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In this article, a novel linear parallel control method is developed for output regulation problems with disturbances. The traditional feedback regulators are passive regulation methods. In order to solve this problem, the parallel controllers are presented, where the time variation of the control is constructed instead of the control value itself, to stabilize the output of the system. The main contributions of the developed method include two aspects: 1) a novel parallel regulator structure is presented, which can provide greater flexibility comparing with traditional methods and 2) the necessary and sufficient conditions for the existence of parallel regulators are analyzed. First, the structure of the linear parallel regulators is provided. Next, considering the situations that the full system information is obtained and the information of error is obtained, respectively, the properties of the parallel regulators are analyzed, and the regulator designs for the two situations are provided. Finally, numerical examples are provided to verify the correctness of the present method.

Truck congestion at chemical sites is a persistent problem that is difficult to solve, even using a truck appointment system. This study presents an alternative solution to improve the flexibility of chemical sites by creating a drop-swap terminal adjacent to the site location. The terminal serves as an intermediate depot where the trucks can drop empty containers and swap them with preloaded containers without entering the site. This study aims to evaluate the performance of such a solution in mitigating truck congestion at chemical sites. The problem is modeled as a nonstationary semi-open queueing network with time-varying arrivals. We propose a combination of a fluid-flow approximation and a decomposition-aggregation method to estimate the time-dependent performance of the system. A chemical site in the Netherlands is presented as a case study. Several scenarios are tested and evaluated. Numerical results show that a drop-swap terminal can effectively reduce truck idling time and increase logistics efficiency at chemical sites. We also found that swapping containers on chassis is a cheaper and greener option to operate the terminal. However, the investment needed to support the operation should not be overlooked to sustain the benefits. The study concluded with several key messages for site operators who wish to maximize the benefit from a drop-swap terminal.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

Problem definition: We consider a two-sided market competition problem where two platforms, such as Uber and Lyft, compete on both supply and demand sides and study the impact of precommitments in a variety of practically motivated instruments on the equilibrium outcomes. Academic/practical relevance: We extend a set of classic oligopoly pricing results to account for two-sided competition under demand uncertainty. Methodology: We investigate multi-stage competition games. Results: We start with a sufficiently low demand uncertainty. First, we show that a precommitment made on the less competitive (demand or supply) side (on price or wage) has a less intense outcome than no commitment (i.e., spot-market price and wage competition). Then we show that, somewhat surprisingly, if the competition intensities of both sides are sufficiently close, the commission precommitment, where the platforms first compete in setting their commission rates and then their prices, is less profitable than no precommitment at all, and vice versa. Furthermore, we show that the capacity precommitment, in which the platforms first commit to a matching capacity and then set price and wage simultaneously subject to the precommitted capacity, leads to the most profitable outcome of all competition modes and extends the celebrated Kreps-Scheinkman equivalency to the two-sided market (without demand uncertainty). Then we extend the comparisons of various competition modes to account for a relatively high demand uncertainty. We show that the comparison between the spot-market price and wage competition and the commission precommitment stays the same as that with a sufficiently low demand uncertainty. In addition, the more flexible competition modes, such as no commitment and commission precommitment, benefit from higher demand uncertainty (with a fixed mean demand) because of their operational flexibility in response to the market changes. Further, a relatively high demand uncertainty may undermine or enhance the value of the wage precommitment, as opposed to no commitment. Finally, we also account for platforms with asymmetric parameters and matching friction and find that our main insights tend to be robust. Managerial implications: Our results caution platforms that a precommitment to the wrong instrument can be worse than no commitment at all. Moreover, the regulation of classifying gig workers as employees, despite many of its benefits to workers, may lead to a less competitive market outcome and, surprisingly, hurt gig workers by paying them lower wages. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2021-04295]. Y. Liu was supported by the Hong Kong Research Grants Council, Direct Allocation Grant [Project ID P0036818]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1173 .

