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This chapter explains the impact containerisation has on the various partners of the global supply chain and the challenges companies encounter and the solutions they use in dealing with empty container repositioning.

The phenomenon of imbalanced container flows and its impact on shipping lines, shippers, container haulage companies, port development and the economy are presented. Special attention is given to explain the many solutions companies use to reduce the impact of empty container repositioning, hence tracing out the past research that led to these solutions and pointing to potentially new research directions in the future.

Because of the widespread use of containerisation and the imbalanced container flows that results from globalisation, empty container repositioning will be an ongoing issue for the maritime logistics industry. Many solutions are being used, but there is room for improvement and more research is needed.

Empty container repositioning is an important issue but has not been deemed as such in the literature. This chapter explains the reasons it is important and that its impact is not limited to shipping lines only but affects the whole supply chain.

The ability to appreciate the sensation of force and torque that is experienced by the slave device in a teleoperator system is essential for achieving remote manipulations such as assembly and disassembly, telesurgery, and remote handling of delicate and dangerous substances. This paper describes an intelligent teleoperator system that not only provides high quality force feedback information in teleoperation but also additional information about the contact formation between the slave device and the environment is presented to the operator. This paper also discusses the design of the teleoperator system and introduces its architecture. Experimental study using the developed teleoperator system is presented that highlights its performance in real life applications.

To present the design and control of a low cost, two‐wheeled, self‐balancing robot for detecting and reporting fire and intruders in a small home/office setting.

Implement an intelligent control system to make up for instabilities of low‐cost components.

Biologically inspired suppression system can effectively eliminate undesired sensor noises.

Improving self‐balancing robots' tolerant to impulsive forces from the sides allows them to be designed with smaller front‐prints.

Inherited high maneuverability together with miniature design enables self‐balancing robots to navigate smoothly through tight space.

Demonstrates the control of a dynamically unstable system can be controlled using a simple suppression mechanism.

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.

A neural network controller is proposed for the motion control of robot manipulators with force/torque feedback signals. This controller is trained with reinforcement learning algorithms and a model is extracted from the synaptic weights within the neural network. This model is continuously refined by the feedback signals to ensure its validity even in a stochastic and non‐stationary environment. With this model and the real‐time force/torque feedback data, the robot can acquire a fine skill for a particular assembly task for which it is trained.

The purpose of this paper is to design a model-based bilateral teleoperation method to improve the feedback force and velocity/position tracking for robotic-assisted tasks (such as palpation, etc.) under constant and/or varying time delay with environment dynamic property. Time delay existing in bilateral teleoperation easily destabilizes the system. Proper control strategies are able to make the system stable, but at the cost of compromised performance. Model-based bilateral teleoperation is designed to achieve enhanced performance of this time-delayed system, but an accurate model is required.

Viscoelastic model has been used to describe the robot tool-soft tissue interaction behavior. Kevin-Boltzmann (K-B) model is selected to model the soft tissue behavior due to its good accuracy, transient and linearity properties among several viscoelastic models. In this work, the K-B model is designed at the master side to generate a virtual environment of remote robotic tool-soft tissue interaction. In order to obtain improved performance, a self perturbing recursive least square (SPRLS) algorithm is developed to on-line update the necessary parameters of the environment with varying dynamics.

With fast and optimal on-line estimation of primary parameters of the K-B model, the reflected force of the model-based bilateral teleoperation at the master side is improved as well as the position/velocity tracking performance. This model-based design in the bilateral teleoperation avoids the stability issue caused by time delay in the communication channel since the exchanged information become position/velocity and estimated parameters of the used model. Even facing with big and varying time delay, the system keeps stably and enhanced tracking performance. Besides, the fast convergence of the SPRLS algorithm helps to track the time-varying dynamic of the environment, which satisfies the surgical applications as the soft tissue properties usually are not static.

