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The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate.

Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring.

The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently.

The PGLM with log link has not been used to monitor multinomial profiles in Phase I.

One of the common approaches to improve systems reliability is using standby redundancy. Although many works are available in the literature on the applications of standby redundancy, the system components are assumed to be independent of each other. But, in reality, the system components can be dependent on one another, causing the failure of each component to affect the failure rate of the remaining active components. In this paper, a standby two-unit system is considered, assuming a dependency between the switch and its associated active component.

This paper assumes that the failures between the switch and its associated active component follow the Marshall–Olkin exponential bivariate exponential distribution. Then, the reliability analysis of the system is done using the continuous-time Markov chain method.

The derived equations application to determine the system steady-state availability, system reliability and sensitivity analysis on the mean time to failure is demonstrated using a numerical illustration.

All previous models assumed independency between the switch and the associated active unit in the standby redundancy approach. In this paper, the switch and its associated component are assumed to be dependent on each other.

In this paper, the joint replenishment problem is modeled for a two-level supply chain consisting of a single supplier and multiple retailers that use the vendor-managed inventory (VMI) policy for several products. This paper aims to find the optimal number of products to order in both policies, the optimal times at which each retailer orders the products in the traditional policy and the optimal times at which the supplier orders the product in the VMI policy.

The problem is first formulated into the framework of a constrained integer nonlinear programming model; then, the problem is solved using a teacher-learner based optimization algorithm. As there are no benchmarks available in the literature, a genetic algorithm is used as well to validate the results obtained.

The solutions obtained using both the algorithms for several numerical examples are compared to the ones of a random search procedure for further validation. A real case is solved at the end to demonstrate the applicability of the proposed methodology and to compare both the policies.

The paper does not have any special limitations.

The study has significant practical implications for the sellers and for the suppliers who have to get the most profit. Also, satisfying the constraints make decision more complicated.

This paper has two main originalities. The authors have developed the model of the joint replenishment problem and have contributed in the problem-solving process. They have used a new meta-heuristic and then compared it to a classic one.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

Tackling the challenges of water scarcity requires comprehensive management according to financial, environmental and social issues. This paper aims to develop a planning approach for systematic decision-making and pay attention to uncertainties in water demand management and supply investment.

This study presents a multiobjective optimization model to manage water resources based on the balance of supply and demand. The objectives of the model include economic, social and environmental (sustainable development) factors. The model achieves an optimal urban water portfolio by using a scenario tree.

The mathematical goal programming (GP) in a multiobjective optimization model is applied and solved by the branch and bound method. The results indicate the selected supply augmentation and demand management options in each stage for 20 years according to the dry, normal and wetness year scenarios.

This model is based on a real-world case and has been implemented in the city of Karaj. It can be applied for water management of other cities concerning sustainable development as well.

This paper innovates by considering the sustainable development criteria that are defined using three objective functions, including economic, social and environmental factors. The balance of supply and demand concerning uncertainty has not been investigated in any urban water portfolios. The standardized precipitation evapotranspiration index (SPEI) is incorporated to generate different scenarios. To the best of the authors’ knowledge, this approach is used for the first time.

The purpose of this paper is to estimate the required number of robots consisting of some non-repairable components, by employing a renewal model. Considering the importance of the availability of standby autonomous robots for reducing and preventing down-times of advanced production systems, which imposes a considerable loss, the present research tries to introduce a practical model for the determination of the required number of autonomous robots.

Most of the available research on the estimation of the required standby components based on the reliability characteristics of components has not considered the environmental factors influencing the reliability characteristics. Therefore, such estimations are not accurate enough. In contrast, this paper focuses on the influence of the environmental and human factors (e.g. the operators’ skill) on the robot reliability characteristics.

A model based on the Weibull renewal process combined with the cold standby strategy is developed for reliability evaluation of the system. The effectiveness of the proposed integrated reliability evaluation model is worked out in some cases.

Determining a required number of robots is an important issue in availability and utilization of a complex robotic production system. In an advanced production system, while the estimation process of a required number of robots can be performed through different approaches, one of the realistic estimation methods is based on the system’s reliability that takes into consideration the system operating environment. To forecast the required number of robots for an existing production system, in some cases, the assumption of a constant failure rate does not differ much from the assumption of a non-constant failure rate and can be made with an acceptable error.

This paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability.

For practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose.

Hazard decision tree (HDT) was configured to compute the hazards of each component and the whole AGV robot system. Through this research, a promising technical approach was established, allowing decision-makers to identify the critical components of AGVs along with their crucial hazard phases at the design stage.

As complex systems have become global and essential in today’s society, their reliable design and determination of their availability have turned into very important tasks for managers and engineers. Industrial robots are examples of these complex systems that are being increasingly used for intelligent transportation, production and distribution of materials in warehouses and automated production lines.

The purpose of this paper is to derive a closed-form expression for the steady-state availability of a cold standby repairable k-out-of-n system. This makes the availability calculation much easier and accurate.

Assuming exponential distributions for system failure and repair, the Markov method is employed to derive the formula.

The proposed formula establishes an easier and faster venue and provides accurate steady-state availability.

The formula is valid for the case when the probability density function of the component failure and the repair is exponential.

The Markov method has never been used in the literature to derive the steady-state availability of a cold standby repairable k-out-of-n: G system.