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This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

To reveal the effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations, the leadership rapport orientation is subdivided into two types of values-based/instrumental rapport orientation. The mechanism of supervisor developmental feedback in mediating between leadership rapport orientation and employees' innovative behaviour and the moderating effect of ambidextrous environments is explored. This paper aims to discuss the aforementioned objective.

Leadership rapport orientation is divided into value-based and instrumental rapport orientation to reveal effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations.

The results show that the values-based (instrumental) rapport orientation of leaders impacts employees' innovative behaviour positively (negatively).

Leaders' values-based/instrumental rapport orientation indirectly influences employees' innovative behaviour through supervisor developmental feedback, which positively moderates the relationship between the values-based or instrumental rapport orientation of leaders and employees' innovative behaviour and further moderates the partially mediating role of supervisor developmental feedback between leaders' values-based/instrumental rapport orientation and employees' innovative behaviour.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.

To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.

The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.

In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.

A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

The purpose of this paper is to propose a theoretical framework to illustrate the archival knowledge applied by archivists in their personal archiving (PA) and the mechanism of the application of archival knowledge in their PA.

The grounded theory methodology was adopted. For data collection, in-depth interviews were conducted with 21 archivists in China. Data analysis was performed using the open coding, axial coding and selective coding to organise the archival knowledge composition of PA and develops the awareness-knowledge-action (AKA) integration model of archival knowledge application in the field of PA, according to the principles of the grounded theory.

The archival knowledge involved in the field of PA comprises four principal categories: documentation, arrangement, preservation and appraisal. Three interactive factors involved in archivists' archival knowledge application in the field of PA behaviour: awareness, knowledge and action, which form a pattern of awareness leading, knowledge guidance and action innovation, and archivists' PA practice is flexible and innovative. The paper underscored that it is need to improve archival literacy among general public.

The study constructs a theoretical framework to identify the specialised archival knowledge and skills of PA which is able to provide solutions for non-specialist PA and develops an AKA model to explain the interaction relationships between awareness, knowledge and action in the field of PA.

This paper aims to understand the impact of smart working on employees’ quality of life. The service-dominant logic indicates that key actors, including employees, are resource integrators. This empirical study contributes to the current knowledge of transformative service by expounding on smart working practices and their relationship with self-efficacy and, ultimately, quality of life of employees.

This study contends that smart working enhances employees’ self-efficacy. The current research uses conservation of resources (COR) theory as a basis in hypothesizing that self-efficacy serves as a key resource, which can stimulate workplace creativity, job satisfaction and quality of life. An onsite survey is conducted among employees who have been involved in smart working in the travel agency context.

Smart working has a positive effect on self-efficacy, which positively influences workplace creativity, job satisfaction and quality of life. Workplace creativity enhances job satisfaction but does not influence quality of life. Job satisfaction boosts quality of life, which is important to tourism services and their employees amid various crises.

Consistent with COR theory, the immediate recommendation to managers is to invest in smart working, which can lead to employees’ creativity, job satisfaction and quality of life.

To the best of the authors’ knowledge, this empirical study is the first to assess the viability of smart working in the travel agency context. Smart working offers positive outcomes, such as convenience for employees to work independently, improving workplace creativity, job satisfaction and quality of life. Travel agencies are mainly categorized as small and medium enterprises (SMEs). In the future, SMEs may also introduce smart working that can eventually become their competitive advantage to retain talented employees.

旅行社中的智慧办公与员工的生活质量

本文旨在了解智慧办公对员工的影响.服务主导逻辑表明, 员工等关键人物是资源整合者。本文通过实证研究揭示了智慧办公实践及其与员工自我效能和生活质量的关系, 对现有转型服务知识做出贡献。

本研究认为智慧办公可以提高员工的自我效能。基于资源守恒理论(COR), 本研究假设自我效能是激发职场创造力、工作满意度和生活质量的关键因素, 对旅行社中参与智慧办公的员工进行了现场调查。

智慧办公对自我效能有正向影响, 同时自我效能对职场创造力、工作满意度和生活质量均产生正向影响。职场创造力能够提高工作满意度, 但对员工的生活质量不起作用。另外, 工作满意度有利于提升生活质量, 这对面临各种危机的旅游企业和员工具有重要意义。

根据COR理论, 即刻建议管理者投资推行智慧办公, 从而提升员工的创造力, 工作满意度和生活质量。

这一实证研究首次尝试评估旅行社中推行智慧办公的可行性。智慧办公可带来多种积极效果, 如为员工独立工作提供便利, 提升员工的职场创造力、工作满意度和生活质量等。旅行社属于中小型企业(SMEs)。未来, 中小型企业同样可尝试引入智慧办公, 这将成为其留住优秀员工的竞争优势。

关键词：智慧办公, 公平评估, 自我效能, 创造力, 自主, 生活质量

文章类型： 研究型论文

El trabajo inteligente en las agencias de viajes y la calidad de vida de los empleados

Resumen

Este artículo tiene como objetivo entender el impacto del trabajo inteligente en la calidad de vida de los empleados. Basándose en la lógica de servicio dominante, los actores clave, incluidos los empleados, son integradores de recursos. Este estudio empírico contribuye al conocimiento actual de lo que constituye un servicio transformador al exponer prácticas laborales inteligentes y su relación con la autoeficacia y, en última instancia, la calidad de vida de los empleados.

Este estudio sostiene que el trabajo inteligente mejora la autoeficacia de los empleados. Plantea la hipótesis de que la autoeficacia sirve como un recurso clave que puede estimular la creatividad en el lugar de trabajo, la satisfacción laboral y la calidad de vida utilizando la teoría de la conservación de recursos. Se ha realizado una encuesta in situ entre los empleados que han llevado a cabo trabajo inteligente en el contexto de la agencia de viajes.

El trabajo inteligente tiene un efecto positivo en la autoeficacia que influye positivamente en la creatividad en el lugar de trabajo, la satisfacción laboral y la calidad de vida. La creatividad en el lugar de trabajo mejora la satisfacción laboral, pero no influye en la calidad de vida. La satisfacción laboral mejora la calidad de vida, lo que es importante tanto para los servicios turísticos como para sus empleados durante las crisis.

De acuerdo con la teoría de la conservación de recursos, la recomendación inmediata a los gerentes es invertir en un trabajo inteligente que pueda conducir a la creatividad, la satisfacción laboral y la calidad de vida de los empleados.

Este estudio empírico es el primero del que tengamos constancia en evaluar la viabilidad del trabajo inteligente en el contexto de la agencia de viajes. El trabajo inteligente ofrece beneficios, como la posibilidad de que los empleados trabajen de forma independiente, mejorando así la creatividad profesional, la satisfacción laboral y la calidad de vida. Las agencias de viajes se incluyen principalmente en la categoría de pequeñas y medianas empresas (pymes). En el futuro, las pymes también pueden introducir el trabajo inteligente, que puede llegar a convertirse en su ventaja competitiva para retener a los empleados con talento.

Trabajo inteligente, Equidad en las valoraciones, Autoeficacia, Creatividad, Autonomía, Calidad de vida