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The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models.

The probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model.

The hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns.

The hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined.

The proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

Today’s dynamic markets are forcing firms to design increasingly complex channel strategies involving multiple channels of distribution. As the complexity of these systems increases, so too does the opportunity for conflict between individual channel coalitions within the firm. Whereas this hybrid channel conflict can reduce channel performance, it can also serve as a mechanism forcing internal channel coalitions to work harder and smarter to serve their markets. In this paper, we develop and test six hypotheses related to hybrid channel conflict. The findings indicate that hybrid channel conflict is an important determinant of both channel performance and satisfaction. The results suggest further that the relationship between hybrid channel conflict and channel performance is moderated by the lifecycle stage. Moreover, our data support the view that the frequency of conflict, but not its intensity, has a negative effect on channel system performance. We conclude with a discussion of the theoretical and managerial implications of this study.

The purpose of this paper is to analyze and compare four numerical methods to estimate the most suitable one which describes wind speed distribution of M’Sila, a province of northern Algeria.

The site chosen in this investigation is characterized by calm winds; in this case, the appropriate wind speed distribution is that of hybrid Weibull.

The four numerical methods used in the present paper are the maximum likelihood method, the graphical method, the moment method and the energy pattern factor method. The hybrid Weibull distributions using the abovementioned approaches are compared with the measured data via three statistical parameters, namely, the correlation coefficient, the root mean square error and the Chi-square error.

The obtained results showed that the moment method is the suitable one in describing month and annual wind speed hybrid Weibull parameters of this region.

Low combustion completeness has been the main defect of hybrid rockets. The present study tries to address the problem by bringing up the setup of the precombustion zone, which do not increase the manufacture cost and complexity.

A precombustion zone can provide a space for the liquid oxidizer to vaporize before entering the combustion zone, and prevents the endothermic effect of liquid oxidizer which can block the chemical reaction as well as the fuel regression. Therefore, this design is expected to raise the combustion completeness. The numerical simulation focuses on the flow field inside a cylindrical hybrid combustor. The distribution of temperature, combustion mode, mass fraction of reactants, velocity, combustion completeness, and solid‐fuel regression rate are presented.

With the setup of prevaporized zone of appropriate length, the upstream separation bubble which is unobvious for the case with no prevaporized zone can increase the mixing of reactants, and then increases the combustion completeness. Besides, the radial temperature distribution is more uniform. But when the length of prevaporized zone exceeds about one fourth of the combustor length, due to no enough space for the reactants to react, the combustion completeness begins to decrease and the radial temperature distribution becomes uneven. Therefore, a prevaporized zone with about 24 per cent of the combustor length can have optimum combustion completeness in the present study.

This study provides a useful design to raise the combustion completeness of a traditional hybrid rocket. However, the manufacture cost and complexity are not increased. So the results can be a good reference for the hybrid rocket designers.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.

To cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.

The results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.

In reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.

Burr distribution has been proved to be a useful failure model. It can assume different shapes which allow it to be a good fit for various lifetimes data. Hybrid censoring is an important way of generating lifetimes data. The purpose of this paper is to estimate an unknown parameter of the Burr type XII distribution when data are hybrid censored.

The problem is dealt with through both the classical and Bayesian point of view. Specifically, the methods of estimation used to tackle the problem are maximum likelihood estimation method and Bayesian method. Empirical Bayesian approach is also considered. The performance of all estimates is compared through their mean square error values. The paper employs Monte Carlo simulation to evaluate the mean square error values of all estimates.

The key findings of the paper are that the Bayesian estimates are superior to the maximum likelihood estimates (MLE).

This work has practical importance. Indeed, the proposed methods are applied to real life data.

The paper is original and is quite applicable in lifetimes data analysis.

This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed.

In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions.

This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader.

The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios.

This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

The aim of this research is to investigate the relationship between (dual) organizational identity and individual heuristics – simple rules and biases – in the process of strategy change. This paper offers a theory on identity reflexivity as a cognitive mechanism of strategy change in the context of organizational hybridity.

The authors draw on a 2-year ethnographic study at a Dutch social housing association dealing with the process of strategy change. The empirical data comprises of in-depth semi-structured interviews, ethnographic observations as well as secondary sources.

Conflicting identities at the organizational level influence heuristics at the individual level, since members tend to identify with their department's identity. Despite conflicting interpretations, paths of cognitive shortcuts – that the authors define as internal and external identity reflexivity – are shared by the conflicting identities.

The findings of this research are subject to limitations typical of a qualitative case-study, such as possibly being context dependent. The authors argue that this research contributes to the understanding of how individual heuristics relate to organizational heuristics, and suggest that the process of identity reflexivity can contribute to the alignment of conflicting identities enabling strategy formation in the context of a dual-identity organization.

Understanding how managers with conflicting identities achieve agreements is important to help organizational leaders to pursue sustainability-oriented strategy change.

Given the pressure experienced by mission-driven organizations to integrate multiple sustainability demands in their mission, understanding managers' decision-making mechanism when adapting to new, often conflicting, sustainability demands is important to accelerate societal sustainability transitions.

This paper addresses the process of new strategy design in the context of a socially driven business. This context fundamentally differs from the one addressed by the existing heuristics literature with respect to organizational environment and role, and specific competing demands.