World Academy of Science, Engineering and Technology

Authors: Jiatao Wu, Naiyu Wang, Xiaodeng Hao, Canrong Zhang

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This study investigates a multi-item, multi-level capacitated lot-sizing and machine scheduling problem within a manufacturing environment that involves complex toolkit reconfiguration. Several factors contribute to the challenges of the problem, especially the intricate mapping relationship between products, processing stages, toolkits, and individual tools. During each production planning period, the manufacturing facility must determine whether and how to reconfigure machine tooling setups. This reconfiguration governs the updated eligibility set of production stages, thereby directly influencing production scheduling efficiency and operational feasibility. A Machine-Based (MB) MILP modelis formulated, introducing the concept of “setup for toolkit'” for the first time. To address the high symmetry inherent in the feasible region of the MB model, a Kit-Based (KB) model is subsequently developed, achieving significant dimensionality reduction. In the KB model, the total unimodularity is demonstrated for certain integer variables by refining the classical optimal transport problem. To solve this model, this study develops a novel iterative algorithm, called the Price and Check Mathematical Heuristic Algorithm (PCMH), in which Benders decomposition is applied to manage the reconfiguration planning in the subproblem, while Dantzig-Wolfe decomposition is employed on the master problem to obtain high-quality kit allocation and production schedules. Numerical experiments based on real-life data validate the effectiveness and tightness of both models, confirming that the PCMHoutperforms commercial solvers and the domain-based rules adopted in the collaborating semiconductor final test company in terms of both speed and quality. The sensitivity analysis yields valuable managerial insights, notably that enhancing the system's flexibility balance combined with machine pre-configuration deserves particular attention.

Keywords: lot-sizing, machine reconfiguration, flexible processes, benders decomposition, dantzig-wolfe decomposition

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Authors: Hanjie Wang, Canrong Zhang

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Single-event upset(SEU) induced by radiation pose a major reliability challenge for commercial field programmable gate array (FPGA) in aerospace and other harsh environments.SEU is a type of cumulative-degradation soft error whose lifetimes follow a Weibull distribution, thereby violating the classical exponential failure assumption.This study investigates five SEU mitigation strategies, including k-out-of-n redundancy, periodic scrubbing, error detection and correction (EDAC), as well as two hybrid strategies.A semi-Markov process framework that incorporates deterministic repair times and aging effects is proposed forthe quantitative analysis of keymetrics, including reliability,mean time to failure (MTTF), availability, and steady-state availability.Results show that the hybrid strategy combining k-out-of-n redundancy with EDAC achieves the highest overall performance.Further analysis explores the influence of parameters such as radiation intensity, degradation level, redundancy configuration, and scrub interval on the effectiveness of each mitigation strategy.The proposed approach provides a practical and scalable tool for designing robust mitigation solutions in space electronic systems.

Keywords: FPGA, single event upset, weibull distribution, mitigation strategies, semi-Markov process, reliability analysis

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Authors: Naiyu Wang, Canrong Zhang

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This paper addresses operation synchronization in vehicle routing problems with time windows, which is characterized by the temporal interdependence between tasks conducted on different routes. We propose a distinct Dantzig-Wolfe decomposition to handle operation synchronization, eliminating the issue of introducing linear costs in pricing sub-problems commonly encountered in conventional decomposition methods, without entailing time discretization. Moreover, considering that some combinations of visiting sequences (not schedules) become infeasible under the condition of operation synchronization, we propose pre-calculated bounds to tighten the search space for the restricted master problem. Furthermore, we introduce two subset-precedence-row inequalities, which are essentially Chv´atal-Gomory rank-1 cuts based on a subset of the temporal precedence constraints in the master problem. We further analyze how to modify the label algorithms used to solve the pricing sub-problem so that they can accommodate the adoption of the subset-precedence-row inequalities. Numerical experiments demonstrate that the proposed Dantzig-Wolfe decomposition significantly outperforms the traditional formulation, and the valid inequalities reduce the solution time substantially.

Keywords: vehicle routing problem, operation synchronization, branch and price and cut, valid inequalities, column and row generation

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Authors: Elham Ahmadi, Andre Ludwig, Omid Fattahi Valilai

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Improving reliability in a combined freight and passenger multimodal transportation system requires accurate delay prediction and timely decision-making. This research introduces a sequential, integrated framework that combines machine learning and Markov Decision Process (MDP) models to enhance estimated time of arrival (ETA) prediction and operational decision-making. The approach begins with a machine learning model that predicts arrival delays at each segment of a transportation chain using contextual features like weather, passenger demand, and temporal data. At transition points, the predicted delays inform an MDP, which selects the optimal operational action (e.g., departure adjustment, rerouting). The chosen action determines the updated departure time for the next leg, which is then used in the next round of ETA prediction. This sequential process continues throughout the journey, enabling dynamic adjustments and improving end-to-end ETA accuracy. Experiments using simulated multimodal data demonstrate that the framework significantly improves delay management and transport chain reliability, outperforming traditional static models. The method offers a scalable, data-driven tool for real-time decision support in interconnected passenger and freight networks.

