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Journal of Industrial Intelligence
JGELCD
Journal of Industrial Intelligence (JII)
JIMD
10.56578
ISSN (print): 2958-2687
ISSN (online): 2958-2695
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Volume
2023: Vol. 1
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Journal of Industrial Intelligence (JII) is a peer-reviewed, scholarly open access journal on intelligent technologies, their industrial applications, and the related fields. It is published quarterly by Acadlore. The publication dates of the four issues usually fall in March, June, September, and December each year.

  • Professional service - All articles submitted go through rigorous yet rapid peer review and editing, following the strictest publication standards.

  • Fast publication - All articles accepted are quickly published, thanks to our expertise in organizing peer-review, editing, and production.

  • Open access - All articles published are immediately available to global audience, and freely sharable anywhere, anytime.

  • Additional benefits - All articles accepted enjoy free English editing, and face no length limit or color charges.

Editor(s)-in-chief(1)
vladimir simić
University of Belgrade, Serbia
vsima@sf.bg.ac.rs | website
Research interests: Operations Research; Decision Support Systems; Transportation Engineering; Multi-Criteria Decision-Making; Waste Management

Aims & Scope

Aims

Journal of Industrial Intelligence (JII) (ISSN 2958-2687) is an open access refereed journal disseminating innovative works in intelligent technologies and their industrial applications. JII provides a high-end forum for academic researchers, industrial professionals, and policy makers in related fields from around the world to report both fundamental and applied research results. We welcome original submissions in various forms, including reviews, regular research papers, and short communications as well as Special Issues on particular topics. The journal is particularly in favor of original and authoritative works that promote real-world industrial applications in addition to technical fundamentals.

The aim of JII is to encourage scientists to publish their theoretical and experimental results in as much detail as possible. Therefore, the journal has no restrictions regarding the length of papers. Full details should be provided so that the results can be reproduced. In addition, the journal has the following features:

  • Authors from non-English speaking countries will receive language support.
  • Authors from emerging countries enjoy the same high-quality services as those from the developed world.
  • The full details of the calculation and experimental procedure as well as source codes can be submitted as supplementary material.

Scope

The scope of the journal covers, but is not limited to the following topics:

  • Industry 4.0
  • Multi-agent collaborative sensing and control
  • Feature extraction and knowledge acquisition
  • Intelligent sensing and industrial data modelling
  • Industrial data representation and visualization
  • Industrial perception, cognition, and decision-
  • New intelligent system theory and methods
  • Smart factories and Internet of things
  • Product quality surveillance, and fault diagnosis
  • Internet-based remote monitoring
  • Integrated sensors and machines
  • Predictive maintenance
  • Abnormal situation monitoring
  • Causality analysis
  • Control performance monitoring and assessment
  • Cooperative control, autonomous control, operation optimization control, etc.
  • Intelligent decision systems
  • Virtual manufacturing, smart grid, unmanned vehicles, unmanned aerial vehicles (UAVs), etc.
  • Real-time optimization through reinforcement learning
  • Weak AI development
Articles
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Abstract

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Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.

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This study presents an advanced generalization of uncertain linguistic numbers (ULNs) and interval-valued intuitionistic uncertain linguistic numbers (IVIULNs) through the development of interval-valued picture fuzzy numbers (IVPFNs). Firstly, the IVPFUL weighted average and IVPFUL weighted geometric operators, denoted as IVPFULWA and IVPFULWG, have been introduced. Furthermore, the IVPFUL Dombi weighted average and geometric operators, represented by IVPFULDWA and IVPFULDWG, are also proposed in the same context. These operators are utilized to establish a multi-attribute decision-making (MADM) approach with IVPFUL data. Finally, the proposed methodology is applied to a mutual fund selection problem through a demonstrative example.

