High-level Services Research Articles

Satisfaction Level of Customers on E-CRM

Objectives: Commercial banks are moving from a product-centric approach to a customer-oriented approach. This shift reflects the current trend of prioritizing the customer, where the saying "Customer is the King" has become prevalent. Customers now have the option to compare various products and choose those that best meet their preferences. They also expect a high level of service, a tailored product offering, and a personalized delivery model. In this context, banks in Kerala, similar to other regions in India, must adapt quickly to meet these evolving expectations to remain competitive. The objective is to understand client needs and transform traditional CRM into electronic Customer Relationship Management (e-CRM). Methods: e-CRM represents an advanced version of CRM, enhanced with technology and focusing on specific market groups through direct marketing and sales services. However, implementing e-CRM effectively presents challenges. Often, organizations fail to consider the customer's needs fully, which impacts the system’s efficiency. The move towards e-CRM also reflects a broader shift from paper-based methods to digital processes in banking. Results: The banking industry has prioritized attracting new clients, retaining current ones, and enhancing customer loyalty and satisfaction. e-CRM systems address the needs of both consumers and banks, allowing banks to meet client demands more effectively while also adapting to modern digital expectations. This shift has enabled banks to improve customer engagement and offer more customized services. Conclusion: The transformation from traditional CRM to e-CRM represents a significant change in the banking sector, especially in regions like Kerala. As customer expectations rise, banks must respond quickly and effectively to meet these demands. e-CRM systems have become essential tools for banks to cater to the needs of modern consumers and to stay competitive in a rapidly evolving market. This approach underlines the importance of technology in enhancing customer relationships and highlights the banking industry's commitment to placing customers at the center of their operations.

How Cyber Security Enhances Trust and Commitment to Customer Retention: The Mediating Role of Robotic Service Quality

Cyber security is supportive of robotic service provision, the objective of which is to help marketers achieve their aim of providing a high level of service. Marketers need to be aware of cyber security issues and adhere to established cyber security policies. We investigate trust and commitment in relation to customer retention while assessing the mediating role of robotic service quality (RSQ). We employ a survey-based study that utilises 231 valid responses from customers in São Paulo, Brazil. To analyse the data, we used partial least squares structural equation modelling (PLS-SEM). The results show that trust and commitment have a positive impact on customer retention. RSQ has a partial mediation effect on the relationship between the latent constructs of trust, commitment, and customer retention. Thus, it can be suggested that RSQ, which embeds trust and commitment, assists in building a loyal customer base. Marketers outside the Latin American region can benefit from the results of this study since it incorporates cyber security awareness and policy within marketing strategy implementation, ensuring that RSQ is aligned in terms of the digitalisation goals of the company.

Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

DESIGNING AN E-KANBAN SYSTEM TO IMPROVE DRUG AVAILABILITY AND MINIMISE INVENTORY WASTE AT THE HOSPITAL PHARMACY INSTALLATION

In an effort to provide a high level of service to hospitals, pharmaceutical installations are often faced with problems in monitoring and determining adequate drug supply levels to respond to demand, both from outpatient and inpatient units. Inventory control by hospital pharmacies is often carried out without being supported by accurate information systems or control methods, resulting in excess inventory levels which leads to higher inventory costs. This study aims to identify and develop an e-kanban system suitable for the scope of hospital pharmacy. E-kanban as a method of controlling inventory through a simple reliable information system is expected to minimize inventory waste that occurs. The e-kanban system coupled with the use of barcodes and portable scanners is expected to provide more accurate information on drug availability. Inventory monitoring and control is expected to be much easier, both at the time of replenishment and during periodic stock-taking. The implementation of the e-kanban system is also able to eliminate waste, card use and manual recording activities, replaced by the use of simpler two-bin containers. The e-kanban system can also be integrated into the existing Hospital Information System, without disrupting the running system.

A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

This repository provides an end-to-end solution to the static rebalancing operations in bike sharing systems. The study uses the raw data of bike demand and station inventory status to generate the optimal rebalancing routes that reallocate system-wide bike inventories among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. The repository reports the raw data, algorithms, and extensive numerical experiments reported in the paper, using real-world data from New York City Citi Bike.