Keywords: estimated time of arrival, machine learning, Markov decision process, passenger and freight integration, multimodal transportation, sequential decision-making

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Authors: Hassan Younis

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This study aims to investigate food loss challenges in Jordan’s fresh fruit and vegetable supply chain (FFVSC), focusing particularly on tomatoes, which are highly perishable and widely cultivated in the country. Using a mixed-methods approach, the research will combine structured field surveys with simulation modeling to analyze critical loss points across the farming, transportation, wholesale, and retail stages of the supply chain. The surveys will collect empirical data from farmers, wholesalers, and retailers to capture insights on handling practices, infrastructure limitations, and pricing behavior. These data will inform a dynamic simulation model capable of visualizing the impact of different logistical and market configurations on food loss outcomes. Specifically, the simulation model will evaluate how alternative network designs, such as cold storage integration and modified distribution routes, influence product quality and waste levels under varying climatic and market conditions. Moreover, the study will explore the role of pricing mechanisms—such as perishability-linked discounts, seasonal adjustments, and dynamic pricing tools—in incentivizing more efficient market behavior. The goal is to identify synergistic strategies that align producer, distributor, and consumer incentives toward reduced waste and enhanced value retention. Given Jordan’s reliance on agriculture under arid climatic conditions and constrained water resources, improving the resilience and efficiency of FFVSCs is both a food security and economic development imperative. The anticipated findings will contribute to the design of practical, evidence-based policy and investment interventions, addressing systemic inefficiencies and supporting national objectives for sustainable agriculture. Ultimately, the study is expected to offer a replicable framework for other water-scarce regions facing similar food supply chain challenges.

Keywords: dynamic pricing, food loss reduction, Jordan, simulation modeling, sustainable supply chain, tomato logistics

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Authors: Adeogun Taiwo Olayinka

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Supply chain is an intrinsic element of trade. In part, trading on a global scale is driven by regional adaptive supply chain management that ensures free-flow production and consumption of goods and services in the international market. However, in an era of trade regulations through the imposition of tariffs on imports by countries, supply chains are disrupted, and their management becomes costly and chaotic. Using the mixed methods of data collection and analysis, this in-depth study explores how the burden of trade tariffs destabilizes supply chain management in the African region and what it portends for the rest of the world. Naturally, no region in the world is immune to the disturbing effects of trade tariffs; however, due to certain fundamentals and trade management instruments, among others, the African region showed resilience to the impact of the global trade war. The data revealed that the peculiar nature of the region’s export of mostly natural resources to the rest of the world led to a cautious approach to tariff imposition on African countries by the rest of the world. In addition, the implementation of the African Continental Free Trade agreement terms further created a ‘safe-haven’ for the evolving intra-African trade systems. Global tariff regimes not only had minimal effects on the supply chain management in the region but also provided opportunities to improve existing supply chains and create new tariff-resistant supply chain management strategies for newly established trade networks across the continent.

Keywords: Africa, supply chain, trade, tariff

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Authors: Shegaw Abebaw Yalew, Gebeyehu Belay Gebremeskel

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Business modernization is dynamic and fast-changing, with internal and external framework conditions and new customer expectations for efficient delivery of goods and services. As business is digitalizing and complex and advanced, it demands advanced tools to develop and synthesize data for its future perspective. However, the changes and business sophistication are not easy for traditional or classic business analytics and operational research. The purpose of the paper is to propose Machine Learning (ML) for business analytics and digital transformation that will affect business process management for critical, rigorous, and dynamic business analytics processes. Therefore, this paper focused on predictive modeling for in-depth analysis of the existing business and trend data using ML algorithms. As a result, we revealed the need for ML for business analytics and digital transformation for sustainable and digitized business communication and performance. The need to deepen knowledge in digital transformation and refine the dimensions found involves data modeling for new business scenarios, possible opportunities, and challenges in future directions. It also optimizes business flow and processes in a smart world.

Keywords: business analytics, machine learning, digital transformation, operational research, business process

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Authors: Arman Moeinadini

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Food packaging presents significant challenges for sustainable packaging practices, particularly in regions with limited exposure to such practices. Food packaging plays an essential role in protecting products during transportation, preserving quality and freshness, providing critical information to consumers, and supporting marketing and branding efforts. The Life Cycle Assessment (LCA) framework offers a comprehensive method to evaluate the environmental impacts associated with food packaging. This research aims to analyse the integration of the 3Rs principles (Reuse, Recycle, Reduce) within the Life Cycle Assessment framework, specifically in the context of the Indian food packaging industry. The primary objective is to elucidate the impacts of applying the 3Rs principles within the LCA framework, identifying and understanding critical sustainability issues specific to Indian food packaging.Utilizing the Life Cycle Assessment approach aligned with ISO 14040 and ISO 14044 standards, this study aims to reveal and overcome barriers within the supply chain to enhance sustainability. The research employs a qualitative methodology, gathering data through semi-structured interviews with 29 senior managers from food packaging companies in India. These interviews explore the interplay between material provision, processes, and the conceptual understanding of sustainable packaging and their implications within the LCA context. Ultimately, the study seeks to pinpoint interrelated issues and propose practical solutions at various supply chain stages, promoting sustainable packaging practices through efficient material usage, minimized waste, and resource conservation.

