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The purpose of this paper is to study the role of organizational forgetting in the impact of strategic thinking on the implementation of an organizational excellence model. Furthermore, the factors with main effects on the implementation success of the organizational excellence model are investigated. The two main causes of organizational forgetting, including purposefulness and randomness, along with the three main factors of strategic thinking (vision, creativity and systematic thinking) also are explored. Enablers and results, which are the two key parts of an organizational excellence model are considered as well.

A model based on structural equations is designed, in which organizational forgetting factors, strategic thinking measures and main parts of a business excellence model are incorporated based on the literature. A total of 297 Iranian companies in which an organizational excellence model had been implemented are selected for investigation. A questionnaire is designed and distributed among the experts, middle managers and top managers of these companies. Based on Cochran’s formula, the sample size of 168 is obtained, for which the response rate is 100 percent. Main statistical measures such as χ² ratio to degree of freedom, non-soft fitness index (RMSEA), fitness index (GFI) and modified fitness index (AGFI) are used to assess the performance of the proposed model.

According to the results of the statistical significance tests, the role of organizational obsessive mediators in the establishment of the organizational excellence model has been largely confirmed. Furthermore, the mediator role of organizational forgetting in the final impact of strategic thinking on implementing an organizational excellence model has been widely endorsed. Failure to use knowledge from learning, the inability of a company in coding and documenting knowledge and lack of incentives to share it are the most important factors in the forgetting of knowledge in companies.

As top managers, middle managers and experts are hard to reach due to the wide geographical spread of the organization under study, a questionnaire is designed and distributed among them. The impact of organizational forgetting on other quality management systems such as ISO 9001 and ISO 4001 needs another research to be conducted in the future.

Using new experiences, increasing the competency of employees and managers experience through organizational learning, employee and managerial assessment and organizational strategy assessment are the main practical methods for considering organizational forgetting in the process of implementing organizational excellence models.

This research addresses organizational forgetting besides strategic thinking as joint main roles for implementing organizational excellence, whereas previous research works only considered strategic thinking as a factor. Furthermore, a structural equation model is developed for appraisal of effect of different factors.

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Learning as the way in which labor acquire new knowledge and skills has important strategic implications for the competitive advantage of an organization. The purpose of this paper is to present an agent-based modeling (ABM) approach to investigate the learning behavior of workers. The effect of interactions among different workers as well as the factors affecting the workers’ learning behavior is assessed using the proposed ABM approach.

For this purpose, the processes through which the competency value of worker is changed are understood and the workers’ learning behavior is modeled, taking account of various influencing factors such as knowledge flow, social ability to teach and forgetting factor.

The proposed model is implemented on a real steel structure project to evaluate its applicability and performance. The variation in the competency value of different workers involved in the project is simulated over time taking account of all the influencing factors using the proposed ABM approach.

In order to assess the effect of interactions among welders as well as the welders’ characteristics on their learning behavior, the competence value of different welders is evaluated.

This research presents an ABM approach to investigate the workers’ learning behavior. To evaluate the performance of the proposed ABM approach, it was implemented on a real steel structure project. The learning behavior of different welders (agents) was simulated taking account of their interactions as well as the factors affecting the welders’ learning behavior. The project involved the welding of a 240-ton steel structure. The initial project duration was estimated as 100 days. In this project, it has been planned to execute the welding process using three different welders namely welder A, B and C.

The wind turbine (WT) is a complex system subjected to wind disturbances. Because the aerodynamics is nonlinear, the control is thus challenging. For the variation of wind speed when rated power is delivered at rated wind speed, the power is limited to the rate by the pitching of the blades of the turbine. This paper aims to address pitch control with the WT benchmark model. The possible use of appropriate adaptive controller design that modifies the control action automatically identifying any change in system parameters is explored.

To deal with pitch control problem when wind speed exceeds the rated wind speed of the WT, six digital self-tuning controller (STC) with different structures such as proportional integral (PI), proportional derivative (PD), Dahlin’s, pole placement, deadbeat and Takahashi has been taken herein. The system model is identified as a second-order autoregressive exogenous (ARX) model by three techniques for comparison: recursive least square method (RLS), RLS with exponential forgetting and RLS with adaptive directional forgetting identification methods. A comparative study of three identification methods, six adaptive controllers with the conventional PI controller and sliding mode controller (SMC), are shown.

As per the results, the best improvement in control of the output power by pitching in full load region of benchmark model is achieved by self-tuning PD controller based on RLS with adaptive directional forgetting method. The adaptive control design has a future in WT control applications.

A comparative study of identification methods, six adaptive controllers with the conventional PI controller and SMC, are shown here. As per the results, the best improvement in control of the output power by pitching in the full load region of the benchmark model has been achieved by self-tuning PD controller. The best identification method or the system is RLS with an adaptive directional forgetting method. Instead of a step input response design for the controllers, the controller design has been carried out for the stochastic wind and the performance is adjudged by the normalized sum of square tracking error (NSSE) index. The validation of the proposed self-tuning PD controller has been shown in comparison to the conventional controller with Monte-Carlo analysis to handle model parameter alteration and erroneous measurement issues.

