM approach is proposed to perform a comprehensive comparison between different maintenance policies. For this purpose, various criteria are taken into account that are different in nature, as some give a crisp value obtained from simulation, some are defined in linguistic terms based on experts’ opinions and some are in the form of triangular fuzzy numbers. The AHP method is used to determine the importance weights of the criteria. Subsequently, a distance-based fuzzy MCDM approach is employed to rank different maintenance policies and select the most appropriate one. Moreover, the fuzzy technique for the order of prioritization by similarity to ideal solution is used for verification of the proposed integrated approach. Lastly, the impact of each criterion on the rankings is examined. Four commonly implemented maintenance policies, namely condition-based, time-based, failure-based and opportunistic, are considered in this study. Also, a real-world example is presented to demonstrate the applicability of the proposed approach. The most significant feature of this approach lies in its capability in incorporating data in the forms of linguistic variables, triangular fuzzy numbers and crisp numbers into the evaluation process.

This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that "pull system" and "fault tolerant" among lean and IRE factors,

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Purpose Resilience engineering, job satisfaction and patient satisfaction were evaluated and analyzed in one Tehran emergency department (ED) to determine ED strengths, weaknesses and opportunities to improve safety, performance, staff and patient satisfaction. The paper aims to discuss these issues. Design/methodology/approach The algorithm included data envelopment analysis (DEA), two artificial neural networks: multilayer perceptron and radial basis function. Data were based on integrated resilience engineering (IRE) and satisfaction indicators. IRE indicators are considered inputs and job and patient satisfaction indicators are considered output variables. Methods were based on mean absolute percentage error analysis. Subsequently, the algorithm was employed for measuring staff and patient satisfaction separately. Each indicator is also identified through sensitivity analysis. Findings The results showed that salary, wage, patient admission and discharge are the crucial factors influencing job and patient satisfaction. The results obtained by the algorithm were validated by comparing them with DEA. Practical implications The approach is a decision-making tool that helps health managers to assess and improve performance and take corrective action. Originality/value This study presents an IRE and intelligent algorithm for analyzing ED job and patient satisfaction - the first study to present an integrated IRE, neural network and mathematical programming approach for optimizing job and patient satisfaction, which simultaneously optimizes job and patient satisfaction, and IRE. The results are validated by DEA through statistical methods.

This article concurrently studies customer relationship management (CRM) and organizational excellence (OE) by pursuing three goals. First, it investigates the relationship between CRM and OE; second, it conducts a performance assessment from CRM and OE viewpoints; and third, it analyzes how each factor of CRM and each criterion of OE affects an organization's performance. To achieve the first goal, a number of hypotheses about potential relationships between CRM factors and OE criteria are proposed with the cooperation of experts and using fuzzy DEMATEL. These hypotheses are then examined using the path analysis method to find out which one is supported and which one must be rejected. Subsequently, the data envelopment analysis (DEA) approach is employed to accomplish the second goal. Finally, a t‐test is used to achieve the third goal. To implement the research in the real world, two major international airports of Iran are considered as our survey cases.

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