Keywords: life cycle assessment (LCA), SSCM(sustainable supply chain), FAO( food And organisation of world), SGDs ( united nation sustainable goal development)

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Authors: Sambit Kumar Dash

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The Indian manufacturing sector faces challenges in lowering energy-related emissions. The study highlights that the industry requires an immediate shift to alternative energy due to heavy reliance on fossil fuels. This research examines how digitalization might improve the performance of the energy supply chain, concentrating on India's transition to renewable energy and greener technologies. This transition can be facilitated by emerging digitalization technologies that improve transparency, efficiency, and risk reduction, including big data analytics, artificial intelligence, blockchain, and the Internet of Things. The study uses the Graph Theoretic and Matrix Approach to evaluate the efficacy of these digitalization strategies in the energy transition. The results show that digital transformation has the potential to enhance energy management, support environmental initiatives, optimize supply chains, and help better policy implementation. The industry may improve sustainable development initiatives and lower operational risks by utilizing digitalization. With an eye toward a sustainable energy future, this study offers insights into how India's industrial sector may address decarbonization issues through digital solutions.

Keywords: energy transition, technology, digitalization, sustainability, energy supply chain, manufacturing sector

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Authors: Michael Gidey Gebru

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Most data envelopment analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on by shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp data envelopment analysis into data envelopment analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the model with fuzzy environment is solved using a multi-objective method to gauge the decision making units’ efficiency. Finally, the developed data envelopment analysis model is illustrated with an application on real data from 50 educational institutions.

Keywords: efficiency, data envelopment analysis, fuzzy, higher education, input, output

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Authors: Jiuyao Jiang, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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As the scale and complexity of systems increase, their interdependencies also grow. Failures and recoveries of network nodes can trigger cascading failures and self-recovery based on these dependencies. Extending network resilience from single-layer networks to interdependent networks is of significant importance for preventing unknown attacks and reducing risks. To this end, an Interdependent Network Resilience Enhancement (INRE) method considering recovery coupling mechanisms is proposed. Firstly, a five-order tensor is used to describe a time-dependent two-layer interdependent network model. This model incorporates a recovery coupling mechanism, where the support network can provide resources to the main network, as well as a cascading failure mechanism that considers both the internal connectivity of the networks and the dependency edges between different layers, thereby accurately reflecting the dynamic changes in interdependent networks. Secondly, based on the network model, resilience evaluation metrics for the two-layer interdependent network under the recovery coupling mechanism are constructed and represented using tensors. Thirdly, a resilience enhancement model for interdependent networks oriented toward node recovery is proposed, and an adaptive improved genetic algorithm based on an elite strategy is employed to solve it, determining the optimal node recovery set. Finally, randomly generated network models and real-world networks are used as case studies to validate the effectiveness of the INRE method. This includes comparing the resilience values of the proposed method with those of different recovery strategies under various disintegration strategies, analyzing the changes in network resilience under different parameters, and comparing the method with a deep reinforcement learning algorithm based on DDQN. The results demonstrate that INRE achieves faster convergence and superior performance.

Keywords: interdependent networks, network resilience enhancement, recovery coupling mechanisms, recovery optimization method, fifth-order tensor

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Authors: Sherif Fayez Lewis Ghaly

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digital factory based totally on visual layout and simulation has emerged as a mainstream to lessen digital improvement life cycle. a few basic business structures are being incorporated thru semantic modelling, and products (P) matching system (P)-aid (R) necessities are designed to satisfy modern consumer demands. despite the fact that, product layout continues to be restrained to constant product fashions and recognised knowledge of product engineers. therefore, this paper presents a rapid reconfiguration approach based on semantic technology with PPR ontologies to reuse recognised and unknown understanding. in an effort to keep away from the have an effect on of big facts, it gadget uses a cloud manufactory and disbursed database to enhance the efficiency of querying assembly PPR necessities.The orientation of pliability and adaptability with regard to manufacturing unit making plans is at device and process stage. manufacturing facility buildings aren't the point of interest of contemporary research. factory making plans has the assignment of designing merchandise, flowers, techniques, corporation, regions and the development of a manufacturing facility. The adaptability of a manufacturing unit can be divided into three types: spatial, organizational and technical adaptability. Spatial adaptability suggests the ability to increase and decrease the scale of a factory. right here, the place-associated breathing capability performs the vital role.