This monograph presents a thorough examination of the phenomena of “threshold” levels of advertising activity and the “wearout’ of advertisements and/or campaigns. These are seen as corresponding to the management questions “How little can we spend/How infrequently can we advertise?” and “How much is too much/How infrequently is too little?” In the first section the relevant literature on, or related to, the two issues is reviewed. Section 2 describes a survey aimed at establishing current beliefs in the existence of the phenomena, the practices resulting from these beliefs, and the data which support them. Finally, Section 3 offers an overview on the managerial issues involved in decisions concerning threshold or wearout risks in advertising. It is suggested that wasted expenditure may be occurring in advertising because the believed levels of threshold and wearout are both too high.

This paper aims to propose and evaluate an intrusion sensitivity (IS)-based approach regarding the detection of pollution attacks in collaborative intrusion detection networks (CIDNs) based on the observation that each intrusion detection system may have different levels of sensitivity in detecting specific types of intrusions.

In this work, the authors first introduce their adopted CIDN framework and a newly designed aggregation component, which aims to collect feedback, aggregate alarms and identify important alarms. The authors then describe the details of trust computation and alarm aggregation.

The evaluation on the simulated pollution attacks indicates that the proposed approach is more effective in detecting malicious nodes and reducing the negative impact on alarm aggregation as compared to similar approaches.

More efforts can be made in improving the mapping of the satisfaction level, enhancing the allocation, evaluation and update of IS and evaluating the trust models in a large-scale network.

This work investigates the effect of the proposed IS-based approach in defending against pollution attacks. The results would be of interest for security specialists in deciding whether to implement such a mechanism for enhancing CIDNs.

The experimental results demonstrate that the proposed approach is more effective in decreasing the trust values of malicious nodes and reducing the impact of pollution attacks on the accuracy of alarm aggregation as compare to similar approaches.

While scheduling and transporting emergency materials in disasters, the emergency materials and delivery vehicles are arriving at the distributing center constantly. Meanwhile, the information of the disaster reported to the government is updating continuously. Therefore, this paper aims to propose an approach to help the government make a transportation plan of vehicles in response to the disasters addressing the problem of material demand and vehicle amount continual alteration.

After elaborating the features and process of the emergency materials transportation, this paper proposes an emergency materials scheduling model in the case of material demand and vehicle amount continual alteration. To solve this model, the paper provides the vehicle transportation route allocation algorithm based on dynamic programming and the disaster area supply sequence self-learning algorithm based on ant colony optimization. Afterwards, the paper uses the model and the solution approach to computing the optimal transportation scheme of the food supply in Lushan earthquake in China.

The case study shows that the model and the solution approach proposed by this paper are valuable to make the emergency materials transportation scheme precise and efficient. The problem of material demand and vehicle amount changing continually during the process of the emergency materials transportation is solved promptly.

The model proposed by this paper improves the existing similar models in the following aspects: the model and the solution approach can not only solve the emergency materials transportation problem in the condition of varying demand and vehicle amount but also save much computing time; and the assumptions of this model are consistent with the actual situation of the emergency relief in disasters so that the model has a broad scope of application.

The purpose of this paper is to explore scenario development (SD) as a method for engaging known challenges in collaborative research projects, i.e. SD is the construct under investigation.

Criticism of the dominant, rational approach to project management (PM) and its underlying hypotheses highlights a considerable PM research gap for research projects (research problem). The authors undertake a six-step constructive research approach to investigate if SD (the construct) constitutes a fruitful method to support the management of collaborative research projects. A two-part literature review summarizes known challenges in collaborative research projects and introduces the history and application of SD methodology. The work includes participatory action research (PAR) in two case studies, constituting a qualitative research method.

The authors found the SD method to be useful for structuring and analyzing intuitive project processes. However, using SD in the management of single projects presents some fundamental challenges. SD, like PM, struggles with issues related to myopic decisions, a “predict and provide” attitude with clear aspects of path dependency in the project front-end as well as inconsistent and/or missing identification of success criteria among different stakeholders.

This paper does not provide any comprehensive, normative account of scenario techniques or compare SD with other foresight and future studies methods. Although PAR is in itself a research method that demands systematic description and execution, the focus of this paper is the overall constructive research approach.

The paper offers a broadened repertoire of methods to describe and analyse project stakeholder situations (collaborative aspects) and to structure and balance the need for both rational and intuitive project processes (research aspects). The SD method also supports development of graphical storylines and facilitates the use of influence diagrams, event trees and cost/benefit analysis.

Although PM literature contains several references to SD, the practical application of SD at single-project level has, to the authors’ knowledge, never been described in the PM literature.

The purpose of this study is to examine the mediation role of intentional organizational forgetting in the relationship between organizational learning and innovation management according to faculty members’ opinions.

Research was designed as a relational survey model. The population of the study consisted of faculty members who work at X University, Y University and Z University during 2019–2020 academic year. The sample consisted of 524 faculty members who were selected by using stratified sample technique from the population. Data of the study was collected with organizational learning scale, organizational forgetting scale and innovation management scale. In the analysis of the research data, descriptive statistics, correlation analysis, structural equation modeling and bootstrapping method were applied.

According to the results of the study, it was found statistically meaningful and positive relationships between organizational learning, innovation management and intentional forgetting in higher education institutions with respect to faculty members’ opinions. Moreover, according to the results of structural equation modeling, it was found that intentional forgetting had a partial mediating effect in the relationship between organizational learning and innovation management. Finally, according to the results of bootstrapping analysis, indirect effects were found to be significant.

Based on research results, it may be recommended for practitioners that higher education institutions implement both organizational learning processes and intentional forgetting processes effectively at the same time to become a more innovative organization.

The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.

In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.

It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.

The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.