Different forms of coworking spaces have spread worldwide that use various artifacts (e.g., office settings, decor, furniture, equipment) to strengthen autonomy, flexibility, spontaneous interactions, and intrinsic motivation. However, the various manifestations of coworking spaces limit clearly defined boundary conditions for their successful implementation and demand a profound understanding of the interactions that take place among actors and artifacts. Using a flexible pattern matching approach, our study explains that artifacts can contour and create the possibilities for actors and define the borders, e.g., when, where, and with whom to communicate and work in coworking spaces. Our findings indicate that actors and artifacts are constitutively entangled in coworking spaces and play a key role in the community development process by fostering a shared understanding and identity, mutual interdependency, and common practices.

High-dimensional dependence modeling remains a crucial challenge in quantitative project cost and time risk analysis. Building a complete and mathematically consistent correlation matrix becomes unrealistically restrictive as the number of uncertain performance units in a project (i.e., activity times and costs) increases, regardless of using empirical data or with subjective judgment. This article presents a pair of additive factor dependence models that provide analytic solutions to the generation of a complete and mathematically consistent correlation matrix. The additive risk factor (ARF) models account for multiple risk factors in two classes (i.e., extra-marginal and intramarginal) while providing additional flexibility for a strategic tradeoff between the accuracy and the scalability to high-dimensional project risks. We extend the ARF models to present an analytic solution to the program evaluation and review technique (PERT) problem with correlated activity times. Numerical examples demonstrate the accuracy and computational efficiency of the ARF approaches. The ARF approaches and the ARF-PERT would serve as a quick and sensible alternative to large-scale Monte Carlo simulation, in particular during the early stage of the project life-cycle.

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24x speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes., 13 pages, 4 figures

Cloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the user’s direct control, which raises new security concerns. In this context, BD security plays a decisive role in the widespread adoption of cloud architectures. However, it is challenging to develop a comprehensive security plan unless it is based on a preliminary analysis that ensures a realistic secure assembly and addresses domain-specific vulnerabilities. This article presents a novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud). In particular, it relies on a systematic security analysis methodology and a completely automated security assessment framework. Our framework enables the mapping of BigCloud security domain knowledge to the best practices in the design phase. We validated the proposed framework by implementing an Apache Hadoop stack use case. The study findings demonstrate its effectiveness in improving awareness of security aspects and reducing security design time. It also evaluates the strengths and limitations of the proposed framework, from which it highlights the main existing and open challenges in the BigCloud-related area.

This paper proposes a new concept of Aviation-to-Grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using PV, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced based on the mixed integer linear programming method to meet electrified aviation and provide A2G frequency response. Case studies are conducted in 8 major UK airports considering seasonal flight schedules and power system scenarios. Results show that the EA charging system can provide effective primary and secondary frequency response to improve the frequency nadir by 0.2 - 0.3 Hz under grid disturbance. The total A2G frequency response capacity across the 8 UK airport can reach up to 1,300 MW overnight and up to 900 MW daytime. Annual A2G frequency response revenue is 46.58 million pounds, which can cover 19.8% to 30% of EA charging costs.

Existing programming practices for building Android apps mainly follow the one-size-fits-all strategy to include lots of functions and adapt to most types of devices. However, this strategy can result in software bloat and many serious issues, such as slow download speed, and large attack surfaces. Existing solutions cannot effectively debloat an app as they either lack flexibility or require human efforts. This work proposes a novel feature-oriented debloating approach and builds a new tool, named XDebloat, to automate this process in a flexible manner. First, XDebloat supports feature location approaches at a fine granularity. It also makes the feature location results editable and changeable. Second, XDebloat considers several Android-oriented issues (i.e., callback, UI dependencies), which are not covered in the state-of-art approaches. Third, XDebloat support two major debloating strategies: pruning-based and module-based debloating. We evaluate XDebloat with 200 open-source and 1,000 commercial apps. The results show that XDebloat can successfully remove unwanted features from apps or transform them into on-demand modules within 10 minutes. For the pruning-based debloating strategy, on average, XDebloat can remove 32.1% code from an app. For the module-based debloating strategy, XDebloat can help developers build modules automatically.