Open Access
Research article
Algorithmic Approach for the Confluence of Lean Methodology and Industry 4.0 Technologies: Challenges, Benefits, and Practical Applications
dragana stojanović ,
jovana joković ,
ivan tomašević ,
barbara simeunović ,
dragoslav slović
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Available online: 06-29-2023

Abstract

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This study focuses on formulating an integration algorithm for manufacturing firms aiming to infuse the immense potential of Industry 4.0 technologies into lean manufacturing systems. The goal is to unlock and harness the advantages offered by these advanced technologies in an economically efficient manner. An analytic approach has been implemented in this investigation, examining a broad array of relevant empirical research. This comprehensive analysis serves to derive a universal algorithm predicated on the principles of both lean methodology and Industry 4.0. The complexities and challenges of amalgamating lean methodology and Industry 4.0 have been scrutinized meticulously in this study. The study elaborates on the extent to which Industry 4.0 technologies can augment lean production practices, delves into the difficulties encountered by corporations during the integration process, and suggests measures to surmount these obstacles. Moreover, potential benefits realized through this integration are explored. The algorithm proffered in this study permits a phased integration approach. Firms have the flexibility to adopt the integration in specific production segments or processes initially and progressively expand, aligning with their capabilities, resources, and the level of process maturity. Such an integration strategy allows companies to leverage Industry 4.0 in overcoming restrictions traditionally associated with the lean approach.

Abstract

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Water distribution networks are susceptible to abrupt pressure fluctuations and spikes due to rapid adjustments in valve and pump settings. A common occurrence resulting from the sudden closure of a valve, known as water hammer, can potentially cause damage to various components within the network if not adequately addressed. Traditionally, water hammer phenomena have been modeled using a set of hyperbolic partial differential equations (PDEs). This study introduces a simplified model that employs switched differential-algebraic equations (DAEs). Recognized for their capacity to generate infinite peaks in response to sudden structural changes, switched DAEs provide mathematical representations of infinite peaks, manifested as Dirac impulses. This modeling approach offers the potential for more straightforward analyses of complex water networks in future research. To validate the proposed technique, a numerical comparison was conducted between the PDE- and DAE-based models, using a basic configuration consisting of two reservoirs, a pipe, and a valve.

Open Access
Research article
Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach
yanjun qiu ,
mouhamed bayane bouraima ,
clement kiprotich kiptum ,
ertugrul ayyildiz ,
željko stević ,
ibrahim badi ,
kevin maraka ndiema
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Available online: 06-25-2023

Abstract

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Developed countries have successfully implemented various Industry 4.0 (I4.0) initiatives, showcasing their ability to reap the benefits of this new industrial revolution. Active pursuit of excellence in Industry 4.0 is evident in these nations. However, in Africa, many countries still lack a clear understanding of Industry 4.0, with some remaining trapped in Industry 1.0 and others facing challenges in transitioning to Industry 2.0. Moreover, a significant number of these African countries continue to grapple with limited access to reliable electricity. To address the issue, this study examines seven strategies identified as criteria for enhancing the adoption of Industry 4.0 within the East African Community (EAC). These strategies are derived from observations of Industry 4.0 initiatives implemented in developed countries. Subsequently, the criteria are used to evaluate and rank the level of Industry 4.0 adoption in two specific East African countries. To tackle the challenges of complex group decision-making, the study integrates the Weighted Aggregated Sum Product Assessment (WASPAS) technique with the Step-Wise Weight Assessment Ratio Analysis (SWARA) within a spherical fuzzy (SF) framework. The SF-SWARA approach is applied to determine the weight and importance of the criteria, while SF-WASPAS is employed to rank the countries based on the criteria weighted by SF-SWARA. According to the findings, it was revealed that education and training, research, development, and innovation, as well as public-private partnerships and policy innovation, are the three most influential strategies for significantly improving the adoption of Industry 4.0 within the East African community. Furthermore, the results indicate that Rwanda stands out as the leading country in terms of implementing these strategies to enhance the adoption of Industry 4.0 technology. To verify the reliability and suitability of the proposed methodology, a sensitivity analysis was conducted, which affirmed the stability and practicality of the suggested approach.

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Libya's strategic position at the crossroads of Europe and Africa offers access to abundant raw materials, labor, and extensive land for establishing free trade zones. The primary objective of this research is to determine the key advantages and opportunities that Libya could potentially leverage as a transit trade hub in the Mediterranean region through the establishment of free trade zones. This study investigates the extent to which Libya facilitates the expansion of commerce between Europe and Africa via initiatives related to free trade zones. Six criteria were employed in the present research, including economic, social, financial, environmental, quality, and demand factors. A panel of experts evaluated these criteria. The Full Consistency Method (FUCOM) was utilized to derive the criteria weights, with the economic criterion identified as the most significant. The Grey-CoCoSo (Combined Compromise Solution) methodology was applied to rank the industries eligible for investment within Libya's free zones. According to the findings, the food sector holds the highest importance in relation to investment.

