Logistics Resources Research Articles

Digital twin‐based production logistics resource optimisation configuration method in smart cloud manufacturing environment

AbstractTo adapt to the dynamic, diverse, and personalised needs of customers, manufacturing enterprises face the challenge of continuously adjusting their resource structure. This has led manufacturers to shift towards a smart cloud manufacturing mode in order to build highly flexible production logistics (PL) systems. In these systems, the optimal configuring of PL resources is fundamental for daily logistics planning and vehicle scheduling control, providing necessary resources for the entire PL segment. However, traditional resource configuration methods face limitations, such as incomplete information acquisition, slow response in resource configuration, and suboptimal configuration results, leading to high subsequent operational costs and inefficient logistics transportation. These issues limit the performance of the PL system. To address these challenges, the authors propose a digital twin‐based optimisation model and method for smart cloud PL resources. The approach begins with constructing an optimisation model for the PL system considering the quality of service for a cloud resource is constructed, aiming to minimise the number of logistics vehicles and the total cost of the PL system. Additionally, a DT‐based decision framework for optimising smart cloud PL resources is proposed. Alongside a DT‐based dynamic configuration strategy for smart cloud PL resources is designed. By developing a multi‐teacher grouping teaching strategy and a cross‐learning strategy, the teaching and learning strategies of the standard teaching‐learning‐based optimisation algorithm are improved. Finally, numerical simulation experiments were conducted on the logistics transportation process of a cooperating enterprise, verifying the feasibility and effectiveness of the proposed algorithms and strategies. The findings of this study provide valuable references for the management of PL resources and algorithm design in advanced manufacturing modes.

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Enhancing Distribution Efficiency Through OTIF Performance Evaluation

Today’s market with more frequent but smaller deliveries complicates the realization of logistics processes and activities. Customers expect products within 24 h or 48 h. This poses a logistics challenge for companies in fulfilling the expected time window. In these situations, companies can either perform self-distribution or they can outsource it. When outsourcing, companies must define key performance indicators (KPIs) to measure the efficiency of the distribution process. One such KPI is On-Time In-Full (OTIF). OTIF fulfillment is increasingly becoming a fundamental requirement not only in distribution but also throughout the entire supply chain. Two essential prerequisites for successful outsourcing collaboration are ensuring that orders are delivered on time and in full. A review of the existing literature revealed that no studies simultaneously address this complex issue. This was exactly the main motive for developing a model that simultaneously takes into account the utilization of logistics resources on one hand, and OTIF on the other when calculating the efficiency. The model developed in this paper was tested on the data of a company operating in the Serbian market. The results showed that seven of the company’s warehouses are inefficient. A two-stage matrix analysis with the three most important indicators (operational efficiency, on-time delivery, and in-full delivery) was performed based on the obtained results. Other than the obvious scientific contributions, the proposed model can help companies increase their resource utilization efficiency as well as OTIF fulfillment.

Underpowered studies in muscle metabolism research: Determinants and considerations

Biomedical research frequently employs null hypothesis testing to determine whether an observed difference in a sample is likely to exist in the broader population. Null hypothesis testing generally assumes that differences between groups or interventions are non-existent, unless proven otherwise. Because biomedical studies with human subjects are often limited by financial and logistical resources, they tend to have low statistical power, i.e. a low probability of statistically confirming a true difference. As a result, small but potentially clinically important differences may be overseen or ignored simply due to the absence of a statistically significant difference. This absence is often misinterpreted as ‘equivalence’ of treatments. In this educational paper, we will use practical examples related to the effects of exercise and nutrition on muscle protein metabolism to illustrate the most important determinants of statistical power, as well as their implications for both investigators and readers of scientific articles.Changes in muscle mass occur at a relatively slow rate, making it practically challenging to detect differences between treatment groups in a long-term setting. One way to make it ‘easier’ to differentiate between groups and hence increase statistical power is to have a sufficiently long study duration to allow treatment effects to become apparent. This is especially relevant when comparing treatments with relatively small expected differences such as the effect of modest changes in daily protein intake. Secondly, one could try to minimize the variance and response heterogeneity within groups, for example by using strict inclusion criteria and standardization protocols (e.g., meal provision), by using cross-over designs, or even within-subject designs where two interventions are compared simultaneously (e.g., studying an exercised limb vs a contralateral control limb) although this might limit the generalizability of the findings (e.g. such single-limb exercise training is not common in practice). In terms of data interpretation, investigators should obviously refrain from drawing strong conclusions from underpowered studies. Yet, such studies still provide valuable data for meta-analyses. Finally, because muscle protein synthesis rates are highly responsive to anabolic stimuli, acute metabolic studies are more sensitive to detect potentially clinically relevant differences in the anabolic response between treatments. Apart from further elaborating on these topics, this educational article encourages readers to more critically question null findings and scientists to more clearly discuss limitations that may have compromised statistical power.