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

Problem definition: In high-product-variety businesses like fashion, mass production (MP) systems create environmental waste in the form of overproduction on a colossal scale. Mass customization (MC) has been proposed—without solid evidence—as a solution. In this paper, we analyze whether MC can indeed offer a win-win solution that helps both the bottom line and the environment. We also study the impact of three real policy options: promoting MC, charging a disposal fee for overproduction, and recycling. Academic/practical relevance: There is increasing interest in mass-customizing fashion goods, not only because consumers value customization, but also because MC is perceived to be environmentally friendly. Our paper puts this advocacy for MC to the test. We contribute to the literature, which has been largely silent on the issue, by uncovering when MC offers a win-win and relating such market outcomes to policy ideas. Methodology: We develop an analytical model of an MP firm adopting MC (going hybrid). The firm’s profit-maximizing variety, price, and inventory decisions then form the basis of our understanding the environmental impact of adopting MC and assessing various policy options. Results: Adopting MC can be a win-win, but it can also increase overproduction and hurt the environment. Our policy analyses reveal two kinds of insights. The first kind is about whether a policy expands win-win outcomes—encouraging sustainable adoption of MC. Among the policy ideas we explore, only promoting MC so as to increase consumers’ tolerance for waiting for mass-customized products can do that unambiguously. The second kind of insight is about whether a policy reduces the hybrid firm’s environmental impact. Only a disposal fee and costly recycling programs can do that unambiguously. Managerial implications: For MC adoption to be a win-win, policy makers must (1) work on convincing consumers to wait for bespoke fashion; (2) target MP firms with low cost of variety (high product-mix flexibility) with disposal fees or costly recycling programs; and (3) encourage those with relatively higher cost of variety to develop/acquire technology that would make recycling profitable. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1088 .

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence., Comment: Accepted to IEEE Transactions on Software Engineering (Aug 2021); This author preprint is 26 pages, the final IEEE version is 25 pages due to reformatting, content is same. More details: https://rashina.com/

The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT’s foremost task is ensuring that there is proper communication between different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles have focused on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.

Space-air-ground integrated network (SAGIN) is still in nascent stage of design. Despite of several key insights onto the augmentation of terrestrial, aerial and satellite systems, SAGIN ecosystem is yet not mature enough to survive into the realty. More agility, robustness, flexibility, and scalability are required to conform to optimum standard of abstraction. As 5G is at the verge of technology domain, this is high time when we should keep our focus on the next-generation advanced 6G technology to cater the issues of existing SAGIN ecosystem. In this article, we envisage a clear vision on how 6G can improve the current scenario of the SAGIN infrastructure will some value-added services. We firstly, present basics behind the SAGIN and discuss key concepts of the 6G. Secondly, we review key technologies related to the unmanned aerial vehicle (UAV) and satellite-based communications. Thirdly, we describe key enablers of the 6G-enbled SAGIN i.e., 6G-SAGIN. Fourthly, we present the UAV-as-a-service to augment the comprehension of 6G-SAGIN. Fifthly, we extend the orientation of 6G-SAGIN toward elemental design aspects. Finally, we depict key open research challenges and prescribe some future direction.

Permanent magnet linear synchronous motor (PMLSM) has a wide application prospect in transmission systems of automatic production, logistics and other fields. However, the existing PMLSM has the problems of poor flexibility and large thrust fluctuation adapted to load and stroke variation. In order to solve this problem, a dual-modular PMLSM is proposed. The motor can be flexibly configured with multiple-unit-primary (MUP) and multiple-unit-secondary (MUS), and achieve low thrust fluctuation. The matching criterion of unit-primary (UP) and secondary is established to reveal the relationship between the slot/pole combination and the winding utilization. The rules of the average thrust varying with the different UP and unit-secondary (US) lengths are illustrated. Then, the matching criterion of different UP is proposed, and the optimization method of the spacing between UP (S1) is obtained by establishing the end force model. The design standard of the optimal S1 is obtained by combining the two factors of minimum detent force and maximum thrust. The results are verified by the simulation and experiments on a 3UP-96US dual-modular PMLSM circular transmission line system and its prototype.

The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.