Keywords: building information modeling, digital factory model, factory planning, maintenance digital factory model factory planning, restructuring

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Authors: Naziha M. Nadzirin

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Smartwatch is a popular device and is being used widely. It offers various functionalities on top of timekeeping, ranging from fitness tracking and health monitoring to receiving/sending notifications and telephone calls, taking photos and productivity tools. Beyond their technological ability, smartwatches have also become fashion statements. Manufacturers offer a variety of designs, materials, and bands, allowing users to express their personal style. The fusion of fashion and technology has broadened the appeal of smartwatches to a wider audience. Currently, most plants or process area which classified as Hazardous Areas (Zone 0/1/2), allowed the usage of smartwatch. The International Electrotechnical Commission, IEC TS 60079-48 (November 2023), a guide on the use of Portable or Personal Electronic Equipment as well as Energy Institute Research Reports highlighted that smartwatches could produce an ignition source due to devices powered by rechargeable lithium-ion cells. The objective of this paper is to discuss how to manage smartwatch usage in Hazardous Areas specifically in the plant or process area. Smartwatches are defined as personal electronic equipment. Other personal or portable electronic equipment are as follows: (1) Personal Devices e.g., standard wristwatches, hearing aid devices, fitness trackers etc. (2) Portable Devices e.g., barcode scanners, cameras, calculators, flashlights, RFID tags, gas detectors, car keys, mobile phones, laptops, iPod, power banks etc. Internal standards and best practices differ on the usage of personal or portable electronic equipment in Hazardous Areas. From IEC TS 60079 series of standards, all personal or portable electronic equipment used in Hazardous Areas shall be EPL (Equipment Protection Level) certified. Smartwatches usually do not have Equipment Protection Level certified. Where Equipment Protection Level certificate is not commercially available, the PEP is to be assessed before allowing usage in Hazardous Areas. They shall be assessed and meet specific criteria in terms of sizes, battery capacity, type of material etc. in order to allow usage at Hazardous Areas. However, this requirement is not applicable to standard wristwatches, hearing aid, passive RFID tag/card and implanted medical devices such as pacemakers, cochlear implants, defibrillators as they do not constitute an ignition risk hence can be used at hazardous areas. Besides the risk of fire, smartwatches also pose other hazards such as security (photo taking in the plant). In addition, the power capacity of smartwatches is getting bigger and bigger with more functionalities being introduced. Typical battery capacity for smart watches ranges from around 250 mAh – 570 mAh. Results from benchmarking of practices among energy producers, comparison of international standards and guidelines and fire incident records due to smartwatch, it was recommended that the usage of smart watch shall be prohibited at Zone 0. Precautions should be taken for the usage of smartwatch at Zone 1 and Zone 2 due to the variety of sizes and battery’s energy level of smart watches available in the market. It is recommended that only smartwatches with voltage not more than 4.5 VDC and maximum battery capacity of 350 mAh that are allowed in Zone 1 and 2.

Keywords: smartwatch, PEP, ignition source, personal electronic equipment

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Authors: Ismail Mahmoud

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This article presents a reflection on the identification and improvement of industrial project management through an approach that combines top-down and bottom-up methodologies. Based on JESA's experience within OCP Group projects, it identifies coordination and communication issues among stakeholders, particularly during the Benguerir washing plant project. The proposed research project aims to develop a project management system based on Lean Management and Social Network Analysis (SNA) to optimize the quality, timelines, and costs of complex projects. The project includes the creation of communication and responsibility maps, information flow analysis, and the proposal of WBS standards adapted to the specificities of projects in Africa.

Keywords: top-down and bottom-up, JESA/OCP, SNA, WBS standard

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Authors: Mahmoud Ismail

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In a highly competitive industrial environment, companies must reduce costs, accelerate production times, and improve product quality to effectively meet customer expectations. Lean Management, a management approach derived from the Toyota Production System, aims to optimize processes by eliminating waste identified as non-value-added activities. Based on extensive bibliographic research on Lean Management, this study analyzes its implementation in a lubricant industrial site in Morocco. The primary objective was to improve the overall performance of manufacturing, packaging, storage, and distribution processes while fostering a sustainable culture of operational excellence within the organization. The methodology adopted relies on proven tools, including Value Stream Mapping (VSM), the "5 Whys" method, and the use of performance indicators. The results obtained demonstrate a significant improvement in performance: the manufacturing workshop's performance rate increased from 47% to 72%, while the packaging workshop's performance rose from 73% to 89%. These results illustrate the effectiveness of Lean Management in strengthening industrial competitiveness. Furthermore, the initiative raised awareness among top management of the importance of their involvement throughout the project while contributing to transforming operational teams' mindsets towards continuous improvement initiatives.

