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The national logistics policy report published by Ministry of Land, Infrastructure and Tourism in 2011 proposes to establish international logistics strategy teams in 10 different regions around Japan to satisfy the increasing demand for advance transportation infrastructure and stay competitive in Asia Pacific. The globalization of world economies creates many opportunities as well as challenges for international logistics companies to gain more business chances in this changing environment. The purpose of this paper is to improve service quality of international logistics companies and explores the quality function deployment in terms of quality evaluation method. The logistics service is particularly characterized by offering a series of transport solution and including other logistics activities. The major customers of the logistics services are the industrial clients. The customer satisfaction is key managerial mission since the competitiveness is a growing issue in this industry. The quality function deployment is one of the unique procedures to expose the requirements of customer and transform them into managerial tasks by cross correlation analysis between requirements and technical measures. The empirical study is performed to investigate service quality of the logistics industry by focusing on a group of leading logistics companies.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

Innovation drives new product development, novel approaches to our professional and personal lives, and entrepreneurial activity in our communities. Women entrepreneurs are increasing in numbers and are becoming more visible across myriad domains. A growing number of scholars are focusing on better understanding women entrepreneurs’ unique approach to developing an entrepreneurial enterprise. However, the research suggests that entrepreneurship is still (mis)perceived as being traditionally masculine, with the number of men outnumbering the number of women entrepreneurs. Using a model of innovation consisting of the three distinct tasks of idea generation, idea promotion, and idea realization (Janssen, 2000; Scott & Bruce, 1994), this chapter explores the influence of gender on these various domains, followed by suggestions for future research and practical implications for women entrepreneurs today.

This paper aims to present a review on utilizing shape memory technology (SMT) for active assembly/disassembly, i.e. assembly/disassembly without physically touching.

The fundamentals behind the shape memory effect (SME) in materials, in particular shape memory alloys (SMAs) and polymers, which are the cornerstones of SMT, are introduced, together with the possible approaches to implement this effect in active assembly/disassembly. Example applications for not only active assembly/ disassembly, but also programmed active disassembly are presented.

The advantages of utilizing SMT over conventional assembly/disassembly techniques are identified.

The paper introduces the fundamentals behind the SME and the basic approaches to implement the SMT in not only active assembly/disassembly, but also programmed active assembly.

Although employee race has been an actively investigated area of scientific inquiry for decades, a thorough and informed understanding of the role of race in the organizational sciences has eluded us for a number of reasons. The relationship of race and stress in organizations is a prime example of this neglect and deficiency in our knowledge base, as little work has been done in this area. We attempt to address this limitation in the literature by proposing an inductively derived, review-centric framework that attempts to articulate the multiple intermediate linkages that explain the process dynamics taking place in the relationship between employee race and health and well-being in organizations. We argue that socialization processes, social networks, information and resource access, and mentoring contribute to distance and differences between racial minorities and nonminorities concerning control, reputation, performance, and political understanding and skill, which in turn, creates barriers to success, and increased stress and strain for racial minorities. The implications of this framework along with directions for future theory and research are discussed in this chapter.