Resilience and flexibility are the means by which a network stands for an efficient service provider platform. Cognitive packet network (CPN) is such a paradigm that solely depends on the behavioral and intelligence capacity of the packets that carry valuable information within the network. Unlike conventional networks, CPN relies on the decision-making ability of the packets. In CPN, different types of packets take part in finding the optimal route from the source to destination. Further, packets dynamically adapt environmental changes by using recurrent neural network-based learning algorithms. Mapping cognitive features along with enrichment of quality of service is the main task of CPN. Due to continuous updates in the packet information vulnerable elements are found to be less prone to attack the CPN, thus resulting in a robust networking solution. In this paper we firstly present a novel taxonomic structure of the CPN. Then, we discuss the stringent routing techniques normally used in CPN. We also investigate various adaptive aware features in CPN. Next, we present intuitive emergency service and sensor network design prospects of the CPN. The article also discusses various means of cognitive aware mitigation aligned with the packets in CPN. Finally, we deliver how CPN is secure and available security aspects.

The primary objective of the case study is to help students understand, model, and solve capacity planning problems (i) when the production yield is uncertain and (ii) if the yield of first production is low, the product can be produced a second time. The dimension of random yield emphasizes supply chain management challenges beyond the traditional newsvendor problem, which has a nice closed-form solution. The dimension of operational flexibility underscores the importance of modeling skills, such as influence diagrams, that can be used to brainstorm, model, and solve new problems. Extensive use of the case in graduate supply chain management courses shows that in the absence of a mathematical model, students systematically deviate from optimal capacity usage. Usually, the presence of backup flexibility reduces first-period production. However, students’ responses often suggest cutting first production too much. When it is available, students consistently overuse the flexibility of second-period production. Overall, students believed the case was challenging and that it provides a valuable learning experience.

In this paper, we introduce, model and solve the pickup and delivery problem with time windows, multiple stacks, and handling operations (PDPTWMS-H). In the PDPTWMS-H, a fleet of vehicles based at a depot is used to complete a set of requests which consist of transporting items from a pickup location to a delivery location. The vehicles have multiple stacks operated using last-in-first-out (LIFO) loading which requires the vehicle to be rear-loaded and items can only be unloaded if they are closest to the back door. In the PDPTWMS-H, additional handling operations, referred to as rehandling, are allowed and an additional handling time might be incurred when rehandling items (by unloading and reloading items). The problem consists of determining the number of vehicles and the vehicle routes needed to complete the set of requests at minimal cost while respecting the possible handling operations. We model the PDPTWMS-H with a set-partitioning formulation and resort to branch-price-and-cut (BPC) for its solution. To solve the pricing problem, we derive a unified labeling algorithm that is able to cope with the different rehandling possibilities. The labeling algorithm keeps track about the information of on-board items such that symmetries with respect to both stacks and item positions are reduced. Extensive tests are performed on benchmark instances to assess the performance of the proposed BPC methodology and to provide insights on the impact of the rehandling flexibility on solution quality and time.

Due to the resource-constraint nature and lack of lightweight computing solutions for diagnostic devices in healthcare IoT, provisioning time-critical responses is still challenging. In this paper, we propose DC-Health, a Dew Computing enabled IoT healthcare solution for offline and ultra-low latency decisions. The proposed solution connects a large number of healthcare devices and provisions user-specific services even when Internet connectivity is not available. The computation module is placed at the extreme edge rather than the cloud to reduce the complexity and to improve the user-specific services. In addition to the other computing facilities provided by the cloud, fog, and edge, our solution performs with a negligible dependency on the Internet. We develop a prototype of DC-Health, which monitors the heart condition using the ECG sensors with end-mile services, flexibility in terms of user-control, and mobility feature. The experimental implementations show that the proposed architecture minimizes the network response time by at least 92% and 98%, compared to the fog and cloud-based approaches, respectively. Along with this, the proposed technique also reduces the CPU and memory usages, and response time by around 30% compared to the conventional method.

Many efforts have been dedicated to addressing data loss in various domains. While task-specific solutions may eliminate the respective issue in certain applications, finding a generic method for missing data estimation is rather complex. In this regard, this article proposes a novel missing data imputation algorithm, which has supreme generalization ability for a vast variety of applications. Making use of both complete and incomplete parts of data, the proposed algorithm reduces the effect of missing ratio, which makes it suitable for situations with very high missing ratios. In addition, this feature enables model construction on incomplete training sets, which is rarely addressed in the literature. Moreover, the nonparametric nature of this new algorithm brings about supreme flexibility against all variations of missing values and data distribution. We incorporate the advantages of denoising autoencoders and ladder architecture into a novel formulation based on deep neural networks. To evaluate the proposed algorithm, a comparative study is performed using a number of reputable imputation techniques. In this process, real-world benchmark datasets from different domains are selected. On top of that, a real cyber-physical system is also evaluated to study the generalization ability of the proposed algorithm for distinct applications. To do so, we conduct studies based on three missing data mechanisms, namely: 1) missing completely at random; 2) missing at random; and 3) missing not at random. The attained results indicate the superiority of the proposed method in these experiments.