Keywords: lean management, continuous improvement, overall performance, value stream mapping

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Authors: Reenu Singh, Rauf Iqbal, Ashok K. Pundir

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Traditional painting tools often lack user-centered design, leading to increased physical strain and reduced efficiency. By optimizing handle ergonomics, material selection, and manufacturing processes, this study aims to enhance user comfort, improve force application, and minimize wrist strain. This study explores the development of ergonomic painting tool prototypes with a focus on handle design, joinery, and material selection for improved usability and industrial manufacturability. While the three prototypes share similar roller mechanisms, variations in handle and joint fabrication distinguish them. The prototypes were manually assembled using readily available plumbing and fixture components, whereas industrial production would require precise, standardized parts with optimized joint configurations. Homogeneous structures in industrial manufacturing would reduce the number of joints, leading to material efficiency and decreased prototype weight. Additionally, substituting PVC with high-performance plastics such as High-Density Polyethylene (HDPE) or Polypropylene (PP) would enhance durability and suitability for painting applications. Recommended manufacturing processes include press molding for switch parts and injection molding for handle and telescopic arms. Further research will include before-and-after assessments to evaluate musculoskeletal strain in painters using traditional versus newly designed tools. A well-designed ergonomic handle can improve grip comfort and optimize force application, potentially reducing wrist strain and enhancing overall work efficiency for painters.

Keywords: construction workers, ergonomics, industrial design, painting, tool-design

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Authors: Salah Ahmed Mohamed Elshourbagy

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This research paper presents a biomechanical model that investigates the impact of obesity on the dynamic response of the seated human body to vertical vibrations. Utilizing the Multibody Dynamics interface, the study simulates various components and connections within the human body to analyze whole-body vibrations (WBV). The findings indicate that an increase in thigh mass enhances vertical rotational movement and apparent mass at peak frequencies, with vibration decay observed up to a specific threshold. Notably, as thigh mass increases, the original stability of the system is maintained without further escalation. In contrast, torso mass positively influences vertical transmissibility, while peak values for rotational transmissibility decrease. Additionally, changes in mass significantly affect the frequency of apparent mass, highlighting a complex interplay among different body segments. The study also notes that increased acceleration elevates vertical transmissibility frequency while reducing rotational transmissibility frequency. These results underscore the intricate relationships and distinct effects that varying masses exert on different regions of the human body, providing valuable insights into biomechanics and postural stability in individuals with obesity.

Keywords: biomechanical model, obesity, vertical vibrations, seated human body, whole-body vibrations, multibody dynamics, thigh mass, torso mass, vertical transmissibility, rotational transmissibility, apparent mass, posture stability

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Authors: Ejem E. Ejem, Timothy S. Aikor

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This paper attempts to develop a logistics performance model incorporating logistics performance as the focal construct with logistics planning strategy as antecedent and organizational performance, both marketing and financial, as consequences. Data from a sample of 360 top/middle managers representing different offshore logistics firms were randomly selected from the Nigerian logistics employees and analyzed using a structural equation modeling methodology and principal component analysis. The findings suggest that logistics planning influences financial performance by improving business continuity processes and enhancing disaster immunity. Further, the findings indicate that when a firm employs mindful planning processes, an important element within logistics planning, it can avoid the trade-off between risk management and efficiency. Amidst other variables with significant loadings in the rotated component matrix of dimensions of offshore logistics planning, logistics service providers in offshore operations should pay close attention to the following: Awareness of whom to contact in an emergency relating to your logistics operations, the level of risk the organization acceptable for logistics operations, when to review business continuity plans for logistics operations, the testing requirements for the business continuity plan for logistics operation, adequate training for staff involved in preparing business continuity plans for its logistics operations, net income before tax compare with competitors and quick recovery of the business unit from disasters compare with competitors. As logistics service providers work to improve the logistics processes, they support their organization's logistics planning strategy, resulting in improved performance for the overall supply chain.

Keywords: logistics planning, supply chain, disaster immunity, competitive capabilities, logistics service providers.

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Authors: Joel Leonard, Johann Wannenburg

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Instead of relying on predetermined schedules or calendar intervals, Usage-Based Maintenance (UBM) is a proactive maintenance method that initiates maintenance operations based on the actual usage of the equipment. This is in contrast to traditional maintenance methods of corrective maintenance and time-based preventive maintenance. The precision and applicability of a usage-based model rely on its exactness when modelling the actual system. However, excessive simplification of usage-based maintenance models may omit all active failure modes that arise during equipment use, leading to inaccurate predictions and ineffective interventions. This paper presents a unique exponential-based predictive maintenance model that simultaneously considers the impact of multiple failure modes during equipment usage. The model integrates time-dependent deterioration dynamics and operational thresholds derived from basic principles. The proposed model integrates a growth-modulating factor governed by a baseline parameter against a system-specific usage threshold, which calculates the time to failure of the component under usage. Within the context of a chemical processing facility, a case study application of the formula is applied to data from the operating history of essential components subjected to four (4) different failure modes. These failure modes include fatigue, corrosion, erosion and wear. The study's findings demonstrate the formula's practical implementation and illustrate the impact that numerous failure modes working simultaneously on a single component might have due to operational stress on the time to failure. This technique provides a dynamic framework, allowing for predicting failure probability, optimizing maintenance schedules, and improving resource allocation in critical systems such as those in the chemical, aerospace, energy, and manufacturing industries.