Investigation of Critical Aspects of Roughness Assessment for Airfield Pavements

One of the main priorities of airport authorities is to maintain a high level of serviceability of runway pavements due to the high safety requirements for aircraft at high speeds. Accordingly, the assessment of the functional condition of airfield pavements is crucial for the proper operation of an airport. The most critical functional parameter appears to be pavement roughness. It characterizes the condition of the runway surface and is directly related to the safety of aircraft flights, as it affects the handling characteristics and braking performance of the aircraft, the increase in operating costs, and the wear of the aircraft. Worldwide, there are several indices for assessing the roughness of airfield pavements. This study aims to compare some of these indices to assess their ability to capture the characteristics of airfield pavement roughness. For this purpose, roughness data were collected along a runway with flexible pavement at a regional airport in southeast Europe and corresponding indices were estimated. The analysis of the data leads to the most efficient index for assessing the roughness of airfield surfaces to date. However, the need for a new index that expresses the response of the aircraft remains a critical issue.

Derajat Aglutinasi pada Pemeriksaan Golongan Darah Metode Tabung Berdasarkan Masa Simpan Test Sel A dan Test Sel B Hari Ke-0, Ke-2, Ke-4, Ke-6 dan Ke-8

Blood group examination is an examination that aims to determine the type of blood group. Cell test is a blood group examination reagent used to detect antibodies in the serum being examined. The long shelf life of cell tests that can only last for two days is considered less effective for agencies with a high level of blood services. The purpose of this study was to describe the degree of agglutination in blood group examination with cell test A and B stored on the 0th, 2nd, 4th, 6th, and 8th days. This study is a descriptive study with a Quasi Experimental research design. Test cell A and test cell B were made from red blood cell specimens of 3 blood type A and 3 blood type B respectively. Test cells are stored in a refrigerator with a temperature of 2-6° C. Test cells were then examined on the 0th, 2nd, 4th, and 6th day of storage. Calculation of samples and repetitions using the Federer formula with the number of treatments in this study is 5 treatments. Based on the calculation, one sample of test cell A and test cell B was obtained with five repetitions of each examination. The results showed that on the 0th, 2nd, 4th, and 6th day of cell test storage, the results of blood type examination were obtained, namely the degree of agglutination 4+ with erythrocytes in the cell test clumping into one bond, cells forming large agglutination with clear supernatant. On the 8th day of storage, the result of agglutination degree is 3+ with erythrocytes in test cells not clumping perfectly, there are erythrocyte granules and cloudy supernatant. Based on the results of the study, it is concluded that test cell A and test cell B can be used optimally until day 6 storage.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

Simulation modeling of passengers flow at airport terminals to reduce delay and enhance level of service

Passenger delays are considered as global issues that influence the overall efficiency of an airport and competitiveness as well as restricting terminal developments. This paper studied Kuwait International Airport Terminal 4 (T4); a new expansion of the old airport. This study mainly aims to analyze the influence of the design on passenger flow in T4 and how it affected the capacity in addition to obtain a path for the passenger movement from the terminal entrance until they reach the gate of the aircraft and vice versa through CAST simulation model. The effect of the T4 design on the overall efficiency was found, the current flow conditions were analyzed and compared with the required Level of Service (LOS) and several developments and expansions were suggested. Passengers flow was studied to improve passenger experience, increase operational efficiencies, maximize retail revenues and increase security effectiveness. It was indicated that customs and security facilities require expansion in areas during peak hours to maintain a high level of performance. However, the check in premium showed the minimum gap between queue capacity and the declared queuing size indicating the high level of service in this area. The results revealed that immigration queueing requires expansion. Moreover, queuing area provision indicates no capacity issues to cope with maximum queue size. The provision of immigration counters according showed no adequate queuing space to cope with MQS. It is recommended to increase the number of economy check-in counters.