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to given chemical composition and application. The extreme flexibility of machine learning models promises to yield reactive force fields that provide a more general description of chemical bonding. However, a significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely-used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance and improved learning rates when compared to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable reactive force fields.

Mathematical models used in preclinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumor volume; however, they typically have little connection with the underlying physiologic processes. This lack of a mechanistic underpinning restricts their flexibility and potentially inhibits their translation across studies including from animal to human. Here we present a mathematical model describing tumor growth for the evaluation of single-agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumors. Importantly, we demonstrate that the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for preclinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition, the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current preclinical modeling studies to also incorporating spatially resolved and multimodal data without significant added complexity and creates the opportunity to improve translation and tumor response predictions. Significance: This theoretical model has the same mathematical structure as that currently used for drug development. However, its mechanistic basis enables prediction of growing fraction and spatial variations in drug distribution.

This paper proposes an adaptive self-tuned current controller design for an LCL-filtered LC-tuned single-phase shunt hybrid active power filter (HAPF), which offers adaptability and flexibility of the control system to filter parameters changes. System modelling and online identification of parameters are presented. Then, a systematic self-tuned controller (STC) design is proposed to calculate the controller parameters and obtain the final control law adaptively. The exact HAPF parameters design is described and the overall closed-loop stability evaluation with considering digital control delay is discussed in this paper. To confirm the theoretical achievements, simulations and experimental tests on a real prototype system are performed. The results are in good accordance and demonstrate the effectiveness and superior performance of the suggested control technique. Furthermore, a comprehensive comparison between the proposed control technique and other controllers is provided to show the superiority of the proposed system from different aspects.

Innovation steps in complex products often go hand in hand with the selection of suppliers. The suppliers are involved in the development of innovative elements of products, supplementing the capabilities of companies. Strategic relationships are established with these suppliers, where the companies need them to be flexible and willing to adapt to changing demands. Flexibility is usually evaluated against a variety of indicators. However, a high score on flexibility does not guarantee cooperative suppliers. Companies also require power to get a supplier to be flexible. They need suppliers who are malleable to their changing needs that are an inevitable part of design processes. The empirical study at a leading European engine company reported in this paper shows that the relative power between companies and suppliers influences the responsiveness of suppliers to requests. This paper analyses malleability as a combination of flexibility and relative power. It unpacks power and flexibility into sets of measurable indicators and proposes a tool which assesses both the relative power between buyer and supplier and the supplier’s flexibility, to provide a malleability index, which indicates to what extent a supplier is suitable to work with.

The selection of an optimal maintenance strategy is one of the principal strategic decisions that must be taken in many contexts in order to maintain an asset with minimum deterioration and to deliver maximum output with high quality. When considering maintenance cost, reliability, and safety level of industrial assets, decision makers must select an appropriate maintenance strategy, preferably, with a known degree of uncertainty. This article utilizes a new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy. The proposed method is compared with the results obtained from the conventional AHP-TOPSIS and type-1 fuzzy AHP-TOPSIS—the rank obtained from IT2F-AHP-TOPSIS is identical to those methods and can be used as an effective alternative to the type-1 fuzzy method. Compared with conventional AHP-TOPSIS and type-1 fuzzy AHP, IT2F-AHP-TOPSIS has the added advantage that it allows decision makers to define the membership function with greater flexibility and is able to handle uncertainty during decision making.

Nowadays, free-form surfaces have been widely used in various industrial fields. They are usually machined by CNC machine tools, but recently have also begun to be manufactured by industrial robotic arm manipulators, thanks to low cost, large operation reaching space and high machining flexibility of robot. So far, various methodologies have been proposed to improve efficiency and quality of free-form surface machining, thus this paper aims at providing a state-of-the-art review on research advances in free-form surface machining. In this review, tool path generation, feedrate scheduling in Cartesian space and trajectory planning in joint space are focused for both CNC machining and robot machining, and their research statues, existing difficulties and key issues are discussed in detail. Finally, the feasible routes, breakthrough points and future development trend are also expounded.