Advanced Analysis of Scalability and Mobility in Cloud Servers using Logistic Regression Models.

With the rapid expansion of cloud computing, efficiently managing scalability and mobility has become paramount. This paper presents an in-depth examination of how logistic regression models can predict and optimize these two critical aspects of cloud infrastructure. By analyzing various cloud configurations, workload patterns, and system performance data, the logistic regression framework forecasts resource needs and identifies potential bottlenecks. The study incorporates real-world applications and case studies to demonstrate the practical benefits of logistic regression in enhancing cloud performance. Additionally, the paper compares logistic regression with other predictive models and explores future advancements in cloud optimization through AI-driven automation and real-time machine learning. The findings underscore the importance of predictive analytics in maintaining seamless cloud operations and provide actionable insights for cloud architects aiming to design more resilient and efficient cloud systems.

Telehealth Outreach Program for Child Traumatic Stress: Strategies for Long-Term Sustainability.

There are high documented rates of exposure to traumatic events and mental health disorders among youths yet existing disparities in access to care for racial and ethnic minority youths and youths in rural communities. Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) is an evidence-based behavioral health therapy for children. The delivery of TF-CBT via telehealth can decrease access to care barriers. An interdisciplinary clinical team developed a training program to guide clinicians to effectively provide TF-CBT via telehealth. The goal of this study was to describe variation in implementation processes of the telehealth TF-CBT program and identify barriers and facilitators to program implementation post-training, which were utilized to develop implementation strategies for intervention sustainability. Using a mixed-methods approach, data were collected on telehealth implementation processes and facilitators and barriers to the delivery of telehealth TF-CBT. This study was guided by an adapted implementation science framework, namely the Exploration, Preparation, Implementation, Sustainment model. Interviews and surveys were completed with clinical site leaders who had participated in the telehealth TF-CBT training. Throughout clinical sites, there was varied adoption and penetration of the telehealth TF-CBT program. Facilitators to implementation included leadership and site staff buy in, community needs, and training resources, while barriers included funding, available logistical resources, and child and family involvement. The feedback gained from this project assisted in the development of implementation strategies for increased adoption and sustainment of TF-CBT delivered via telehealth. Strategies include ongoing interactive assistance and resource support, enhanced training for stakeholders, and program adaptations, with the goal to increase access to quality mental health care for underserved populations.

Multi-stage resource leveling problem with self-operation and outsourcing cooperation in sharing logistics

Self-operation and outsourcing cooperation have emerged as the prevailing operating mode for logistics companies in the realm of sharing logistics. This mode facilitates the rational and effective coordination of multiple resources for task assignment. However, the competition and cooperation relationship between self-operation and outsourcing remain largely unexplored, leaving logistics companies grappling with the trade-off between cost-effectiveness and risk. To address this gap, we propose a multi-stage resource leveling model that accounts for the fuzzy outsourcing resources, the efficacy of intra-stage resources, and the stability of inter-stage resources. We employ an improved gravitational search algorithm to solve this model efficiently. Inspired by Markowitz's portfolio theory, we propose a resource portfolio strategy under the self-operation and outsourcing cooperation mode based on the mean-variance model to better weigh up the “cost-effectiveness” and “risk” exhibited by logistics resources. The strategy uses expected returns and variance to evaluate the benefits and risks, thereby categorizing allocation schemes according to risk levels. Finally, through experiment analysis, we discuss the optimal scale of self-operation and outsourcing resources for logistics subtasks at different risk levels. This analysis equips logistics companies with the insights needed to make informed decisions aligned with their risk preferences and overall benefits, offering crucial managerial implications for business and strategic decisions.