Keywords: usage-based maintenance (UBM), failure modes, predictive maintenance model, operational thresholds, and maintenance optimization

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Authors: Timothy Shirgba Aikor, Ejem Agwu Ejem

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The African Continental Free Trade Area (AfCFTA), founded in 2018, represents a major milestone in Africa's pursuit of economic integration and sustainable growth. Its core aim is to remove trade barriers, enhance trade among African nations, and encourage economic diversification and industrial growth. The agreement seeks to reduce tariffs on goods by 90%, tackle non-tariff barriers, and implement frameworks for dispute resolution, intellectual property rights protection, and trade facilitation. This paper explores supply chain inefficiencies that obstruct AfCFTA's goal of enhancing intra-African trade and economic integration. It also assesses key stakeholders' perceptions of the agreement's effectiveness in addressing these challenges and identifies strategies for improving supply chain resilience and efficiency. A descriptive survey research design was adopted, systematically capturing insights from logistics and supply chain managers and researchers. The study indicates that inadequate infrastructure, inconsistent trade policies, border delays, and limited technological adoption hinder smooth trade flows among member states. Although trading under the AfCFTA officially began on January 1, 2021, significant trade activity has yet to take place under the agreement, raising doubts about its ability to address these challenges effectively. The paper suggests a coordinated strategy involving public-private partnerships, regional collaboration, standardized legal and regulatory frameworks, and technology-driven solutions to improve supply chain efficiency and reliability across Africa.

Keywords: AfCFTA, economic integration, intra-African trade, supply chain inefficiencies

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Authors: Taimoor Ahmed, Amna Yousaf, Roberto Chavez Clavijo

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The growing importance of addressing environmental issues has resulted in a heightened emphasis on incorporating sustainability into different aspects of business operations, specifically within the realms of Green Human Resource Management (Green HRM) and Green Supply Chain Management (Green SCM). Despite the increasing amount of scholarly literature pertaining to both fields, there exists a significant research gap in examining the interconnection between Green uman Resource Management (HRM) and Green Supply Chain Management (SCM). The objective of this systematic literature review is to address the existing knowledge gap by conducting a thorough bibliometric analysis of 60 distinct articles, selected from an initial set of 86, which specifically reference both domains in their titles. These articles were retrieved from reputable databases such as Scopus and Web of Science. The analysis encompassed a comprehensive investigation of citations, patterns of authorship, publication venues, and thematic emphasis within the chosen articles. The findings provide an overview of the existing knowledge regarding the interconnectedness of Green HRM and Green SCM, emphasising significant topics, influential scholars, and notable publication platforms. The results of the study also highlight areas where further research is needed and suggest avenues for future investigation. This study highlights the importance of conducting additional empirical studies and developing theoretical frameworks that encompass both domains. This study provides a comprehensive perspective on the interplay between Green HRM (Human Resource Management) and Green SCM (Supply Chain Management), thus assisting scholars and professionals in formulating approaches for the implementation of environmentally sustainable business practices.

Keywords: green HRM, green SCM, systematic literature review, bibliometric analysis, sustainability

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Authors: Rabiatu Adamu Saleh, Abdullahi S. B. Gimba, Adekunle Akanni Adeleke, Ahmed Usman

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One fossil fuel that is utilized to generate electricity is burned coal, which is a nonrenewable energy source. A lot of individuals steer clear of coal because they don't understand its significance. It is considered to contain a notable amount of rare metals, which serve as basic materials in the production of renewable energy. The occurrence of rare metals in Garin Maiganga Coal Deposit was analyzed using ICP OES, 16 rare metals were determined in the coal: Cesium, Gold, Platinum, Osmium, Iridium, Palladium, Ruthenium, Rhodium, Tellurium, Rhenium, Indium, Tantalum, Tungsten, Gallium, Lithium, and Cobalt. The results indicated the occurrence of Cs in all three decarbonized coal ash. The objective of this study is to increase the amount of data that scientists need before beginning metal recovery experiments. Inductively coupled plasma optical emission spectroscopy is the main focus of this work as it will be used to measure the concentration of these metals in Garin Maiganga coal resource in Gombe, Nigeria. In order to do this, the contents of rare metals were detected, and the economic values of the elements were looked into.

Keywords: coal, Maiganga, rare metals, ICP OES

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Authors: Michele Sinico

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Usability is significantly influenced by the perceptual characteristics of interfaces. This study investigates the effect of the inclination of elements in a physical interface on the evaluation of perceived usability. In the first experiment, a psychophysical methodology was employed to measure the perceived usability of 15 different washing machine interfaces. A model of perceived usability was adopted, which incorporating four factors: understandability, ease of use, safety, and attractiveness. The results indicate that participants were able to discriminate between the stimuli based on the factors considered. In the second experiment, the inclinations of the interface elements (buttons, LEDs, icons and text labels) were systematically modified. The findings reveal that inclination significantly affects three perceived usability subcomponents: understandability, ease of use, and attractiveness.

