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Optimization of Textile SCs Design and Planning: A Bibliometric Analysis
(Springer Nature Switzerland, 2025)
Leandro L. Lorente-Leyva
;
María del Mar Eva Alemany
;
Diego H. Peluffo-Ordóñez
This study provides a comprehensive overview of the current state of research in the design and operations planning optimization of textile supply chains. This paper includes a bibliometric analysis of 42 selected papers that reveals a focus on tactical decision-making, with an emphasis on the manufacturing stage. The bibliometric analysis exposes the influence of prominent authors and journals in this field. A co-occurrence map graphically represents the keywords used in the papers. The literature review results suggest that models are primarily developed in a deterministic context, with a focus on cost minimization and limited use of artificial intelligence techniques. The solution context exhibits a preference for static approaches and periodic replanning.
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Automation of Filter Press Drive Control for Enhanced Palm Oil Extraction
(International Information and Engineering Technology Association, 2025-01-31)
CORDOVES GARCIA, ALEXIS
;
Agustín Moreira Orellana
;
Alexis Cordovés Rodríguez
;
Ricardo Avila Rondon
;
Leandro L. Lorente-Leyva
The study focuses on the automation of the drive system of a filter press in palm oil extraction plants, with the aim of optimizing operational efficiency, reducing manual intervention and improving process accuracy. The proposal replaces the manual drive with a motorized system controlled by a Programmable Logic Controller (PLC). A Siemens motor-reducer, model Z79-LE112MC4P-G040M-PN, was used, which guarantees a torque of 777.75 Nm and an output speed of 40 rpm, sufficient to efficiently tighten and release the filter press. The system control was implemented by a Siemens LOGO 230RC PLC, complemented by an AM2 analog expansion module to interpret strain gauge signals. These signals determine the exact moment to stop the motor-reducer once the programmed tightening force is reached. Programming was done in Ladder language using LOGOCONFORT software. The integration of these components improves system consistency and reliability, standardising processing times and reducing physical effort. The results highlight an increase in productivity and operational safety, aligning with modernisation trends in the palm oil industry. This advance represents a significant step towards greater sustainability and competitiveness of the sector.
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Data-enabled Bayesian inference for strategic maintenance decisions in industrial operations
(Elsevier BV, 2024-12)
Raúl Torres-Sainz
;
Leandro L. Lorente-Leyva
;
Yorley Arbella-Feliciano
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Carlos Alberto Trinchet-Varela
;
Lidia María Pérez-Vallejo
Efficient management of industrial assets and equipment depends heavily on the selection of appropriate maintenance strategies. This research presents a dataset generated through Monte Carlo simulations to evaluate 12 key criteria relevant to maintenance strategy selection. The dataset covers a wide range of potential maintenance scenarios, providing comprehensive data for researchers to explore various strategies in industrial settings. The data were normalized and structured in a way that facilitates their use for further modeling or analysis. The dataset offers an opportunity for researchers to reproduce the data collection process, enabling comparisons with their own studies. By providing this dataset, we aim to support the development of new models for maintenance strategy selection and encourage further exploration of data-driven approaches in industrial maintenance. Additionally, the dataset can serve educational purposes, assisting in the teaching of decision-making in the context of maintenance operations.
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Conceptual design vulnerability assessment of the housing light roofs to strong winds
(Growing Science, 2024)
Anabel Reyes-Ramírez
;
Roberto Andrés Estrada-Cingualbres
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Libys Martha Zúñiga-Igarza
;
Roberto Pérez-Rodríguez
;
Leandro L. Lorente-Leyva
Hurricanes are one of the most significant causes of human and material losses in the Caribbean region. These events have demonstrated their devastating impact on housing and infrastructure. The assessment of the vulnerability of buildings with light roofs, at the initial design stage, is considered to be a fundamental step in the mitigation of these damages and losses. This paper presents the introduction of an indicator-based vulnerability assessment in an effort to mitigate these damages in advance. This indicator facilitates the design team's decision to select the appropriate light roof alternative subject to strong winds at the conceptual stage of the process. The indicators that contribute to the conceptual assessment of vulnerability were identified based on a comprehensive review of the literature and numerical simulations of the risk scenarios using CFD/FEM software’s. The ranking of indicator weights was determined by the Kano method according to experts' opinions. A desktop application has been developed for the assessment of the vulnerability of light roof variants for buildings at the conceptual design stage. The results reported in a case study demonstrate the viability of the desktop application based on the vulnerability indicator to assist decision making in the conceptual design stage.
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A conceptual framework for the operations planning of the textile supply chains: Insights for sustainable and smart planning in uncertain and dynamic contexts
(Elsevier BV, 2024-01)
Leandro L. Lorente-Leyva
;
M.M.E. Alemany
;
Diego H. Peluffo-Ordóñez
Recent practices in textile supply chains (SC) show a growing concern for sustainability not only in its economic dimension, but fundamentally in its environmental and social ones. One of the key management processes that affect sustainability is the SC operations planning since its fundamental role in achieving a balance between supply and demand in a sustainable manner. Moreover, in an uncertain and dynamic environment such as the textile sector, it is necessary to provide a certain learning capability to the operations planning techniques used to increase the speed and quality of response of the textile SC to unexpected situations. In this context, mathematical programming models, heuristics and artificial intelligence techniques have proven their validity to achieve sustainable, robust and smart supply chains. Despite their potential, neither a conceptual framework (CF) nor a literature review have been detected to support the development and study of such models in the textile supply chain operations planning. In view of these gaps, this paper proposes a CF for supporting the sustainable and smart operations planning of the textile supply chains in a dynamic and uncertain context based on a set of dimensions, categories and elements that reflect the specific characteristics of the textile sector. Firstly, a tentative CF is predefined based on other generic works on SC operations planning in uncertain context and the own authors’ knowledge. Secondly, a structured literature review based on this CF has been made resulting, at the same time, in the updating of some of its dimensions, categories and elements to reflect some textile specific characteristics. Consequently, the CF is not only predefined but also logically derived from the literature analysis. The results of the literature review show that there is a great opportunity to contribute to making textile supply chains more sustainable, smart, flexible, robust and resilient in dynamic and uncertain environments.

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