Evaluation of the sustainable development of logistics based on local characteristics: Matching logistics architecture with resources.

This paper sets out to investigate the alignment between the logistics architecture and resources across China's nine logistics regions, while also examining the individual advantages these regions possess. With the goal of establishing an evaluative framework for sustainable logistics development, the research introduces the principle of logistics equilibrium to augment traditional evaluation metrics, thus forming an assessment index system designed to quantify the congruence between logistics architecture and resources.Utilizing a democratic evaluation approach that emphasizes individual regional advantages, and leveraging empirical data from the decade spanning 2011 to 2020, this paper reveals a general upward trajectory in the matching level of logistics resources to architecture albeit with notable regional disparities. It is observed that while certain areas have made significant strides, others lag, underscoring the varied pace of development among different logistics regions.The research also brings to light the distinct advantage characteristics that emerge as each region evolves, suggesting that these attributes can be harnessed to foster a more environmentally conscious and forward-thinking approach to logistics. By implementing a democratic evaluation to rank these regions, this paper aims to identify exemplars that are not only recognized for their achievements but also serve as models for the formulation of green and sustainable logistics strategies tailored to each region's specific needs and potential.

The Intelligent Upgrading of Logistics between an Internet Enterprise and a Logistics Enterprise Based on Differential Game Theory

At the background of “Internet + Logistics”, intelligent logistics has high operational efficiency and provides a superior customer experience, meeting the requirements of sustainable development. It also plays a crucial role in promoting the modernization of the industrial chain in China. This paper develops a mathematic model based on differential game theory, which sets the intelligent level of logistics and the goodwill of intelligent logistics as state variables. The research reveals the collaborative strategies between a logistics enterprise and an Internet enterprise for the intelligent upgrading of logistics, and separately calculates the optimal effort levels and optimal revenues of participating enterprises under the non-cooperative mechanism, the cost-sharing mechanism, and the cooperative mechanism. This paper also observes the crucial parameters that affect the optimal revenue for the intelligent upgrading of logistics. The research findings are as follows: (1) Cost-sharing mechanism and cooperative mechanism can motivate the Internet enterprise and the logistics enterprise to improve their effort levels and increase the total revenue, which achieves the Pareto improvement. Under the cooperative mechanism, the intelligent level of logistics and the goodwill of intelligent logistics are the highest. (2) Participating enterprises can promote the intelligent upgrading of logistics by accumulating innovative resources for intelligent logistics, attaining cost-conversion efficiency, and cultivating customer preferences for intelligent logistics services. (3) When the revenue-sharing ratio of the logistics enterprise is relatively low, although the cost-sharing mechanism can continuously motivate a logistics enterprise to make an effort in the intelligent upgrading of logistics, it is not conducive to enhance the goodwill of intelligent logistics. This paper highlights the pivotal role of enterprise collaboration in the intelligent upgrading of logistics, and proposes practical recommendations.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Logistic transportation role for sustainable ecotourism industry