Currently, industrial robots are considered as an alternative towards traditional machine tools. Especially for the large-scale parts milling, robotic flexibility and low cost make it possess the irreplaceability. However, the milling chatter caused by its weak rigidity hampers robotic application and promotion severely in aviation industry. Rotary ultrasonic milling (RUM) technology with one-dimensional axial vibration has been proven and approved on avoiding robotic chatter. Based on this, the research of project team demonstrates that longitudinal-torsional composite ultrasonic milling (CUM-LT) involving separation characteristic has a greater advantage than RUM in terms of chatter suppression. Thereby, the CUM-LT as a new means is applied to avoid processing vibration of robotic milling system. And its influence mechanism on stability improvement of weak stiffness processing system is clarified. Meanwhile, the approaches to strengthen separation effect are provided innovatively. Moreover, a new analysis method of robotic CUM-LT (RCUM-LT) stability is proposed on the basis of ultrasonic function angles. The simulation and experimental results indicate that compared with robotic RUM (RRUM), stability regions of separated RCUM-LT (SRCUM-LT) and unseparated RCUM-LT (URCUM-LT) are improved by 124.42% and 39.20%, respectively. The addition of torsional ultrasonic energy has a wonderful effect on the milling chatter suppression of low stiffness robots.

To reinforce competitiveness, companies must focus on competitive priorities including cost, quality, delivery, and flexibility. The competitiveness also depends on the adherence to environmental requirements and the ability of companies to survive unforeseen disruptions. Lean, agile, resilient, and green (LARG) paradigm is a hybrid supply chain paradigm, which can enhance competitiveness in today's complex markets. In this article, we propose a new conceptual decision-making model to evaluate LARG supply chain performance in medical equipment companies. In this regard, supply chain operations reference model attributes were utilized as performance evaluation criteria. Furthermore, the appropriate practices of LARG supply chain are selected through fuzzy Delphi method. Also, an extension of decision making trial and evaluation laboratory-based analytic network process method is proposed to uncover network relationships and obtain the weights for dimensions and elements. According to the results, the agile paradigm, responsiveness, and quick introduction of new products are determined as the most prioritized elements in their corresponding dimensions, which emphasize the importance of the customer satisfaction. Finally, the extended hesitant fuzzy linguistic VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) is applied to rank LARG performance of Iranian needles and syringes companies via qualitative group decision-making process. The results of this study are conformed to conventional selection results of needles and syringes suppliers conducted by Iran's Ministry of Health.

Reduction gears are used in the joints of the robots. While precision and efficiency are required for industrial robot gearboxes, humanoid robots are expected to become more popular in the future. Flexibility must be considered in humanoid robots, and high back drivability shall be required. In this study, we aimed at realizing high efficiency, and high-precision reduction gears with high reduction ratios by maximizing the efficiency equation, hybridizing a 3K compound planetary reduction gearbox and traction drive, experimentally determining the efficiency, backlash, and angular velocity variation, and demonstrating the effectiveness of the proposed mechanism from a control perspective. In the experiments, we achieved a maximum forward driving efficiency of 90.90%, backward driving efficiency of 90.58%, and a minimum backlash of 1.18 arcmin.

Most existing voltage optimization approaches of low voltage distribution networks (LVDNs) assume users follow the regulations unconditionally,which is not always true.To tackle this, we propose a real-time peer to peer flexibility trading scheme for LVDNs. Besides distributed optimization, our scheme offers a way to let users trade their flexibility, in case some users do not want to follow the system regulations at some point. The scheme consists of flexibility allocation and trading. Player compatibility equilibrium and Bernoulli trials are employed to approximate the Pareto optimum in flexibility allocation,while consensus and voltage reference are used to provide real-time offers for flexibility trading. The proposed scheme is computationally tractable and can work with limited communication in a decentralized manner. All the user data is used locally so that privacy is well protected. A network based on a real Belgian semiurban LVDN is employed to validate the proposed scheme with three different scenarios. Besides, the flexibility allocation is benchmarked with a centralized ACOPF algorithm. The case studies result clearly prove the effectiveness and robustness of the proposed scheme. ispartof: Ieee Transactions On Power Systems vol:37 issue:4 pages:2821-2832 status: published

