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Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.

The research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.

The proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.

The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.

These models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.

A unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

The present paper analyzed a model consisting of one unit with a warm standby unit where the main unit has three states: up, degraded and down.

The semi-Markov model under the regenerative method is used to construct the mathematical model for the system.

The effectiveness measures of the system are discussed such as availability, reliability, steady-state availability and mean time to system failure. The life and repair times of the system units are assumed to be discrete follow discrete Weibull distribution. Also, the parameters of the discrete Weibull distribution are assumed to be fuzzy with bell-shaped membership function. An application is introduced to show the results obtained for the system and the profit of the presented model.

Rarely papers in literature treated the topic of the discrete-time semi-Markov process using a regenerative point technique.

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.

The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.

The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.

This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

This paper aims to determine the stage of the life cycle where the trucks of a waste collection fleet from a Colombian city are located through a reliability approach. The reliability analysis and the evaluation of curve of operational costs allow to know the moment in which it is necessary to make decisions regarding an asset, its maintenance or possible replacement.

For a dataset presented as maintenance work orders, the time to failures (TTFs) for each vehicle in the fleet were calculated. Then, a probability density function for those TTFs was fitted to locate each vehicle in a region of the bathtub curve and to calculate the reliability of the whole fleet. A general functional analysis was also developed to understand the function of the vehicles.

It was possible to determine that the largest proportion of the fleet was in the final stage of the life cycle, in this sense, the entire fleet represent critical assets which in most of cases could be worth replacement or overhaul.

In this study, an address is exposed for the identification of critical equipment by reliability and statistical analysis. This analysis is also integrated with the maintenance management process. This is a broadly interested topic since it allows to support the maintenance and operational decision-making process, indicating the focus of resource allocation all over the entire asset life cycle.

This study integrated the service gap of the PZB model, hidden importance and Taguchi’s quality engineering to develop a new two-dimensional strategic matrix, the Hidden Importance and Relative Quality Performance (HI-RQP) model. The HI-RQP model was then used to determine the managerial implications and improvement direction of memorable dining experience (MDE) attributes of Michelin-starred restaurants (MSRs).

This study collected 619 MDE questionnaires from customers of seven MSRs.

The results reveal that the HI-RQP model comprises four quadrants. Professional and high-quality service, carefully prepared dishes, restaurant style and customer-oriented service attitude are competitive advantages that should be maintained. Conversely, taste and freshness that exceed customers’ expectations and provide extra services to customers are MDE attributes that require urgent improvement.

Besides contributing practically to the enhancement of MDE in MSRs, the findings facilitate a more plausible identification of MDE attributes by integrating various theories into the HI-RQP model.

The hotel industry is one of the primary sectors contributing to every country's economy by promoting and reinforcing tourism. The pandemic situation across the globe is forcing the hospitality industry to adopt a suitable operational excellence (OPEX) methodology and its associated tools to deliver quality service at an affordable cost. However, there is limited evidence of applying one of the prominent OPEX methodologies, such as Six Sigma (SS), to enhance the hospitality industry's productivity and performance. Thus, the purpose of the study is to explore the application of the SS methodology to reduce the cycle time of a pantry workstation in a United Arab Emirates (UAE) hotel sector.

The research adopted a case study approach using DMAIC (define-measure-analyse-improve-control) phases to improve the performance of the minibar updating process attached to the hotel rooms. In addition, the study effectively used several tools, including normality tests, control charts, capability analysis, cause and effect diagram, failure mode effects analysis (FMEA), value stream mapping (VSM) and GEMBA, to validate the root causes and draw robust inferences.

The average cycle time and standard deviation of the pantry process were drastically reduced. Further, the analysis showed that the process capability index was substantially improved. Consequently, a notable improvement in the process performance was witnessed, which has resulted in a profit of $50,000 per annum in the hotel.

The article provides a structured and disciplined approach of SS in a hotel which can be used as a benchmarking document by the academicians, practitioners and policymakers for OPEX within the hospitality industry. Further, it paves the way to develop a road map for applying SS in the hospitality sector.

As per the authors' knowledge, to date, no case study has been reported in top tier refereed journals regarding the application of SS in the hotel sector. Thus, the current case study bridges this gap and reiterates that SS can be effectively used with meticulous planning and deployment in the hospitality industry.

In some environments, the failure rate of a system depends not only on time but also on the system condition, such as vibrational level, efficiency and the number of random shocks, each of which causes failure. In this situation, systems can keep working, though they fail gradually. So, the purpose of this paper is modeling multi-state system reliability analysis in capacitor bank under fatal and nonfatal shocks by a simulation approach.

In some situations, there may be several levels of failure where the system performance diminishes gradually. However, if the level of failure is beyond a certain threshold, the system may stop working. Transition from one faulty stage to the next can lead the system to more rapid degradation. Thus, in failure analysis, the authors need to consider the transition rate from these stages in order to model the failure process.

This study aims to perform multi-state system reliability analysis in energy storage facilities of SAIPA Corporation. This is performed to extract a predictive model for failure behavior as well as to analyze the effect of shocks on deterioration. The results indicate that the reliability of the system improved by 6%.

The results of this study can provide more confidence for critical system designers who are engaged on the proper system performance beyond economic design.

The purpose of this study is to examine the structure of the international fossil fuel trade network (IFFTN) and assess its effects on CO₂ emissions and global trade patterns. This research integrates complex network theory with econometric analysis to explore the dynamics of fossil fuel trade and its implications for environmental quality across various countries. Specifically, the study analyses the roles of different countries within this global network, examines the relationship between trade volumes and environmental impacts and evaluates how advancements in renewable energy generation could mitigate these effects. Through this comprehensive examination, the study seeks to provide an in-depth understanding of the trade-environment nexus.