Machine-learning based prediction for high health care utilizers using a multi-institution registry

Abstract Background Rising healthcare costs worldwide have spurred interest in using machine learning to pinpoint major healthcare utilizers for population health efforts. Previous studies have focused on individuals who impose the highest financial burden, such as targeting high-need high-cost utilizers, a small group with limited potential for cost reduction. Therefore, we developed models to predict future healthcare utilization across various thresholds. Methods We leveraged data from a multi-institutional diabetes database to develop binary classification models capable of predicting healthcare utilization in the following year. The models predicted six different outcomes: patients with an annual total length of stay of ≥ 7, ≥14, and ≥30 days, and emergency department (ED) attendance of ≥ 3, ≥5, and ≥10 visits. To mitigate class imbalance, we applied random and synthetic minority oversampling techniques and compared against models trained without oversampling. Models were trained with 2019 data and tested on unseen data from 2020 and 2021. Results Models trained with random oversampling, including logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron, consistently demonstrated high AUC (>0.80) and sensitivity (>0.60) across both training-validation and test datasets. Addressing class imbalance was found to be crucial, as models without oversampling displayed satisfactory AUC (>0.80) but significantly lower sensitivity (<0.10). Key predictors identified included age, number of ED visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean HbA1c levels. Conclusions Our machine learning models successfully predict high service level utilization with strong performance, offering valuable tools for policymakers and health planners to develop targeted health initiatives based on patient utilization patterns. Key messages • Machine learning models can be leveraged to predict future healthcare utilization and facilitate interventional program development. • Annual total length of stay and total emergency department visits are useful service level indicators.

Machine Learning-Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation.

The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. We developed a collection of models that predict future health care utilization at various thresholds. We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ≥7, ≥14, and ≥30 days and emergency department attendance of ≥3, ≥5, and ≥10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives.

ВИКОРИСТАННЯ ЕЛЕКТРОННИХ СИСТЕМ ДОКУМЕНТООБІГУ ТА ЗВІТНОСТІ ЛОГІСТИЧНИМИ КОМПАНІЯМИ УКРАЇНИ

The article explores the possibilities and ways of using information technologies for document management and electronic reporting at logistics enterprises. Theoretical approaches to understanding the goals and tasks of logistics information systems in transport technologies have been considered. The main information technology systems that change the logistics industry in modern conditions have been characterized, namely: RPA; chat bots; visualization; Artificial Intelligence; predictive analytics. The electronic technologies used for information provision in Ukrainian practice, the IT-Enterprise ERP system of JSC ‘Ukrposhta’ and the CRM system of the company ‘Epicentr K’ have been analyzed. Their characteristics and advantages of use have been defined. It have been found that the basis of the functional component of the modern information system is a single module of operations between the links of the logistics system, which determines the relationship between the functional divisions of the company (in terms of the implementation of logistics functions), intermediate links and consumers of the company’s products. The implementation of a logistics information system in the work of a logistics enterprise makes it possible: - to increase the efficiency of its work, achieve a high level of service for customers and ensure effective control; - to ensure a modern approach to the management of implementation processes and the organization of various types of events; - to monitor the efficiency of commercial operations; - to conduct an analysis of the profitability of the activity; - to increase the effectiveness of price policy, etc. The problems of the introduction of electronic document flow and reporting systems in Ukraine have been noted: - the problem of interoperability in electronic document flow; - lack of clear standards and requirements for electronic reporting, companies may hesitate to switch to this type of reporting; – constant changes in legislation and requirements can create difficulties for enterprises in implementing stable electronic reporting systems; - inconsistency of electronic reporting formats with global ones; high implementation costs; - concerns about data privacy and security can deter companies from switching to electronic reporting, especially when the security of information on the network is at stake.

