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Conventional statistical forecasting methods typically need a large sample size or the use of overly confident hypotheses, like the Gaussian distribution of the input data. Unfortunately, these input data are frequently scarce or do no not follow a normal distribution law. A grey forecasting model can be developed and used to predict energy consumption for at least four data points or ambiguous data based on grey theory. The standard grey model, however, may occasionally result in significant forecasting errors.

In order to reduce these errors, this paper proposes a hybrid multivariate grey model (namely Grey Modeling (1,N)) optimized by Genetic Algorithms with sequential selection forecasting mechanism, abbreviated as Sequential-GMGA(1,N). A real case of Cameroon's oil products consumption is considered to demonstrate the effectiveness of the proposed forecasting model.

The results show the superiority of Sequential-GMGA(1,4) when compared with the results of competing grey models reported in the literature, with a mean absolute percentage error as low as 0.02%.

Without changing the model's basic structure, the suggested framework completely extracts the evolution law of multivariate time series. Regardless of data patterns, Sequential-GMGA(1,4) actively enhances all model parameters over the course of each predicted timeframe. Consequently, Sequential-GMGA(1,4) improves forecast accuracy by resolving the discrepancy between the model's least sum of squares of prediction errors and the parameterization approach based on grey derivative.

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

The purpose of this paper is to test the relationship between objective and subjective sales performance and salespeople’s economic and non-economic satisfaction in a sequential logic model.

Based on a questionnaire survey using a deductive approach. A total of 315 companies were ultimately selected for participation in the study, to represent a range of companies from different industries and company sizes in the product-oriented business sector of Norway. A total of 236 questionnaires were returned, generating a response rate of 74.9%.

The sequential logic of objective and subjective sales performance, in connection with salespeople’s economic and non-economic satisfaction, reveals an underlying structure that can link existing theory and previous studies on sales performance and salesperson satisfaction in business-to-business (B2B) settings.

The results reported applying only to a B2B setting, to test whether the sequential logic model and mediating effects still hold in such setting. This study is also limited to product-oriented companies in Norway, which offers the opportunity for a future study to verify whether the refined research model also applies to service-oriented companies.

The results indicate that the constructs of objective and subjective sales performance and salespeoplés economic and non-economic satisfaction are intertwined in a B2B setting. Specifically, these constructs are related to one another sequentially.

Contributes to structuring in a B2B setting, the relationships between objective and subjective sales performance on the one hand and salespeoplés economic and non-economic satisfaction on the other. It also highlights two mediating effects, namely, subjective sales performance mediates the relationship effect between objective sales performance and salespeoplés economic satisfaction and salespeople economic’s satisfaction mediates the relationship effect between subjective sales performance and salespeople’s non-economic satisfaction.

This two-part study aims to contribute to the body of knowledge on team development by examining the development of self-managing teams (SMTs) in healthcare. Based on an exploration of the team development literature, a perspective on SMT development was created, which suggested that SMTs develop along a non-sequential pattern of three processes–team management, task management and boundary management and improvement–that is largely the result of individual, team, organizational and environmental-level factors.

The perspective on SMT development was assessed in a Dutch mental healthcare organization by conducting 13 observations of primary mental healthcare SMTs as well as 14 retrospective interviews with the self-management process facilitator and advisors of all 100 primary mental healthcare SMTs.

Empirical results supported the perspective on SMT development. SMTs were found to develop along each of the three defined processes in a variety or possible patterns or simultaneously over time, depending on many of the identified factors and three others. These factors included individual human capital, team member attitudes and perceived workload at the individual level, psychological safety, team turnover, team size, nature of the task and bureaucratic history at the team level, and management style and material and social support at the organizational level.

This study provides a non-sequential model of SMT development in healthcare, which healthcare providers could use to understand and foster SMTs development. To foster SMT development, it is suggested that cultural change need to be secured alongside with structural change.

Even though various team development models have been described in the literature, this study is the first to indicate how SMTs in the healthcare context develop toward effective functioning.

Presents two sequential preventive maintenance (PM) models. The first model can be viewed as an extension of Nakagawa’s model where the age reduction of the system is assumed to depend on the level of PM activities. Linear and nonlinear relationships between age reduction and PM level have been considered. In the second model, a sequential PM model is developed in which PM intervals are defined such that the integrated hazard rate over each interval is the same for all intervals. This restriction reduces the number of decision variables from N + 2 to only three decision variables, where N is the number of intervals between PMs. The cost difference between these two models was very small for the examples solved. This provides evidence that the integrated hazard rate restriction provides a very good approximation of this sequential PM model. This suggests that the integrated hazard rate condition may be used to reduce greatly the number of decision variables at the expense of a reasonable increase in the expected total cost.

Existing literature clearly documents the importance of new product development to success of a manufacturing firm. Many examples of generic models of the process, including sequential, concurrent, and multiple convergent models, can be found. However, these models are of insufficient detail to provide an adequate foundation for redesigning the apparel product development process. The no‐interval coherently phased product development (NICPPD) model for apparel introduced in this paper documents apparel product development as a six phase process with multiple convergent points and coherently phased divisions. The NICPPD model provides for developing both product lines and individual products, developing seasonal lines and multiple seasons annually, and use of alternative development strategies including original design development, knock‐offs or take‐offs, and modification of existing products. Multiple applications for use of the NICPPD model by both researchers and practitioners in examining and improving the apparel product development process are identified.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

In the presence of a low rate of investment in research and development in the fish industry, the Moroccan government launched in 2009 a new fisheries program which directs fish processing companies towards a non-price competitiveness strategy. These companies are driving to establish a modernized value chain that supports product innovation in its performance generation. This study therefore aims at measuring the impact of this value chain on the performance of a new product taking into account the early stages of development, namely prototyping.

First, the authors tried to collect the data in a dichotomous qualitative form for the structure of the innovation process which reflects the measure of elapsed time for each stage of the innovation process in the two cases, namely, sequentiality and parallelism of the steps. The authors then addressed a second time to the quality managers to provide them with quantitative data. Nevertheless, the evaluation of the improvement of the innovative product had remained qualitative.

The study shows that there is a positive and significant relationship between the partially parallel structure and the internal improvement objectives of the new prototype.

The main limitation of this study was the very small sample of firms operating in innovation, which did not allow us to apply a parametric analysis such as logistic or linear regression according to a normal law on a sufficient number of observations according to the transversal approach. As theoretical implications of this study, Davila et al. (2006) argue that to succeed in a product development process, it must be possible to measure the resulting performance. Assessing performance in the product development process is particularly important for managers and decision makers to address key management issues such as “what we do”, “what we have learned” and “what should we do in the future” (Tatikonda, 2007).

The empirical implications of this study have shown that accelerating the execution of innovation activity is enormously favored to increase the performance of the innovative product over the medium term. This will enable the company to be efficient in terms of market entry time with good quality and as soon as possible mainly in the early stages of development of the new product.

Compared with previous studies, the originality of this study is to answer two inadequacies in the theory of performance of the new product, namely, the objective/quantitative nature of the practice measured in the innovation process and the use of a holistic approach based on the performance indicators of the innovative product at each stage of the innovation process.

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.

In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.

The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.

The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.