The study uses data on international fossil fuel trade from 2005 to 2020, which includes 74 countries categorized as high-income, low-income and Asian economies based on their roles in the global market. This research constructs the IFFTN, where countries are depicted as nodes and trade links as edges. The authors analyse network parameters, such as degree, density and clustering coefficient, along with trade metrics like strength and centrality. These parameters are integrated into a panel fixed effects model, with the robustness of the findings confirmed through dynamic ordinary least squares (DOLS) analysis.

The study finds that the dynamic fossil fuel trade network includes key players such as the USA, China, France, India, the Netherlands and South Korea. It demonstrates increased connectivity and dependence among these countries, directly correlating with higher CO₂ emissions. However, this correlation is mitigated by the adoption of renewable energy, particularly in Asia and high-income countries. The impact on environmental quality is mediated through scale, technique and composition effects, suggesting significant environmental improvements through enhanced industry structure, technological progress and economies of scale.

This study recognizes several limitations. First, the categorization of countries into Asian economies, low-income and high-income groups may oversimplify the intricate effects of economic status on environmental impacts. Second, focusing primarily on per capita CO₂ emissions may neglect other critical environmental indicators. Future research should consider examining regional variations and including a wider range of environmental metrics. This approach would offer a more detailed perspective on the nuanced interactions between economic development and environmental sustainability, enhancing the depth and applicability of the findings.

To address the challenges of the IFFTN and CO₂ emissions, several practical policy measures are recommended. Governments should enhance international cooperation by establishing global platforms for sharing best practices and initiating technology transfer agreements to accelerate the adoption of energy-efficient technologies. Additionally, a phased transition towards more sustainable energy sources is crucial, involving increased investment in the renewable energy sector alongside incentives for adopting green technologies. On the trade front, governments should modify trade partnerships to address congestion externalities, fostering a shift towards more sustainable and environmentally friendly trade practices.

The social implications of the IFFTN are profound. As global reliance on fossil fuels continues, communities face heightened health risks due to increased pollution. Transitioning to renewable energy can alleviate these health concerns and the creation of green technologies, enhancing social well-being. Moreover, equitable access to energy-efficient solutions can reduce energy poverty, particularly in low-income countries, fostering greater societal resilience and inclusivity.

This study offers a pioneering examination of the trade-energy nexus across 74 countries, using complex network models to analyse diverse economic settings, particularly in Asian economies dominated by non-renewable energy. It identifies key market players and assesses their impact on dynamics such as congestion and market power. Additionally, the study explores the positive effects of renewable energy capacity on these relationships, highlighting its crucial role in driving sustainable energy transitions and enhancing the understanding of indirect trade-environment interactions.

This paper aims to capture the challenges faced by large industrial firms in implementing employee green behaviour. It uses the gamification-based Octalysis framework for identifying motivational drives and entwins it to self-efficacy theory seeking to motivate and engage the employees through game techniques.

This paper uses qualitative approach where semi-structured interviews were conducted through snowball sampling technique with managers in senior positions in power sector holding significant decision-making authority. The interviews were transcribed and were analysed thematically.

This study offers compelling evidence that industrial firms are grappling to inculcate pro-environmental behaviour largely losing on incentivising motivation. Gamification can provide an enjoyable framework balancing intrinsic and extrinsic motivational drives.

This study offers a framework applicable to organisations across sectors, addressing challenges in implementing green behaviour by leveraging four phases of game mechanics. It tackles issues related to motivation and demand for incentives by striking a balance between intrinsic and extrinsic motivations.

This research stands out by incorporating game mechanics, specifically designed through Octalysis, to boost self-efficacy and encourage green behaviour among employees. Furthermore, it is in harmony with Sustainable Development Goals and circular principles.

Through state-of-the-art insights on whistleblowing in India, the purpose of this paper is to highlight the role of sociocultural dynamics in whistleblowing.

A review of literature on wrongdoing and whistleblowing in India revealed various aspects of the national context pertinent to different stages of the phenomenon. Thematic analysis of these dimensions, allowing for a nomothetic approach, resulted in identifying six sociocultural themes common across wrongdoing and whistleblowing.

Sociocultural dynamics impacting the emergence, persistence and recognition of wrongdoing, the decision to blow the whistle, engagement in whistleblowing and the outcomes of whistleblowing encompass social relationships, power distribution, materialistic considerations, sense of propriety and fairness, public/civic orientation and ideological leanings. These factors coexist with international influences, institutional framework, workplace ethos and individual orientation. The presence of wrongdoing and the trajectory of whistleblowing in India are affected by the aforementioned factors.

The paper is based on secondary data rather than empirical endeavours.

By underscoring the relevance of contextual dynamics, in particular sociocultural factors, in the whistleblowing process, the paper indicates an important basis for appropriate interventions to manage wrongdoing and encourage whistleblowing while protecting whistleblowers and ensuring attention to rectifying wrongdoing and sanctioning offenders.

Apart from providing a contemporary and comprehensive overview of whistleblowing in India, the paper uncovers the significance of sociocultural factors which have been overlooked so far in the substantive area. Moreover, a contextualised process model of whistleblowing is proposed based on the analysis. In subsuming temporality, context and outcomes for all stakeholders, the model displays complexity and causality, emphasising holism.