ГОТОВНІСТЬ МАЙБУТНІХ КЕРІВНИКІВ ЗАКЛАДІВ ВИЩОЇ ОСВІТИ ДО ЗАСТОСУВАННЯ ІННОВАЦІЙНИХ УПРАВЛІНСЬКИХ ТЕХНОЛОГІЙ

The relevance of this article is determined by the necessity to ensure the competitiveness of educational institutions in the educational services market. Modern challenges require educational institutions to continuously improve their activities. Successful development of an educational institution is possible only through the integration of innovations that stimulate the achievement of its goals and contribute to improving the quality of education for students. This aligns with the interests of Ukrainian society, which, like any other developed society, is interested in a high level of educational services. With the growth of the educational services market and increasing competition, it is important for leaders of educational institutions to focus on searching for and implementing innovative management technologies to secure significant competitive advantages. Thus, the current market conditions and the need for competitive struggle highlight the importance of innovations for leaders, with innovative management technologies becoming key to achieving the goals of the educational institution and enhancing the quality of education. Objective: To analyze the readiness of future leaders of educational institutions to implement and apply innovative management technologies to enhance the competitiveness of their organizations in the educational services market. Methods: Study of scientific sources, legislative and normative-legal documents, surveys and interviews, case studies, SWOT analysis, expert assessments, and modeling. Results: During the study, the readiness of future educational institution leaders to implement innovative management technologies was evaluated; key barriers and challenges faced by future leaders, including limited resources, fear of change, and an insufficient level of knowledge about innovative technologies, were identified. At the same time, positive aspects were also discovered, namely a high level of motivation for learning and improvement, and readiness to introduce innovations to enhance the quality of education and adapt to modern labor market requirements. A comprehensive set of measures to increase the readiness of future leaders for innovation was developed and substantiated. This includes professional development programs, mentorship, and the creation of innovative educational spaces for experimentation and practical application of new approaches. The role of strategic planning in the successful implementation of innovative management technologies was also determined, which involves clear formulation of the mission, vision of the educational institution, as well as the development of specific goals and tasks to achieve defined results. Conclusions: This article examined the readiness of future educational institution leaders to apply innovative management technologies, which is crucial for enhancing competitiveness and the quality of educational services provided. The main barriers and challenges leaders may face when implementing innovations were identified, and methods to overcome these challenges were proposed. Special attention was given to the importance of a systematic approach and strategic planning in the process of adapting and implementing new practices in management. Developed and substantiated recommendations for focusing on innovative management practices include: enhancing the qualifications of future leaders, developing openness to change, and stimulating creativity and innovative thinking. It was determined that the successful implementation of innovative management technologies depends on a comprehensive approach that takes into account the specifics of the educational institution, the readiness of the staff for changes, and the presence of the necessary material and technical base. The findings of the study make it clear that innovative management technologies play a decisive role in forming an effective management model in modern educational institutions. Their implementation not only improves the quality of educational services but also ensures the sustainable development of educational organizations, meeting the challenges of the contemporary dynamic educational environment.

Intelligent Resource Monitoring and Control Method in Vehicular Ad-hoc Networks for Electric Vehicle Enabled Microgrids

Software-Defined Networks (SDN) and Cloud Radio Access Networks (CRANs) are added to vehicle ad hoc networks (VANETs). The goals include making data transfer and resource sharing more efficient, achieving the shortest possible wait and response times, and ensuring the network is reliable even when conditions change. The usual ways of handling and grouping loads in Electric Vehicles (EVs), which are made for stable environments, need to be changed to work with VANETs, which are constantly evolving. To regularly meet these high service levels, focus on more adaptable and durable solutions. This study shows a software-defined EV fog computing design that improves VANETs' resource sharing. The suggested design uses smart controls placed strategically in the network to make the flow of data and use of resources as efficient as possible. The system uses parallel processing to split up computing tasks among EV stations. This makes the network more mobile and lessens the chance of jams. Simulations and real-world tests of the model show that it makes the network much more efficient. The study found that compared to traditional methods, the average response time went up by 29%, network delay went down by 23%, and it took 27% less time to get to the best assets spread.

Pricing model of ride-hailing platform considering rationally inattentive passengers

ABSTRACT The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately.

Technical vocational students’ higher learning institution preference and level of academic and skills preparedness

Vocational education is increasingly recognized for its role in creating gainful employment opportunities and stimulating economic growth. As such, this paper studied the higher learning institution preferences of the technical vocational students and their academic and skills readiness. Specifically, this study focused on the senior high school students enrolled in the technical and vocational livelihood track at Buruanga Vocational School and their regards on the growing importance of vocational education in job creation and national support. Utilizing a descriptive-correlational research design, this research examined the relationship between academic performance, skills preparedness, and higher learning institution preference 50 purposively selected completers as grounded on Vocational Identify Theory to understand how students perceived different career alternatives. Each student responded to self-report survey questionnaire that revealed academic preparedness and interest in various institutions. The results demonstrated a significant relationship between their readiness and preference for the institution. Additionally, the graduates indicated that their performance and literal understanding were lacking compared to their academic track. Hence, the outcomes confirmed that schools need to check their program levels and outreach and plan to offer higher-level services like the high school career guidance programs. The results provide hallmark-witted assortment preparations for strengthening undergraduates for high-level activities, thus preparing vocational schooling to attain a degree.

