An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.<br/

From Network to Web dimension in supply chain management

Cette thèse soutient que la dimension réseau, étant actuellement la portée du domaine de la gestion de chaîne logistique, contraint l’avancement de ce domaine et restreint des innovations conceptuelles et fondamentales capables d’adresser les grands défis économiques, environnementaux et sociaux. Les concepts de chaîne et de réseau ne reflètent pas la complexité des flux physiques, informationnels et financiers générés par les interactions qui ont lieu dans des réseaux interconnectés. Ces concepts n’offrent pas les fondations théoriques pour supporter des interventions allant au-delà d’un seul réseau et laissent échapper des opportunités nécessitant une vision multi-réseau. Ainsi, la dimension “web”, celle des réseaux de réseaux, est proposée comme une extension de la dimension réseau. Cette extension peut être vue comme l’étape naturelle suivante dans la progression qui a commencé par le niveau de gestion des opérations internes, est passée au niveau de la chaîne logistique et se trouve actuellement au niveau du réseau logistique. Après l’investigation théorique des raisons et de la façon d’intégrer la dimension web dans le domaine de la gestion de la chaîne logistique, la thèse étudie des implications importantes de cette intégration sur la collaboration inter-organisationnelle et le processus de prise de décision dans des environnements de webs logistiques. Elle démontre, en exploitant l’exemple des réseaux interconnectés ouverts, des potentialités inimaginables sans une vision web. Une méthodologie de conception d’un modèle de simulation permettant l’évaluation et la comparaison des webs ouverts par rapport aux webs existants est proposée. Puisque l’aide à la décision est une composante importante de la gestion de la chaîne logistique, la thèse contribue à déterminer les besoins des gestionnaires et à identifier les lignes directrices de la conception des outils d’aide à la décision offrant le support adéquat pour faire face aux défis et à la complexité des webs logistiques. Ces lignes directrices ont été compilées dans un cadre de conception des logiciels d’aide à la décision supportant la dimension web. Ce cadre est exploité pour développer quatre applications logicielles offrant aux praticiens et aux chercheurs des outils nécessaires pour étudier, analyser et démêler la complexité des webs logistiques.This thesis argues that the network dimension as the current scope of supply chain management is confining the evolution of this field and restricting the conceptual and fundamental innovations required for addressing the major challenges imposed by the evolution of markets and the increased intricacies of business relationships. The concepts of chain and network are limitative when attempting to represent the complexity of physical, informational and financial flows resulting from the interactions occurring in overlapping networks. They lack the theoretical foundations necessary to explain and encompass initiatives that go beyond a single chain or network. They also lead to overlook substantial opportunities that require beyond a network vision. Therefore, the “web” dimension, as networks of networks, is proposed as an extension to the network dimension in supply chain management. This new scope is the natural next step in the progression from the internal operations management level to the supply chain level and then to the supply network level. After a theoretical investigation of why and how the web dimension should be integrated into the supply chain management field, the thesis studies and discusses important implications of this integration on inter-organisational collaboration and of the decision-making processes in the logistic web environments. It demonstrates through the example of open interconnected logistic webs some of the potentials that cannot be imagined without a web vision. A methodology for designing a simulation model to assess the impact of such open webs versus existing webs is proposed. Since decision support is a key element in supply chain management, the thesis contributes to determine the needs of supply chain managers and identify the important axes for designing decision support systems that provide adequate assistance in dealing with the challenges and complexity presented by logistic web environments. The identified elements result in the establishment of a foundation for designing software solutions required to handle the challenges revealed by the web dimension. This conceptual framework is applied to the prototyping of four applications that have the potential of providing practitioners and researchers with the appropriate understanding and necessary tools to deal with the complexity of logistics webs

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Regenerating the Logistics Industry through the Physical Internet Paradigm: A Systematic Literature Review and Future Research Orchestration