Abstract

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Smart phone selection involves several product attributes and brand values of the manufacturing company, and the sets of alternatives, criteria, and decision-makers may be updated multiple times during the purchasing process. In this study, a multi-index multi-criteria decision-making approach is proposed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique with intuitionistic fuzzy sets (IFS) measures based on score-based measures. The purchasing of electronic gadgets is considered, and a similarity-based solution to the multi-index, multi-criteria decision-making problem is proposed. The effectiveness of the suggested approach is demonstrated through a numerical scenario. The results highlight the efficacy of the proposed method in resolving specific decision-making problems in the marketplace.

Abstract

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Improving the effectiveness of green supply chains is a critical step towards minimizing waste, optimizing resource use, and reducing the environmental impact of business operations. Sustainable practices should be implemented throughout the entire supply chain, from product design and procurement to production and transportation, in order to achieve these goals. By doing so, businesses can not only improve their environmental performance but also reduce costs, increase customer satisfaction, and gain a competitive advantage in the market. However, due to the existence of competing characteristics, imprecise information, and a lack of knowledge, selecting the appropriate green provider is a complex and unpredictable decision-making issue. The primary objective of a linear-diophantine fuzzy (LiDF) framework is to assist decision makers in selecting the optimal course of action. This paper introduces several novel aggregation operators (AOs), namely the linear Diophantine fuzzy soft-max average (LiDFSMA) and the linear Diophantine fuzzy soft-max geometric (LiDFSMG) operators. The proposed method is then demonstrated through a simple example of a green supplier optimization technique containing linear Diophantine fuzzy content, showing the utility and applicability of the approach. Overall, the proposed LiDF framework and AOs can aid decision makers in selecting the most suitable green provider, thereby enhancing the efficiency of green supply chains.

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The attention of many researchers has been drawn to Pythagorean fuzzy information, which involves Pythagorean fuzzy numbers and their aggregation operators. In this study, the concept of the Pythagorean fuzzy set is discussed, along with the Hamacher t-norm and t-conorm operators. Furthermore, novel aggregation operators are developed using the operational rules of the Hamacher t-norm and t-conorm. The primary objective of this article is to develop a multi-attribute decision-making method in a Pythagorean fuzzy environment using Pythagorean fuzzy Hamacher aggregation operators. It is noted that the Hamacher operator, which is a generalization of the algebraic Einstein operator and contains a parameter, is more potent than some existing operators. Finally, an example of an enterprise application software selection problem is presented to demonstrate the proposed method.

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The most sensitive and vulnerable component of the supply chain is last-mile logistics, which is especially vulnerable to consequential challenges due to the current global crises. Customers expect prompt and dependable delivery of their orders, regardless of where they buy or order them. To meet the needs and requirements of customers, logistics companies are being forced to use innovative Industry 4.0 solutions. Last-mile logistics are under constant challenge due to high population density and growing urbanization, which concentrate the majority of user service requests in urban city areas. As a result of the increase in the number of online orders and the volume of e-commerce, longer delivery times, delivery errors, and customer dissatisfaction occur. Therefore, the implementation of modern Industry 4.0 solutions, such as new autonomous vehicles, is necessary to respond to numerous challenges that affect the efficiency of all entities in the supply chain, particularly the last mile. Autonomous vehicles are an essential component of Industry 4.0, primarily concerned with the autonomy of activities in last-mile logistics, and have filled the market with numerous innovations. This study aims to highlight the benefits of some of the most common autonomous vehicles for realizing user requests in the last mile and provide suitable guidelines for selecting the most suitable alternative for the logistics company. Additionally, the research identifies certain challenges in their implementation, pointing to some of the key motivations for future research.

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This study aimed to address the optimization of magnetically coupled resonant wireless power transfer. An equivalent circuit for the wireless power transfer was established and the factors affecting the transmission efficiency were analyzed. To optimize the system, an improved whale optimization algorithm (WOA) was proposed and applied to optimize the optimal matching values of resonant frequency and load resistance. Performance of the improved WOA was tested using different test functions, and the optimized parameters were applied to the transmission efficiency test of the wireless power transfer system. Experiments demonstrated that the improved WOA effectively optimized the transmission efficiency and achieved good application results in the intelligent transfer system.

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