Abstract Transportation plays a pivotal role in the industrial landscape, and the ecotourism sector, particularly sustainable ecotourism, is no exception. Sustainable ecotourism places emphasis on the responsible management of natural resources, aiming to foster comprehension, appreciation, and the conservation of both the environment and cultural heritage. The purpose of this research is to examine how the emergence of logistics transportation studies is driven by the transportation needs of island communities and tourists to increase sustainable ecotourism industry activities. Logistics transportation services that support ecotourism is a relatively new study of existing logistics resources in Indonesia. This study chooses sea transportation routes for the Karimunjawa Islands and Jepara. The logistics services for tourism transportation provided by the government and third parties pay little attention to sea transportation routes that pass-through conservation areas, namely the Panjang Island Conservation Area and Karimun Jawa National Park. Indeed, there has been a sea route provided, but several obstacles arose when passing through the conservation area. The establishment of novel transportation routes emerges as a viable solution for bolstering the sustainable ecotourism sector and addressing the transportation requisites of the local community. Additionally, these new transportation routes can yield positive outcomes for the safeguarding of conservation areas.

Embedding work coaches in GP practices: Findings from an interview-based study in the UK

BackgroundThe integration of work coaches (WCs) and disability employment advisors (DEAs) into General Practitioner (GP) practices in the UK aims to address the interplay between health and employment by facilitating access to employment support, especially to people with disabilities and health conditions affecting their ability to work. This study seeks to explore the perspectives and perceptions of WCs, DEAs and GPs regarding the benefits and challenges of embedding WCs and DEAs in GP practices. MethodsData was collected between May and July 2023 through semi-structured interviews with four GPs, four WCs and four DEAs working in NHS GP practices. The interviews were audio-recorded, transcribed verbatim and thematically analysed using the Thematic Framework method. Emergent themes were pre-structured and classified as perceived benefits, barriers and challenges or drivers and strategies. ResultsThe integration of WCs & DEAs within GP practices was perceived by respondents as fostering a direct collaboration between professionals as well as a greater openness of patients which benefited patients, WCs/DEAs and GPs. While all respondents emphasised the various benefits, they also identified several barriers and challenges. The implementation stage was perceived as particularly challenging, especially by WCs and DEAs, whereas the lack of human, financial and logistical resources hindered the service beyond this stage. Several strategies and drivers to support the service were identified, including the importance of receiving support from all professionals involved as well as making the service visible to both healthcare professionals and patients. ConclusionEmbedding WCs and DEAs in GP practices emerges as a promising approach which can benefit patients, GPs and WCs/DEAs. Exploring patients' perspectives directly is crucial to fully assess this type of service and identify any additional challenges and benefits.

A Flow Shop Scheduling Method Based on Dual BP Neural Networks with Multi-Layer Topology Feature Parameters

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.

The use of the WHO criteria to detect severe malaria among patients clinically diagnosed with uncomplicated malaria.

The World Health Organization (WHO) strict defining criteria were used to identify severe malaria among Ghanaian patients clinically diagnosed as uncomplicated malaria. From each study participant, blood haemoglobin (Hb) and plasma bilirubin levels were estimated using automated analyzers. According to the WHO, the criteria for diagnosing severe malaria among children (< 12 years) was assessed using Hb < 5 g/dL and among other patients ≥ 12 years, Hb < 7 g/dL with parasitemia > 10,000/μL, plasma bilirubin > 50 μmol/L amidst parasitemia > 100,000/μL and P. falciparum hyperparasitaemia (> 500,000 parasites/μL). Patients initially diagnosed with asymptomatic malaria (n = 347) were recruited. The parasitemia range was 540-863,402 parasite/μL. Overall, 86.2% of the patients had uncomplicated malaria while 13.8% of the patients were diagnosed with severe malaria of various origins. In children < 12 years, 10.8% (17/157) had Hb < 5g/dL with parasitaemia < 10,000 parasites/μL and in other patients (≥ 12 years), 6.3% (12/190) of them recorded Hb < 7g/dL with parasitaemia < 10,000 parasites/μL. Furthermore, 13.8% (48/347) had serum bilirubin levels > 50 μmol/L with parasitemia > 100,000/μL. In all the patients with hyperbilirubinemia, Hb levels fell below either 5g/dL or 7g/dL, for patients less than and 12 years or more, respectively. Finally, 1.7% (6/347) of the patients with malaria had parasite counts (> 500,000 parasites/μL). Irrespective of the etiology, patients diagnosed with severe malaria presented with pallor, vomiting, diarrhea, chills, fever and nausea, concurrently. Without comprehensive laboratory evaluation, patients with severe malaria could be misdiagnosed. Therefore, healthcare facilities need adequate human and logistical resources to be able to diagnose severe malaria for appropriate management to avert any untoward outcomes.