Designing a new sustainable healthcare network considering the COVID-19 pandemic: Artificial intelligence-based solutions

The COVID-19 pandemic, which is still spreading its new mutations all around the world, is considered a healthcare challenge around the world. The best approach to forestall this pandemic is to avoid exposure to the virus. Therefore, medical protective equipment is essential for fighting this pandemic. This underlines the chief role of having a sustainable supply chain network (SCN) for producing and distributing personal protective equipment to avoid shortage and augmenting costs. The reality, however, is that the COVID-19 pandemic leads to many developments in countries and this study is another step for these purposes. This research developsa multi-period, multi-objective, multi-echelon, and multi-product medical protective equipment sustainable SCN considering the production, distribution, allocation, and inventory with “risk pooling” strategy effect with the aim of filling the existing gaps in health SCN research during the COVID-19 pandemic. By applying the risk pooling strategy, lower inventory levels or higher service levels can be achieved without increasing inventory costs. This model explores the possibility of lateral transshipments between distribution centers, as a way to increase the reliability of SCN performance. We model a new production, inventory, distribution, location, and allocation problem and consider four objectives for our suggested model (i) minimizing total SCN costs, (ii) minimizing environmental effects, (iii) minimizing social impacts, and (iv) maximizing the reliability of demand delivery. The proposedmodel simultaneously examines all three dimensions of sustainability (economic, environmental, and social) as well as the reliability of demand delivery. Considering all of these decisions and assumptions brings the studied problem closer to reality. We employ various algorithms for solving our developed model with different sizes: the improved version of the augmented ε-constraint (AUGMECON2) algorithm for small and medium-sized problems and two meta-heuristic algorithms, i.e., Multi-Objective Whale Optimization Algorithm (MOWOA) and Multi-Objective Variable Neighborhood Search (MOVNS) algorithm, for large-sized ones. The Taguchi approach is used to tune the parameters of meta-heuristic algorithms, and a comparison is performed using four evaluation metrics: Mean Ideal Distance (MID), Number of Pareto Solutions (NPS), Maximum Spread (MS), and Spread of Non-Dominance Solution (SNS). Proposed solving methods for the studied problem and making comparisons between them are another innovation of this study. A couple of numerical examples are provided to illustrate the applicability of the presented solution methods. Finally, sensitivity analysis for problem parameters is performed to validate our suggested model. Our study reveals the superiority of the MOWOA over the other algorithms.

Why are the railways of Eastern Europe less efficient than those of the West?

We use a DEA analysis to compare the efficiency of the railways of Western and Eastern Europe and find while most railways of Western Europe are on the efficiency frontier, those of Eastern Europe are typically a long way from it. One explanation may be that the reform process only started much later in Eastern Europe than in the West, although it appears that Eastern Europe has largely caught up. Secondly, Eastern Europe suffered a significant loss of traffic after the end of the communist regime, and this may still be resulting in an excess of labour and assets. There is some evidence that this remains the case for labour and freight vehicles. Although the excess of freight vehicles may be largely vehicles out of service, they still contribute to the poor efficiency scores for Eastern Europe. It is also the case that the countries of Eastern Europe suffer some disadvantages in terms of population density. However, we believe that two aspects of policy play an important role in the poorer performance of Eastern European countries compared with Western. Firstly, is the poorer infrastructure quality associated with lower levels of investment. This shows up as less use of electric traction and slower train speeds, resulting in lower productivity of staff and assets. Despite the efforts of the European Commission to overcome this problem, there is still a long way to go. Secondly, is the strong use of public service obligations to maintain high levels of service with relatively low load factors. If it is desired to raise the efficiency of Eastern European railways, governments in Eastern Europe will need to consider whether they are specifying excessively high levels of service.