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The purpose of this paper is to study the impact of learning effect and demand uncertainty on aggregate production planning (APP), provide practitioners with some important managerial implications for improving production planning and productivity.

Motivated by the background of one labour-intensive manufacturing firm – a mosquito expellant factory – an APP model considering workforce learning effect and demand uncertainty is established. Numerical example and comparison with other two models without considering learning and uncertainty of demand are conducted.

The result shows that taking into account the uncertain demand and learning effect can reduce total production cost and increase flexibility of APP.

Managerial implications are provided for practitioners with four propositions on improving workforce learning effect, i.e. emphasizing employee training, combing individual and organizational learning and reduction of forgetting effect.

This paper has practice value in improving APP in labor-intensive manufacturing.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

This study aims to explore the effect of expatriate supervisors’ managerial coaching behavior on local subordinates’ learning effects through the mediating role of subordinates’ thriving at work under the boundary condition of expatriate supervisors’ cultural intelligence.

This study collected the data form 230 Zambian subordinates and their immediate expatriate supervisors working in the Chinese company in Zambia. Regression analyses and bootstrapping analyses were used to test the authors’ hypothesis.

The results indicated that expatriate supervisors’ managerial coaching behavior was positively related to local subordinates’ learning effects. In addition, the study also found that local subordinates’ thriving at work mediated the linkage between managerial coaching behavior and learning effects. And expatriate supervisors’ cultural intelligence moderated the indirect relationship between managerial coaching behavior and learning effects via thriving at work, such that the indirect effect was stronger for expatriate supervisors with high rather than low cultural intelligence.

This study contributes to a better understanding of how expatriate supervisors’ managerial coaching behavior influences local subordinates’ learning effects by investigating the mediating effect of thriving at work on the managerial coaching behavior–learning effects link. In addition, the study deepens the understanding of the boundary condition of the associations between managerial coaching behavior and subordinates’ learning effects in a cross-cultural context by investigating the moderating effect of expatriate supervisors’ cultural intelligence.

This study aims to investigate whether the contract manufacturer (CM) should take the first-mover advantage in the end-product without supplying core components to the original equipment manufacturer (OEM) immediately, or should fully squeeze the benefit of the learning effect through an amplified production quantity by letting the OEM enter the end-product market early.

The authors propose a two-period model for a supply chain consisting of a CM and an OEM where the CM has four alternative entry strategies concerning it competition to the OEM in the end-product market. For each strategy, the authors derive the equilibrium solutions of the two firms using a backward approach. Comparison leads to the CM’s final choices among the four strategies.

For both CM and OEM, the monopoly and the first-entry strategies will be dominated by either the post-entry or the simultaneous-entry strategy, and thus, their preferred strategy is chosen from the latter two. Regarding the two firms choices between the post- and simultaneous-entry strategy, the CM prefers the post-entry strategy when the OEMs brand premium is at a moderate level, whereas the OEM prefers the post-entry strategy when its brand premium is low, and the learning effect can amplify the interval for the CMs adopting the post-entry strategy as well as changes the interval for the OEMs preference related to the two strategies.

This paper is the first one to explore the optimal strategy for a CM to maximize its profit in a co-opetitive supply chain situation with a CM and an OEM. The authors believe that our paper contributes to both literature and the market.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Social learning theory specifically acknowledges that most human behaviour is learned observationally through modelling. The focus of this approach has been teaching leadership across formal and informal settings. This and the behavioural focus is what distinguishes social learning theory from others as a leadership theory. However it will not become a leadership theory unless the behaviours to be imparted to future leaders are outlined. This has not been done in the social learning context. However, because of its growing importance as a theoretical foundation for the fields of psychology and organisational behaviour as a whole, a social learning approach to leadership would seem to have potential for the future.

The purpose of this paper is to investigate the relationship between altruistic leader behaviors, organizational learning capability and organizational performance.

The sampling frame consists of several databases or listings of business that consider people as a key element of the organization and are considered by their employees to be good firms to work for or organizational environments where human resources management has high priority (n=251). The authors use structural equation modeling to test if the relationship between altruistic leader behaviors and organizational performance is mediated by organizational learning capability.

Results suggest that organizational learning capability fully mediates between altruistic leader behaviors and organizational performance. Thus, organizational learning capability plays a key role in explaining how altruistic leader behaviors affect organizational performance, essentially because it facilitates the creation of a creative, participatory and dialogue-based environment that promotes organizational learning.

The database used in the study is very heterogeneous. Future research might delimit the database by organization size or sector. Qualitative studies may also improve our understanding of the relationships studied and enable other concepts to be included.

This study provides evidence of the positive relationship between altruistic leaders and performance. However, recruiting and fomenting altruistic leaders is not sufficient on its own to improve performance, and should be accompanied by implementing other facilitating factors of organizational learning such as dialogue or experimentation.

In recent years some studies have linked leadership with organizational learning. However, this is one of the first studies to concentrate on altruistic leader behaviors as such, a concept that has received scant mention in the literature despite its importance in a number of leadership styles, and its relevance today as an alternative to the egotistic leader. The authors offer empirical evidence of the role of altruistic leader behavior as an antecedent of organizational learning capability and subjective measures of performance.

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support computer-based adaptive instruction for teams of learners. Several concepts related to team tutoring are defined along with team processes, and fundamental tutoring concepts are provided including a description of the learning effect model (LEM), an exemplar describing interaction between learners and ITSs with the goal of realizing optimal tutor decisions. The challenges noted herein are closely related to the LEM and range from acquisition of learner data, synthesis of individual learner and team state models based on available data, and tutor decisions which center on optimizing strategies (recommendations) and tactics (actions) given the state of the learner, the team, and the conditions under which they are being instructed, the environment. Finally, we end this chapter with recommendations on how to use this book to understand and design effective ITSs for teams.