Keywords: ergonomics, perceptual usability, interfaces, inclination, washing machine

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Authors: Shaohui Gong, Cheng Zhu, Yun Zhou, Weiming Zhang

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Low Earth orbit mega-constellation networks (LEO MCNs), as critical future infrastructure, are expected to provide dynamically configurable communication, remote sensing, and other services on demand, making the assessment of regional on-demand service capabilities highly significant. However, the regional on-demand services of LEO MCNs rely on multi-satellite collaboration and dynamic resource allocation to address the non-uniform distribution of regional service demands, yet existing research has paid insufficient attention to this issue. To the best of our knowledge, this work proposes Payload Unit Service Capability (PUSC) metric for the first time, which quantifies the average service capability of a single payload unit to a fixed ground area within the shortest feasible service window, integrating temporal, spatial, and performance dimensions. Based on this metric, we construct an evaluation method for the on-demand service capability of mega-constellation regions, comprehensively considering factors such as regional demand distribution, payload service rules, multi-satellite task allocation, and resource coordination, to assess the overall service level and fairness of regional services. The proposed methodology begins by dividing target regions into Geographic Service Units (GSUs) based on payload service unit coverage footprints and segmenting the evaluation period into time slices. Using satellite simulation software, we model mega-constellation coverage relationships between satellites and GSUs through time-snapshot analysis. By incorporating regional demand distribution, payload service rules, and resource coordination mechanisms, we compute service fulfillment rates for each GSU, quantified through two metrics: Overall Regional Service Satisfaction Rate (ORSSR) and Minimum GSU Satisfaction Rate (MGSU-SR), representing system-level performance and geographical fairness, respectively. Implementing Starlink Phase I constellation parameters, we conduct communication service simulations for a 300 km radius circular region. Experimental results validate the effectiveness of our methodology, demonstrating a 36% average improvement in evaluation efficiency compared to existing evaluation methods based on channel formulas. Furthermore, the experiments analyze the impact of factors such as regional demand distribution, constellation scale, and multi-satellite task allocation on service capability. The results show that random distribution of target region demands can lead to better service. The service demand of the target region’s geographical service unit is the primary determinant of resource allocation, while the service demands of neighboring service units also influence the service conditions of the region, thereby making ground demand distribution a significant factor affecting the service capability of mega-constellation regions. Additionally, increasing the constellation scale enhances regional service capability, and the greater the parameter differences between different shell layers of the mega-constellation, the more stable the regional service level becomes. Moreover, multi-satellite task allocation rules have a significant impact on regional service capability, and task allocation rules based on satellite load balancing can achieve superior regional service.

Keywords: LEO mega-constellations, regional service, payload unit service capability, capability evaluating, key influencing factors, simulations

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Authors: Lijian Sun, Yun Zhou, Cheng Zhu, Weiming Zhang

Abstract:

Modern warfare emphasizes that the combat system-of-systems (CSoS) includes various combat units and powerful information exchange capabilities. Therefore, these units have formed a complex Heterogeneous Combat Network (HCN), where enemy and friendly combat forces engage within this network. To better accomplish an assigned task, equipment contribution analysis aims to identify important nodes within the HCN. However, constraints such as equipment payload often give rise to conflicting edges within the HCN. Hence, this paper focuses on analysing equipment contribution within task-oriented HCN from the perspective of Course of Action (COA). Specifically, this paper defines the HCN with constraints and proposes an approach to compute COA within the HCN. Then, the COA Capability Index (COACI) and COA Capability Change Index (COACCI) are proposed to analyse equipment contribution in the HCN. Finally, the effectiveness of the above method is validated through a case study. This paper provides a quantitative analysis approach for equipment contribution analysis within task-oriented HCN and offers valuable insights for designing more effective combat system-of-systems (CSoS).

Keywords: equipment contribution, heterogeneous combat network, conflicting edges, course of action, kill chain

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Authors: Abdurahman Muhammad Tanko, Muhammad M. Tanko

Abstract:

Digitalization is seen as the engine that changes today’s logistics and supply chain industries. It brought the era of increasing complexities, overwhelming competition, and accelerated change. On the other hand, not so much of these changes is seen in Nigerian logistics and supply chain industries. The aim of this study is to analyze the digitalization of different logistical and supply chain industries and to provide new digital solutions. Thematic analysis was used to analyze the data collected. The study demonstrated significant effect of industry 4.0 by utilizing digital technologies in emerging market like Nigeria. The research concluded that, digital technologies like Block chain, IoT, Machine Learning, increase the ability to optimize planning, sourcing and procurement strategies. The outcome of this research brings solutions to logistical and supply chain problems faced within industries in Nigeria. The study is significance to Nigerian government, logistical and supply chain companies, regulatory bodies, and researchers.

