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Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.

This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.

The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.

The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

As academic social Q&A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity.

The study examines 1,968 questions in five disciplines on the academic social Q&A platform ResearchGate Q&A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines.

The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines.

This study provides support for academic social Q&A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.

This study aims to focus on the clothing recycling supply chain and aims to provide optimal decisions and managerial insights into supply chain strategies, thereby facilitating the sustainable development of the clothing industry.

Based on previous single- and dual-channel studies, game theory was employed to analyze multiple recycling channels. Concurrently, clothing consumer types were integrated into the analytical models to observe their impact on supply chain strategies. Three market scenarios were modeled for comparative analysis, and numerical experiments were conducted.

The intervention of fashion retailers in the clothing recycling market has intensified competition across the entire market. The proportions of various consumer types, their preferences for online platforms and their preference for the retailer’s channel influence the optimal decisions and profits of supply chain members. The diversity of recycling channels may enhance the recycling volume of clothes; however, it should meet certain conditions.

This study extends the existing theory from a channel dimension by exploring multiple channels. Furthermore, by investigating the classifications of clothing consumers and their influence on supply chain strategies, the theory is enhanced from the consumer perspective.

The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to investigate the inventory-related optimization of an e-marketplace official store that works on a business-to-customer system when cashback promotion is used to attract more customers. Also, it proposes a new inventory model to maximize the e-commerce profit by optimizing the cashback amount and delivery period.

The proposed model assumes that customer demand is a function of price and delivery time and that price is affected by the cashback amount. The e-commerce operator has a profit-sharing contract with an e-payment company that facilitates the payment. E-commerce also builds collaboration under a cost-sharing contract with a supplier to ensure product delivery. A mathematical model is developed and the related theories are investigated. A numerical example illustrates the validity of the model and a sensitivity analysis is carried out to give useful insights.

A new inventory model for an e-market system has been introduced which shows the impact of a cashback promotion on the e-commerce business. This study shows that managers can optimize the cashback amount and its delivery time to get the maximum profit. In certain cases, the manager may set a high cashback amount (e.g. 100%) to attract customers to place more orders.

This study presents a new inventory model for today’s fast-growing e-commerce business; therefore, the results contribute to the understanding of promotion program practices and inventory management and provide insights to develop efficient e-commerce managerial decisions.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

Drawing on social information processing theory (SIP), this paper examines whether and how humble leadership affects employees' service improvisation (ESI) in the hospitality industry. Further, the study investigates the mediating role of psychological safety and the moderating role of creative self-efficacy (CSE).

To test the proposed relationships, the study adopts a cross-sectional design, administering questionnaires to 456 frontline staff in Egypt’s hospitality industry across three main sectors: restaurants, hotels and travel agencies. SPSS 27 and AMOS 22 were used for statistical analysis.

The study reveals a positive relationship between humble leadership and ESI, partially mediated by psychological safety. Furthermore, CSE not only strengthens the relationship between psychological safety and ESI but also enhances the indirect effect of humble leadership on ESI via psychological safety.

The study offers valuable insights for practitioners in the hospitality industry. To boost ESI, organizations can incorporate humble leadership attributes into their leadership development programs. Fostering a psychologically safe workplace would facilitate the positive impact of humble leadership on ESI. Recognizing CSE as a pivotal moderator underscores the importance of strategically selecting and developing employees with high CSE. These insights aim to cultivate a more service-oriented and effective workforce in the hospitality industry.

This study significantly contributes to leadership research in the hospitality industry by uncovering a previously unexplored link between humble leadership and ESI. Exploring psychological safety as a mediator and CSE as a moderator enhances our comprehension of how and when humble leadership influences ESI.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context.

The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data.

Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve.

Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target.

This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry.

The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.