Interactions between different policy instruments in real markets can lead to a mismatch between stakeholders striving for individual benefits and the attainment of an overall system optimum. These distortion effects are of particular relevance for sector-integrated systems with high prosumer shares. Distributed battery storage systems, in particular, could constitute a pivotal source of flexibility in close to 100% renewable energy systems. However, depending on their operation mode, these battery systems can either serve individual profit maximization or the benefit of the system as a whole. We quantify the resulting differences by means of a fundamental linear optimization model of the electricity market, which we stringently link to a stakeholder's complete cost-of-energy analysis including relevant regulatory price components. We demonstrate that the effects encompass both system cost changes and distributional effects for certain stakeholder groups. We quantify the effects and apply an impact variation analysis to demonstrate that they could be relevant in view of the overall acceptance of the energy system transformation.

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for sometime and then transfer to another one, MHRS helps in attaining 30\% lower cost and 20\% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services., Comment: This is an extended version of the work presented in NeurIPS Workshop 2019. arXiv admin note: text overlap with arXiv:1903.03882

In the early onset of the COVID-19 pandemic in the U.S., consumers experienced surprising shortages of essential goods such as toilet paper, yeast and flour, and some of their favorite meat cuts that appeared to be unrelated to the pandemic at first look. The “usual” explanations attributing these shortages to “demand spikes” (e.g., foreign suppliers, trade wars and tariffs, and “hoarding behavior” of panicked consumers) often failed to explain them, and in the best of cases, predicted only temporary shortages. However, shortages in these supply chains ended up being in many cases real “supply chain struggles,” with their true causes going beyond the “usual” ones, and revealing a set of deeper and “unusual causes.” Our detailed analysis of the affected supply chains identifies these overlooked failure factors and hidden causes. Our underestimated causes include demand shifts among market segments, siloed demand planning, bottlenecks in shared resources such as transportation and distribution, and supply chain structures driven by the economics of an efficiency era that failed in a risk-fraught environment. We conclude on the profound lessons learned from the pandemic crisis on supply chains, and the implied challenges to address for building resilient supply chains for the future. The resilient supply chains of the future require rethinking the relevant “systems” we plan and optimize (e.g., firm, industry, region, global factor resources, etc.). The usual answer of building firm-specific redundancy of assets and operational flexibility might be prohibitive in the level of investment required for any one firm, or their financial stakeholders, to pursue and accept.

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.

Coordinated preheating can potentially enhance the flexibility of the electricity system, thereby driving significant economic savings. In this context, this paper proposes a novel game-theory based preheating coordination scheme aimed at guaranteeing the effectiveness of collective preheating performed by a large population of households. By adopting an innovative two-phase iterative algorithm, individual households autonomously schedule their heating power allocation to seek for savings in energy bills while smartly maintaining thermal comfort. Meanwhile, the total electricity generation cost decreases at each iteration and converges to an equilibrium solution asymptotically. Compared to the centralised optimization-based energy management control methods, the proposed algorithm effectively reduces the information exchange while significantly reduces computational complexity. The simulation results indicate that the proposed coordination strategy can drive 12.30% cost saving when all households participate in the preheating scheme whereas 11.35% cost increase will be incurred if coordination is absent. Overall, the proposed preheating control algorithm simultaneously benefits both individual households and the whole system by intelligently linking local behaviour with global interests.

This work investigates the tracking consensus problem of multiagent systems over directed networks, where the control gains follow certain volatile patterns. Some event-based consensus protocols are formulated so as to reduce the redundant execution of control. By using an extended differential inequality with a time-dependent coefficient, criteria for tracking consensus under time- and state-dependent triggering conditions are constructed, respectively. It is proved that the time average of the control gain, together with the agent dynamics, network topology, and triggering conditions, governs the consensus despite the fluctuation of control gain. The derived theorem can be utilized to ensure consensus with intermittent strategies aiming to lessen the burden in communications, including aperiodic on-off control with periodic perturbation and pulse-modulated on-off control. Unlike existing works, the requirement of a positive lower bound of control ratios is removed and, thus, a wide range of control gain patterns is possible, signifying higher flexibility in intermittent policy design. Finally, numerical examples are provided to further illustrate the theoretical results.

