Hasil untuk "Industrial engineering. Management engineering"

Michael Pinedo

F. Glover, M. Laguna

From the Publisher: This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan et al.

This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.

Jorge Melegati

The adoption of Generative AI (GenAI) suggests major changes for software engineering, including technical aspects but also human aspects of the professionals involved. One of these aspects is how individuals perceive themselves regarding their work, i.e., their work identity, and the processes they perform to form, adapt and reject these identities, i.e., identity work. Existent studies provide evidence of such identity work of software professionals triggered by the adoption of GenAI, however they do not consider differences among diverse roles, such as developers and testers. In this paper, we argue the need for considering the role as a factor defining the identity work of software professionals. To support our claim, we review some studies regarding different roles and also recent studies on how to adopt GenAI in software engineering. Then, we propose a research agenda to better understand how the role influences identity work of software professionals triggered by the adoption of GenAI, and, based on that, to propose new artifacts to support this adoption. We also discuss the potential implications for practice of the results to be obtained.

Yang Cai, Yunli Hao, Yongfang Qi

The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 fuzzy control to accurately handle their inherent fuzziness and environmental disturbances in complex environments. To address this, this paper introduces the backstepping control method based on Type-2 T-S fuzzy control, incorporating the niche situation function as the consequent of the T-S backstepping fuzzy control. The stability analysis of the system is completed by constructing a Lyapunov function, and the adaptive law for the parameters of the niche situation function is derived. This design reflects the tendency of biological individuals to always develop in a direction beneficial to themselves, highlighting the bio-inspired intelligent characteristics of the proposed method. The results of case simulations show that the Type-2 backstepping T-S fuzzy control has significantly superior comprehensive performance in dealing with the complexity and uncertainty of high-order niche situation systems compared with the traditional Type-1 control and Type-2 T-S adaptive fuzzy control. These results not only verify the adaptive and self-development capabilities of biological individuals, as well as their efficiency in environmental utilization, but also endow this control method with a solid practical foundation.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Haibo Wang, Zhuolin Xu, Shin Hwei Tan

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Dipin Khati, Yijin Liu, David N. Palacio et al.

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.

Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos et al.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

Zifeng Zhao, F. Xiao, S. Amirkhanian

Solid waste materials refer to those municipal or industrial or even domestic wastes that are massively produced during manufacture as well as daily life. A wide variety of solid wastes are generated, but only a few of them are employed in pavement structures. To achieve the task, papers between 2014 and 2019 from top journals which address waste management, waste characterization, and economic and environmental assessment were collected at first. Then, the scope of solid waste materials was narrowed down based on their applications, In the end, six most popular solid waste materials were selected for further study and for the review, namely recycled concrete aggregates, reclaimed asphalt pavement, fly ash, bottom ash, waste rubber, and waste plastics. The reason why only these six solid waste materials are selected is that the rest are neither massively produced not can they be directly used in pavement structures. This review article contains structural properties, treatments, novel researches, environmental analysis, and economic analysis of each solid waste material. The objective of this review article is to provide future researchers with the latest research findings, limitations and improvements in this subject. In conclusion, quite a few novel researchers in terms of workmanship, construction methodology, and treatments are collected for all six materials. Furthermore, an increasing number of findings have proven that most solid waste materials have both environmental and economic benefits when applied in field. Future researchers are encouraged to implement researches on solid waste materials in pavement engineering with consideration of both material properties and environmental/economic effects.

T. Choi, H. Chan, Xiaohang Yue

Li Yizhan, Dong Lu, Fan Xiaoxiao et al.

Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

Joseph W. Lockwood, Avantika Gori, Pierre Gentine

Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks.

Yasena Chantova

This article presents a discussion on polysemy in a crossword by Perec. We apply Culioli’s modelling approach in terms of operation of location and construction of a notion and its notional domain. Polysemy is seen as part of the intangible cultural heritage within languages.

Umm-e- Habiba, Markus Haug, Justus Bogner et al.

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

Kendrik Yan Hong Lim, Pai Zheng, Chun-Hsien Chen et al.

Abstract Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.

Raihan Maskuriy, A. Selamat, P. Marešová et al.

Technology and innovations have fueled the evolution of the fourth industrial revolution (Industry 4.0). Industry 4.0 spurs growth and development through its efficiency capacity, as documented in the literature. The growth of the construction industry is a subset of the universal set of the value of gross domestic product, and thus, industry 4.0 has a spillover effect on the engineering and construction industry. The aim of this paper is to map the state of Industry 4.0 in the construction industry from the point of view of manarial activities, such as investment management, project preparation, and an overall approach to the management of related activities. This study employed scoping review techniques to dissect the status quo for Industry 4.0 and the construction industry. The empirical results from the systematic and scoping review methods for the ten sampled publications revealed that information and communication technology (ICT)—Industry 4.0—has a significant positive impact on the growth of the construction industry. Therefore, construction practitioners should partner more with researchers in the ICT industry to enhance the automation of work processes and managerial activities in the engineering and construction industry.

Jinzhi Lu, Zhaorui Yang, Xiaochen Zheng et al.

Digital Twin technology has been widely applied in various industry domains. Modern industrial systems are highly complex consisting of multiple interrelated systems, subsystems and components. During the lifecycle of an industrial system, multiple digital twin models might be created related to different domains and lifecycle phases. The integration of these relevant models is crucial for creating higher-level intelligent systems. The Cognitive Digital Twin (CDT) concept has been proposed to address this challenge by empowering digital twins with augmented semantic capabilities. It aims at identifying the dynamics and interrelationships of virtual models, thus to enhance complexity management capability and to support decision-making during the entire system lifecycle. This paper aims to explore the CDT concept and its core elements following a systems engineering approach. A conceptual architecture is designed according to the ISO 42010 standard to support CDT development; and an application framework enabled by knowledge graph is provided to guide the CDT applications. In addition, an enabling tool-chain is proposed corresponding to the framework to facilitate the implementation of CDT. Finally, a case study is conducted, based on simulation experiments as a proof-of-concept.

M. D. Ooms, C. Dinh, E. Sargent et al.

Microalgae and cyanobacteria are some of nature’s finest examples of solar energy conversion systems, effortlessly transforming inorganic carbon into complex molecules through photosynthesis. The efficiency of energy-dense hydrocarbon production by photosynthetic organisms is determined in part by the light collected by the microorganisms. Therefore, optical engineering has the potential to increase the productivity of algae cultivation systems used for industrial-scale biofuel synthesis. Herein, we explore and report emerging and promising material science and engineering innovations for augmenting microalgal photosynthesis. Photosynthetic microalgae could provide an ecologically sustainable route to produce solar biofuels and high-value chemicals. Here, the authors review various optical management strategies used to manipulate the incident light in order to increase the efficiency of microalgae biofuel production.