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The purpose of this paper is to investigate the impact of radio frequency identification (RFID) deployment at an airport baggage‐handling system (BHS).

The impact of number of RFID readers at different power levels with varying conveyor (i.e. baggage‐handling conveyors) speeds on timely delivery of baggage is studied via simulation. The layout of the BHS at the Hong Kong International Airport and data pertinent to its RFID deployment in 2005 are used to build the simulation model. The RFID read logic is based on the equations defined as a function of the number of tags and the time the tags spend in the interrogation zone for each reader in order to capture possible read‐rate issues realistically.

The identification capability of the BHS studied in this paper is a result of its combined ability to identify tags via RFID technology on straight and circulating conveyors, as well as at the manual recovery station for unidentified bags on circulating conveyors. Overall, timely delivery of bags to gates, as a performance metric, increases as the identification capability is improved. The controllable factors that affect the identification capability are the conveyor speed, which determines the time a tag stays in the interrogation zone; the reader antenna power level, which determines the size of the interrogation zone; and the number of reader antennas in the system that increases the likelihood of not missing tags. This paper shows that “the higher the number of reader antennas and the higher the power level on them, the better” approach is not correct.

Unlike typical simulation studies related to RFID deployment where read‐rate issues are considered to be non‐existent, this paper captures read rate in a realistic manner in the simulation model by incorporating the effect of number of RFID tags in the interrogation zone and time that RFID tags spend in the interrogation zone due to baggage conveyor speed. Such a simulation approach can be used as a system design tool in order to investigate the impact of RFID‐specific parameters on system‐level performance.

The purpose of this paper is to present the development of an architecture for real‐time routing of automated guided vehicles (AGV) in a random flexible manufacturing system (FMS).

AGV routing problem is modeled using an evolutionary algorithm‐based intelligent path planning model, which handles vehicle assignments to material handling requests and makes routing decisions with the objective of maximizing the system throughput. The architecture is implemented on a 3‐layer software environment in order to evaluate the effectiveness of the proposed model.

The proposed architecture, along with the evolutionary algorithm‐based routing model, is implemented in a simulated FMS environment using hypothetical production data. In order to benchmark the performance of the path planning algorithm, the same FMS model is run by traditional dispatching rules. The analysis shows that the proposed routing model outperforms the traditional dispatching rules for real‐time routing of AGVs in many cases.

Future work includes expanding the scope of the current work by developing and implementing other routing models and benchmarking them against the proposed model on different performance measures.

The implementation of evolutionary algorithms in real‐time routing of AGVs is unique. In addition, due to its modularity, the proposed 3‐layer architecture can allow effective and efficient integration of different real‐time routing algorithms; therefore it can be used as a benchmarking platform.

This paper investigates interference mitigation and read rate improvement by using novel power control and graph‐based scheduling schemes for radio frequency identification (RFID) systems.

The first method is a distributed power control (DPC) scheme proposed as an alternative to listen‐before‐talk (LBT) for RFID systems specified under CEPT regulations. The DPC algorithm employs reader transmission power as the system control variable to achieve a desired read range and read rate without causing unwanted interference. The second approach is graph‐based scheduling, which uses a graph coloring‐based approach to temporally separate readers with overlapping interrogation zones. The scheduling of the timeslots is carried out so as to offer better efficiency for each reader.

This paper shows that power control, graph theory, collision probability analysis along with timeslot scheduling schemes can be widely adapted to solve general RFID problems. The study shows that selection of timeslot allocation schemes should be carried out after carefully analysing the process/workflow in the application domain. While fair scheduling schemes can be applicable to stable manufacturing environments, event‐triggered scheduling schemes are more effective in fairly chaotic environments.

The study shows that the proposed interference mitigation and read rate improvement techniques can be generalized to assist in design, development, and implementation of a variety of RFID‐based systems, ranging from supply chain level operations to shop floor control. The proposed techniques improve not only the reliability of RFID systems but, more importantly, improve business processes that rely on RFID data.

This paper presents an overview on the Auto‐ID (Automatic Identification) technologies testbed that has been established at the University of Missouri‐Rolla (UMR) with the objective of supporting research, development, and implementation of Auto‐ID technologies in network‐centric manufacturing environments.

UMR's Auto‐ID testbed uses a unique hardware‐in‐the‐loop simulation methodology, which integrates decision‐making model development with the design of networking topology and data routing/scheduling schemes, in order to develop, test, and implement viable Auto‐ID solutions. The methodology is founded on a 3‐level integrated model: controller simulation, distributed controller simulation, and distributed controller simulation with hardware‐in‐the‐loop.

This paper discusses two case studies that highlight the effective use of RFID technology, its potential advantages, challenges, and deficiencies stemming from particular applications. These applications include dock doors, automated guided vehicles, conveyor and automated storage/retrieval systems, integration of RFID middleware with programmable logic controllers, and inventory management of time‐sensitive materials.

The paper presents an innovative idea: hardware‐in‐the‐loop simulation methodology to design automation systems. The approach has been implemented on a variety of applications, which are presented in the paper as case studies.

Neuroimaging research has substantially enhanced our understanding of the neurobiological mechanisms of typical and atypical learning in children. These developments can advance the design of novel approaches to diagnosis and intervention for learning disabilities. Despite the promise of educational neuroscience, there are still walls between neuroscience and special education researchers such that more collaboration and understanding are needed between these disciplines. This chapter attempts to break down the walls by discussing how neuroimaging techniques can be incorporated into special education research. We also present arguments as to why neuroscience is “the next big thing” in special education research and the obstacles that must be overcome in order for neuroscience to be incorporated into education research. To describe how neurobiology might impact special education, we focus primarily on reading disability. We believe that educational neuroscience can aid in the identification and intervention of other learning disorders as well.

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.

Sustainable development is a central issue for the world economy today. The United Nation’s Sustainable Development Goals (SDGs) are associated with both responsible business practices and strategic orientation for competitive advantages. While most multinational enterprises (MNEs) want to ensure that their businesses will maintain or even enhance sustainability across borders, they face enormous challenges, often due to a lack of capabilities and inefficient institutions in host countries. In the nexus between the SDGs and international business (IB) research, the contexts of emerging markets and developing countries have particular significance, because they impose complex constraints on the achievement of the SDGs. At the same time, there is a high potential for MNEs to have positive effects internationally through their sustainable practices. This chapter discusses the recent trend in IB research on sustainability by showcasing current issues addressing several interrelated SDGs. The exemplary topics touch upon child labor, innovation for social sustainability, challenges in the green transition, MNE activities associated with the pollution haven, and health and safety concerns in global supply chains. The discussion cuts across various contextual settings and calls for actions by all stakeholders, including business entities, governments, and scholars.

This study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience.

Thematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts.

Digital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms.

Our first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets.

The novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

The adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment.

The novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.