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.

Bipolar VSC-MTDC is a promising solution to increase power transmission capacity and system reliability for large-scale renewable energy integration. However, traditional interpolar active power control methods cannot comprehensively explore the maximum transmission capability of the bipolar VSC-MTDC under abnormal operating conditions. Considering the flexibility of the bipolar VSC-MTDC that the positive-pole and negative-pole DC networks can be operated independently, a novel fault self-recovering control strategy is proposed in this paper to provide automatic, global and timely recovery solutions under various fault conditions: (a Firstly, a coordinated time-driven and event-driven security monitoring, optimization and recovering control framework is proposed to guarantee the accuracy and speed of the corresponding calculation and control, hence to meet the timeliness requirement of fault self-recovery; (b Secondly, a global optimal power allocation calculation method is proposed to automatically calculate the optimal power allocation between the positive-pole and negative-pole DC networks; (c Thirdly, an interpolar active power dispatch method is proposed to realize the fault self-recovering control strategy based on primary control strategies of bipolar VSC-MTDC. Simulation results using MATLAB and PSCAD/EMTDC show that the proposed control strategy can timely and effectively enhance the overall power transmission capability for large-scale renewable energy integration after the fault.

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in the minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow to solve the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility resulting in a significant reduction in the time needed to rebalance the network.

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

This paper documents two facts: (i) elasticities of substitution in production vary significantly across sectors, with manufacturing sectors being generally less flexible than service sectors, and (ii) during the Great Recession the rise in bond spreads varied systematically with these elasticities. Specifically, more flexible sectors paid lower spreads during the Great Recession. Moreover, among the less-flexible manufacturing sectors, sectors with relatively high flexibility and high debt saw their spreads rise less than average, while among the more-flexible service sectors the sectors with relatively high flexibility and high debt saw their spreads rise more. We interpret these results using a simple two-sector model with working capital constraints, and show that the model replicates these observations if manufacturing sectors face constraints on their purchases of intermediates while services face constraints on their purchases of labor/capital. The dynamics of intermediate prices and quantities support our results, as does a quantitative investigation of a 62-sector version of the US economy.

The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques.

An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail (HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter-modal strategy focuses on occasionally operational integration of existing airside and HSR infrastructure capacities. It is different from air-rail cooperation implemented in Europe which emphasizes a long-term strategy. In addition to modelling the air-rail inter-modal strategy, an inter-modal time-band network is presented. Modelling is applied to a pure aviation network and the inter-modal network. Comparison results show that the inter-modal air-rail strategy helps to reduce the number of cancelled flights and the total disruption cost.

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.

Today, in the market with ever-changing consumer preferences, three-dimensional (3D) printing is becoming an overwhelming trend. In this paper, we explore the use of 3D printing in mass customization (MC) programs. We consider the case when 3D printing brings extra self-design fun to consumers, which is highlighted in MC practices of the auto company BMW and the furniture company Poltrona Frau, and also changes the cost formula (i.e., the marginal product variety cost) of the MC product. In addition, the roles played by the risk attitudes of the MC manufacturer and consumers, consumer returns, as well as consumers’ time sensitive behaviors, are also uncovered. We find that under the case with a low consumer's willingness to pay for the traditional “ready-made product variety enjoyment” (RPVE), even the maximized product variety level and the additional self-design fun cannot make the 3D printing based MC more profitable. In addition, compared to the markets with risk-averse and risk-neutral consumers, the 3D printing based MC can increase more MRBs and a higher consumer surplus by simultaneously highlighting the high self-design fun and the high RPVE in a market with risk-seeking consumers. The high flexibility and responsiveness of 3D printing also shows its advantages in remanufacturing the consumer returned MC items and enhancing MC programs’ overall lead-time. Finally, applying 3D printing in creating molds can also help the MC manufacturer tackle demand uncertainties. These findings all provide a good reference to the application of 3D printing in MC operations.