Keywords: digitalization, logistics, supply chains management, new technologies, nigeria

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Authors: Rodrigo Costa, Thomas Mazzuchi, Shahram Sarkani

Abstract:

This study analyzes the evolution of Bitcoin Improvement Proposals (BIPs), the self-governing mechanism that enables updates to the Bitcoin protocol. By modeling BIP submission frequencies with a Negative Binomial distribution and detecting change points with the Pelt Rupture model, we identify three distinct intervals of proposal activity, suggesting shifts in development priorities over time. Long-term growth patterns, captured by Gompertz and Weibull models, indicate an S-shaped trend in cumulative BIP counts, pointing toward a maturation phase in Bitcoin’s protocol. Our findings suggest that Bitcoin may be entering a stable stage, with fewer fundamental changes and more incremental enhancements. This trend highlights the need for further research into BIP content and more studies into its dynamics to better understand decentralized protocol governance and maturation.

Keywords: bitcoin improvement proposals, innovation management, change point detection, systems modeling, simulation

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Authors: Xueming Xu, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

Abstract:

A combat system-of-systems (CSoS) comprises various types of functional combat entities that interact to meet corresponding task requirements in the present and future. Enhancing the resilience of CSoS holds significant military value in optimizing the operational planning process, improving military survivability, and ensuring the successful completion of operational tasks. Accordingly, this research proposes an integrated framework called CSoS resilience enhancement (CSoSRE) to enhance the resilience of CSoS from a recovery perspective. Specifically, this research presents a two-phase resilience optimization model to define a resilience optimization objective for CSoS. This model considers not only task baseline, recovery cost, and recovery time limit but also the characteristics of emergency recovery and comprehensive recovery. Moreover, the research extends it from the deterministic case to the stochastic case to describe the uncertainty in the recovery process. Based on this, a resilience-oriented recovery optimization method based on deep reinforcement learning (RRODRL) is proposed to determine a set of entities requiring restoration and their recovery sequence, thereby enhancing the resilience of CSoS. This method improves the deep Q-learning algorithm by designing a discount factor that adapts to changes in CSoS state at different phases, simultaneously considering the network’s structural and functional characteristics within CSoS. Finally, extensive experiments are conducted to test the feasibility, effectiveness and superiority of the proposed framework. The obtained results offer useful insights for guiding operational recovery activity and designing a more resilient CSoS.

Keywords: combat system-of-systems, resilience optimization model, recovery optimization method, deep reinforcement learning, certainty and uncertainty

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Authors: Adre Dwi Wiratama, Budi Sampurna, Syougie Ali, Djunadi

Abstract:

Background: Even though safety is a priority in Commercial Aviation (CA) operations, fatal fixed-wing aircraft accidents still occur frequently in Indonesia. Objective: This research aims to determine factors associated with fatal and non-fatal CA fixed-wing aircraft accidents in Indonesia. Methods: The research used a cross-sectional design, which was carried out in July 2023. It included all final reports on fixed-wing aircraft accidents published by the Indonesian National Transportation Safety Committee (KNKT). Analysis was conducted using chi-square and Fisher’s exact test methods using IBM SPSS software version 29.0. Results: Out of 52 final reports, 25 were fatal. The study found that factors associated with a higher risk of fatal accidents are pilots in command with CPL, unpressurized aircraft, single-engine aircraft, aircraft with MTOW less than 5,700kg, accidents occurring at weekends, accidents occurring outside of airport premises, CFIT occurrences, and the cruise phase of flight. The factor associated with non-fatal accidents is the landing phase. Conclusion: Efforts such as enhancing pilot training and certification processes, implementing stricter safety regulations for small, unpressurized, single-engine aircraft, and increasing safety measures during weekends and specific phases of flight can reduce future fatal accidents.

Keywords: fatal accident, fixed-wing aircraft, commercial aviation

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Authors: Hang Yang, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

Abstract:

The system-of-systems (SoS) are utilized to characterize networks formed by integrating individual complex systems that demonstrate interdependence and interconnectedness. Research on the disintegration issue in SoS is significant in improving network survivability, maintaining network security, and optimizing SoS architecture. Accordingly, this study proposes an integrated framework called resilience-oriented disintegration in SoS (SoSRD), for modeling and solving the issue of SoS disintegration. Firstly, a SoS disintegration index (SoSDI) is presented to evaluate the disintegration effect of SoS. This index provides a practical description of the disintegration process and is the first integration of the network disintegration model and resilience models. Subsequently, we propose a resilience-oriented disintegration method based on reinforcement learning (RDRL) to enhance the efficiency of SoS disintegration. This method is not restricted by the problem scenario as well as considering the coexistence of disintegration (node/link removal) and recovery (node/link addition) during the process of SoS disintegration. Finally, the effectiveness and superiority of the proposed SoSRD are demonstrated through a case study. We demonstrate that our proposed framework outperforms existing indexes and methods in both node and link disintegration scenarios, providing a fresh perspective on network disintegration. The findings provide crucial insights into dismantling harmful SoS and designing a more resilient SoS.

Keywords: system-of-systems, disintegration index, resilience, reinforcement learning

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