The operations of other businesses rely heavily on the logistics sector, making logistics the most crucial industry. There are multiple issues available in the existing logistics sector including the lack of logistics optimization and unsustainability operations. To deal with these issues, the concept of the Physical Internet (PI) has arisen. For this reason, the current study conducts a comprehensive literature review to determine how PI has emerged within the logistics sector to improve its condition. In this research, we present a comprehensive and in-depth analysis of the present situation of the PI in the logistics literature by conducting a systematic review of 114 publications published in 39 top journals on the topic between 2007 and 2022. This paper makes three significant contributions to the existing literature using such an analysis. To begin with, it provides an overarching context for the part played by the PI in the overall logistics industry. Second, it provides a road layout of the breadth and depth of the research on PI and the overall logistics, including the approaches taken by researchers, regions covered, sectors examined, and theoretical stances taken by those who have explored the topic thus far. Finally, it addresses the conclusions based on the different clusters discovered and the issues of existing logistics systems along with the moderators of influencing the PI and its outcomes. Given the rising significance of PI and channels for sustainability in the logistics sector, this is the first time that an effort has been made to investigate the function of the PI within the context of overall logistics performance. The paper identifies crucial gaps in research and brings to light different factors that have the potential to shed light on this essential subject. Furthermore, we argue that there is an immediate need to build new business models for enhanced adaptation and execution of the PI strategy, and we urge business managers, academics, and regulators to consider it. In addition, we advocate that professionals scrutinize ways in which the PI approach may be applied to the existing business structures

Big data analytics for intra-logistics process planning in the automotive sector

The manufacturing sector is facing an important stage with Industry 4.0. This paradigm shift impulses companies to embrace innovative technologies and to pursuit near-zero fault, near real-time reactivity, better traceability, and more predictability, while working to achieve cheaper product customization. The scenario presented addresses multiple intra-logistic processes of the automotive factory Volkswagen Autoeuropa, where different situations need to be addressed. The main obstacle is the absence of harmonized and integrated data flows between all stages of the intra-logistic process which leads to inefficiencies. The existence of data silos is heavily contributing to this situation, which makes the planning of intra-logistics processes a challenge. The objective of the work presented here, is to integrate big data and machine learning technologies over data generated by the several manufacturing systems present, and thus support the management and optimisation of warehouse, parts transportation, sequencing and point-of-fit areas. This will support the creation of a digital twin of the intra-logistics processes. Still, the end goal is to employ deep learning techniques to achieve predictive capabilities, all together with simulation, in order to optimize processes planning and equipment efficiency. The work presented on this thesis, is aligned with the European project BOOST 4.0, with the objective to drive big data technologies in manufacturing domain, focusing on the automotive use-case