Dynamic changes and improvement paths of China's emergency logistics response capabilities under public emergencies-research based on the entropy weight TOPSIS method.

Due to global industrialization and urbanization, natural disasters, accidents, and public health emergencies happen frequently. These events cause significant loss of life and property damage to countries worldwide. In the context of frequent public emergencies, enhancing emergency logistics response capabilities is crucial, ensuring rapid supply of rescue materials and support for rescue personnel, thereby saving lives and reducing economic losses. In order to identify the changes and enhancement paths of the emergency logistics response capability of Chinese regions under the shocks of public emergencies, this paper innovatively constructs emergency logistics response capability measurement indicators. This paper uses the entropy weight TOPSIS method and panel quantile regression model to quantify the change and enhancement paths of China's regional emergency logistics response capability under different events. It is found that: (1) The gap in emergency logistics response capability among Chinese regions is widening, with the internal difference in the eastern region higher than that in the west, while the difference in the central region is relatively low. (2) China's emergency management department can effectively transform social logistics into emergency logistics, thereby promoting the improvement of emergency logistics response capabilities. (3) Sudden geological disasters break through the limits of social logistics resources when they cross lower scales, resulting in the failure of emergency logistics response capabilities. This paper expands research on assessing emergency logistics capabilities, addressing issues in existing assessments such as reliance on single indicators and subjective measurement methods. Additionally, it quantifies the dynamic changes in China's regional emergency logistics response capabilities under public emergencies by extending the study of event content, types, and impacts. This enhances discussions on the effects of public emergencies. Finally, from an empirical perspective, the paper explores pathways to enhance regional emergency logistics response capabilities in China. In practice, this paper assists countries worldwide in assessing whether different regions of China can effectively provide emergency support for various resources in direct investments, thus providing a scientific basis for investment decisions.

Enhancing Teacher Quality: The Role of Digitalization in Transforming Educational Supervision

This research aims to develop a system-based academic supervision model to address specific inefficiencies in the supervision process, such as logistical challenges and resource limitations. The purpose of this research is to develop a system-based academic supervision model in overcoming the ineffectiveness of the academic supervision process. The method in this research is development research using the Borg and Gall model which consists of 10 steps of product development from preliminary research to product dissemination . The research subjects were teachers and school supervisors in Salatiga City, especially in elementary schools consisting of public and private schools. The selection of subjects is based on subjects with criteria, namely subjects who play an active role in the academic supervision process and have experience in academic supervision at school. The instruments in this study were observation guidelines, interview guidelines, user perception questionnaire, and product trial questionnaire. The research instruments have been validated by expert validators and meet the criteria of valid and reliable. The results showed that the development of the Management Information System for academic supervision of the Borg and Gall model resulted in a product that met the criteria of valid, effective and practical for academic supervision. The research findings show that using the academic supervision system makes it easier for supervisors and teachers to carry out the academic supervision process. In addition, the efficiency of supervision becomes better, because it can overcome the distance and the limited number of supervisors to the number of schools whose ratio is not balanced.In addition, the use of an academic supervision system speeds up the supervision process, simplifies the use of assessment instruments, and is practically used flexibly.

Impact of Technology on Human Resources in Marine Logistics

Technology has significantly changed the landscape of the ocean logistics industry, affecting the roles, responsibilities, and skill needs of human resources. This research aims to explore the impact of technologies such as automation, digitalization, and information systems on human resources in ocean logistics. Through a qualitative and quantitative approach that includes a literature review, online surveys, and in-depth interviews, key findings reveal a shift in worker roles towards automation system oversight, data analysis, and strategic decision-making. New skills such as programming, data analytics, and understanding automation systems are becoming crucial. However, the industry faces challenges in recruiting a workforce with the required skills and resistance from workers to change. Strategies such as extensive training programs, collaboration with educational institutions, and effective change management help integrate technology with human resources. Technology adoption also drives changes in organizational structures and work cultures that are more collaborative and data-oriented. This research provides important insights for companies and policymakers in developing strategies to prepare a competent marine logistics workforce for digital transformation.