The originality of this work lies in that an enhanced perception of bilateral teleoperation structure under constant/varying time delay that benefits robotic assisted tele-palpation (time varying environment dynamic) tasks is developed. With SPRLS algorithm to on-line estimate the main parameters of environment, the feedback perception of system can be enhanced with stable velocity/position tracking. The superior velocity/position and force tracking performance of the developed method makes it possible for future robotic-assisted tasks with long-distance communication.

As the new emerging workforce, Generation Y (Gen Y) is said to be demanding, influential and possessing strong bargaining power. This study examines the impact of corporate social responsibility (CSR) on employee retention among Gen Y in the accounting profession. CSR is widely researched subject due to its applicability in multidisciplinary fields and industries. This research intends to investigate the nexus between CSR and human capital disciplines. It employs Carroll's pyramid of CSR as the main theoretical framework to establish its relationship with talent retention among Gen Y employees. This study has a threefold aim: (1) to determine the level of CSR awareness, (2) to determine the relationship between CSR dimensions and talent retention and (3) to examine the effect of CSR dimensions on talent retention.

The paper opted for an exploratory study using the structured questionnaire. A total of 377 Gen Y accountants who are currently working in accounting firms located in Klang Valley, Malaysia, were involved as respondents. Data were analyzed using descriptive, correlation and regression analyses to answer the research objectives.

The paper provided empirical insights about the impact brought by CSR practices in financial-based firms on employee retention. It was found that all CSR elements, as suggested by Carroll, have a significant relationship with employees’ retention. The interaction between the CSR elements and employee retention accounts for 16% of the research model. Based on the multiple regression analysis, it was found that only two CSR elements are the significant predictors of employee retention among Gen Y in the case of financial-based firms in Malaysia.

This research covers Gen Y employees in accounting firms; thus, generalization is not applicable to other generations. Besides, the predictors of the research study utilize Carroll’s pyramid of CSR. Therefore, future research studies are encouraged to validate the research model into other sectors. Other models of CSR could also be used.

This paper includes implication for the organization to understand employee retention practices on Gen Y who are currently dominating the workforce.

This paper fulfills an identified need to study how CSR practices could enhance employee retention among Gen Y in the organization.

The flexibility of an automated manufacturing system (AMS) is largely determined by the architecture of its supervisory software. The AMS supervisory software should be compatible enough to allow re‐configuration, maintenance, and upgrading to be done at a reasonable cost, so as to prolong the life span of the AMS, and to maintain its value and success. In this connection, the application of software engineering methodologies is to introduce better management of AMS software development, with a view to providing some structure and formality to express, analyse, and produce them. This paper presents an object‐oriented methodology to address the task of AMS supervisory software development. The effectiveness of the methodology is examined in an extensive case study of the development of the supervisory software for a flexible manufacturing cell (FMC) that contains a robot and an AGV.

The purpose of this paper is to present a systematic review and analysis of existing research articles on new product development (NPD) published in the 12-year period starting from 1998 to 2009.

To explore the articles related to NPD, four key words namely new product, product design, product development and product innovation were used in combination of title or abstract or keyword of the articles through several knowledge databases. The abstracts of journal papers were read and a decision as to whether article belongs to any NPD research issue or not was made. In total, 1,127 research articles were categorized systematically and then analyzed on various principal NPD information groups.

Analysis of selected articles led to a certain level of dispersion in the publication of NPD research in different journals. It is found that more attention needs to be on knowledge and creativity management, communication and information transfer in any NPD process.

By observing extended literature from authors reviewing articles from various journals, growth in research, and variety of topics covered in NPD, a broad systematic multi journal review of NPD literature is clearly overdue. The authors have developed a comprehensive listing of publications on NPD where they have classified the surveyed papers according to various principal NPD information groups like: published year, NPD research stream, type of organization studied (industrial/consumer/service), level of innovation (high/moderate/low), NPD focus on frameworks, performance perspective (success, failure or both), NPD research design (conceptual/empirical and qualitative/quantitative) and NPD relevant best practice element. Based on the classification scheme, the issues were analyzed from the system's perspective and their implications to NPD research.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.