From Graph Coloring to Receptor Clustering

1. Hued colorings for planar graphs, graphs of higher genus and K4-minor free graphs.;For integers k, r \u3e 0, a (k,r) -coloring of a graph G is a proper coloring of the vertices of G with k colors such that every vertex v of degree d(v) is adjacent to vertices with at least min{lcub}d(v) ,r{rcub} different colors. The r-hued chromatic number, denoted by Xr (G), is the smallest integer k for which a graph G has a ( k,r)-coloring. A list assignment L of G is a function that assigns to every vertex v of G a set L(v) of positive integers. For a given list assignment L of G, an ( L,r)-coloring of G is a proper coloring c of the vertices such that every vertex v of degree d(v) is adjacent to vertices with at least min{lcub} d(v),r{rcub} different colors and c(v) epsilon L(v). The r-hued choice number of G, XL,r(G), is the least integer k such that every list assignment L with | L(v)| = k, ∀ v epsilon V(G), permits an (L,r)-coloring. It is known that for any graph G, Xr(G) ≤ XL,r( G). Using Euler distributions, we proved the following results, where (ii) and (iii) are best possible. (i) If G is planar, then XL,2(G) ≤ 6. Moreover, XL,2( (G) ≤ 5 when Delta (G) ≤ 4. (ii) If G is planar, then X2( G) ≤ 5. (iii) If G is a graph with genus g(G) ≥ 1, then XL,2 (G) ≤ ½ 7+1+48gG .;Let K(r) = r + 3 if 2 ≤ r ≤ 3, and K(r) = 3r/2+1 if r≥ 4. We proved that if G is a K4-minor free graph, then (i) Xr(G) ≤ K(r), and the bound can be attained; (ii) XL,r(G) ≤ K( r)+1. This extends a previous result in [Discrete Math. 269 (2003) 303--309].;2. Quantitative description and impact of VEGF receptor clustering .;Cell membrane-bound receptors control signal initiation in many important cellular signaling pathways. Microscopic imaging and modern labeling techniques reveal that certain receptor types tend to co-localize in clusters, ranging from a few to hundreds of members. Here, we further develop a method of defining receptor clusters in the membrane based on their mutual distance, and apply it to a set of transmission microscopy (TEM) images of vascular endothelial growth factor (VEGF) receptors. We clarify the difference between the observed distributions and random placement. Moreover, we outline a model of clustering based on the hypothesis of pre-existing domains that have a high affinity for receptors. The observed results are consistent with the combination of two distributions, one corresponding to the placement of clusters, and the other to that of random placement of individual receptors within the clusters. Further, we use the preexisting domain model to calculate the probability distribution of cluster sizes. By comparing to the experimental result, we estimate the likely area and attractiveness of the clustering domains.;Furthermore, as VEGF signaling is involved in the process of blood vessel development and maintenance, it is of our interest to investigate the impact of VEGF receptors (VEGFR) clustering. VEGF signaling is initiated by binding of the bivalent VEGF ligand to the membrane-bound receptors (VEGFR), which in turn stimulates receptor dimerization. To address these questions, we have formulated the simplest possible model. We have postulated the existence of a single high affinity region in the cell membrane, which acts as a transient trap for receptors. We have defined an ODE model by introducing high- and low-density receptor variables and introduce the corresponding reactions from a realistic model of VEGF signal initiation. Finally, we use the model to investigate the relation between the degree of VEGFR concentration, ligand availability, and signaling. In conclusion, our simulation results provide a deeper understanding of the role of receptor clustering in cell signaling

Analysis Tools for Small and Big Data Problems

The dissertation focuses on two separate problems. Each is informed by real-world applications. The first problem involves the assessment of an ordinal measurement system in a manufacturing setting. A random-effects model is proposed that is applicable to this repeatability and reproducibility context, and a Bayesian framework is adopted to facilitate inference. This first problem is an example of an analysis tool to solve a small data problem.;The second problem involves statistical machine learning applied to big data problems. As more and more data become available, a need increases to automate the ability to identify particularly relevant features in a prediction or forecasting context. This often involves expanding features using kernel functions to better facilitate predictive capabilities. Simultaneously, there are often manifolds embedded within big data structures that can be exploited to improve predictive performance on real data sets. Bringing together manifold learning with kernel methods provides a powerful and novel tool developed in this dissertation.;This dissertation has the advantage of contributing to a more-classical problem in statistics involving ordinal data and to cutting edge machine learning techniques for the analysis of big data. It is our contention that statisticians need to understand both problem types. The novel tools developed here are demonstrated on practical applications with strong results

The relevance of space analysis in warehouse management

Customers are becoming increasingly demanding and logistics is gaining more importance in order to assure customer satisfaction in terms of money, quality and time. The goal of this paper is to show that warehouses can be a source of competitive advantage and that a critical approach towards the existing available space may lead to increases in capacity with low investment. To accomplish this, a company is used as a model and suggestions for improvements are carried out. Regarding the capacity, a volume approach is taken into consideration, both in terms of the products and regarding the bins. Moreover, opportunities to achieve better space usage are presented by suggesting additional or different bins. Therefore, it is possible to reduce costs, to increase safety, to increase capacity up to 9,77%, and to help minimizing traveling distances and damages caused to the items.info:eu-repo/semantics/publishedVersio