Nephrological care in Ukraine during wartime: challenges and innovations

The war in Ukraine has profoundly impacted the provision of nephrological care, presenting numerous challenges while also driving significant innovations. This study aims to analyze the availability and quality of nephrological care in Ukraine during wartime, focusing on chronic kidney disease stages 1–5D/T. The conflict has caused population displacement, restricted access to treatment, and disrupted healthcare logistics, adversely affecting nephrological care. Despite these challenges, the healthcare community has adapted through the implementation of virtual nephrology, incremental and extended dialysis regimens, and increased use of new therapy approaches. The war led to a decrease in the population and migration of medical personnel, particularly from southeastern to western Ukraine, causing staffing imbalances that were gradually addressed. Technological innovations, such as digital nephrobiopsy with immunohistochemical analysis and the use of cloud-based technology, allowed for rapid and remote diagnosis. Virtual healthcare and remote consultations ensured continuity of care, while kidney transplantation activities saw a notable increase, facilitated by advanced techniques and international aid. Despite initial logistical challenges and resource shortages, the resilience and adaptability of the Ukrainian nephrological care system have been evident. This study underscores the importance of robust contingency planning, international collaboration, and the adoption of innovative solutions to maintain healthcare delivery in conflict zones. The findings offer valuable insights into managing nephrological care under extreme conditions and highlight the potential for these innovations to improve care delivery globally.

The effect of subjective well-being on the management of successful organizations

Purpose: The objective of this research was to analyze the effect of subjective well-being on successful organizational management through the study and measurement of the variables: quality of work life, job satisfaction and happiness at work.Design/methodology/approach: This research has a positivist or quantitative approach of a correlational type at a descriptive level and corresponds to a population sample of 302 workers randomly selected from a total of nine hundred collaborators hired by the Universities of Barranquilla, Colombia.Findings: In order to predict the behavior of a dependent variable based on other independent variables, the multiple regression model was used, so the statistical data indicate that subjective well‑being index can increase or decrease in organizations and derives from the perception of quality of work life of employees. In this research, job satisfaction was the factor with the highest score, followed by the quality of work life variable, then happiness at work, and due to these factors, subjective Well-being at work is generated. The conclusions of the research denote the importance for organizational management to know the perception of workers. The data generated through this measurement will allow companies to personalize their work well-being programs, and to invest better in their logistical and human economic resources, as well as how to create business strategies that promote higher levels of commitment and work productivity, this diagnostic measurement model helps human talent areas to be more aligned with the real needs and expectations of employees. The analysis of the study concludes that the development of strategies to improve the subjective well-being of employees promotes successful organizational management.Originality/value: This research analyzes subjective well-being at work, because most of the studies carried out on this topic focus on subjective well-being from a personal perspective, emphasizing how citizens evaluate their lives in general terms. This article is focused on aspects related to the perception of workers in the work context based on the measurement of three variables in an integrated manner; In general, other research has analyzed the variables individually and related them to other aspects such as the work environment or productivity. The originality of this study lies in the integrated analysis of the three variables quality of work life, satisfaction and happiness at work as predictors of SWB at work. The data from this measurement model provides relevant information so that organizations can make better decisions regarding their policies for attraction, loyalty and retention of human capital, in this way they can design strategies, create incentive and benefit plans, according to the employee’s expectations and needs.

The mediating effect of service quality on the relationship between logistics resources and sustainable competitive advantage in Iraqi Kurdistan hotels

The mediating effect of service quality on the relationship between logistics resources and sustainable competitive advantage in Iraqi Kurdistan hotels