ABSTRACT
Pandemics and sudden disease outbreaks place considerable stress on hospital resources. Their increasing numbers in recent years has necessitated investment in disaster risk management strategies, particularly in the healthcare sector. The sudden surge of patients, particularly in requesting ambulance services, overwhelms hospital systems and compromises health service delivery. Failure of health planners to respond immediately to a sudden disease outbreak can result in insufficient distribution of healthcare services and can thereby exacerbate the death toll dramatically. The current research aims to develop an optimisation-based integrated decision model to assist healthcare decision-makers with immediate and effective planning for ambulances to move critical patients from their residences to hospitals, considering the available capacities of each hospital. Several lemmas for the problem are proposed, and based on these;several local search methods are developed to improve the performance of the proposed optimisation method. To confirm the efficacy of the proposed approach, a comprehensive comparison is conducted. In conclusion, sensitivity analyses are performed to discuss some practical insights. The proposed models can be adopted to develop decision tools that enable hospital system managers to optimize their resources to changing healthcare needs in disease outbreaks.
ABSTRACT
A growing number of investors in recent years has focused on environmental, social and governance (ESG) factors in carrying out investment activities and the COVID-19 pandemic has only driven such trends of ESG investing at an accelerated rate. Many studies have examined the relationship between ESG scores and corporate financial performance, along with the effectiveness of ESG portfolios. This paper discusses various approaches to incorporate ESG factors into a portfolio optimisation and critically compares and contrasts the efficacy of these approaches on the Dow Jones Industrial Average constituents. It finds that thematic investing appears to be the best performer. In addition, it is also found that there is no evidence that ESG portfolios underperform the market. © Henry Stewart Publications 1752-8887 (2023).
ABSTRACT
Because of the COVID-19 pandemic and the war in Ukraine, agricultural commodities had significant price increases, which inevitably implies high risk. In this article, we try to mitigate the extreme risk of corn and soybeans by constructing multivariate portfolios with developed and emerging European stock indices. We measured extreme risk via conditional value at risk. To address different goals that investors might prefer, we produced portfolios with the lowest risk and highest return-to-risk ratio. According to the results, corn and soybeans had relatively high portfolio shares. However, they are the riskiest assets because they have a very low pairwise correlation with the stock indices. Portfolios with emerging European indices had better risk-reducing results, considering both agricultural commodities because these indices are less risky than developed indices. In particular, the risk reductions of corn were 38% and 50% in the portfolios with developed and emerging stock indices, respectively, whereas, for soybeans, the results were 28% and 41%, respectively. In optimal portfolios, emerging European stock indices had the upper hand in most cases. © The authors.
ABSTRACT
Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.
ABSTRACT
In the 21st century, global supply chains have experienced severe risks due to disruptions caused by crises and serious diseases, such as the great tsunami, SARS, and, more recently, COVID-19. Building a resilient supply chain is necessary for business survival and growth. Similarly, there is increasing regulatory and social pressure for managers to continuously design and implement sustainable supply chain networks, encompassing economic, social, and environmental components. Hence, a panacea approach is required to establish a compromise position between resiliency concerns and sustainability responsibilities. To address this, this work presents a hybrid integrated BWM-CoCoSo-multi-objective programming model (BC-MOPM) formulated to deliver a compromise between resilience and sustainability supply chain network design (RS-SCND). First, a thorough literature review analysis is conducted to explore the relationship and correlation between resilience and sustainability to develop a framework for the resiliency and sustainability criteria, in a supply chain context. Second, four objectives were formulated, including the minimisation of total cost and environmental impact and the maximisation of social and resilience paradigms. A real two-tier supply chain network is deployed to evaluate the applicability of the developed BC-MOPM. Furthermore, sensitivity analysis is conducted to establish the relative importance of the identified criteria to prove the model's robustness. Results demonstrate the capability of the BC-MOPM in revealing trade-offs between the resiliency and sustainability aspects. © 2023 Elsevier B.V.
ABSTRACT
Along with the increase in population, the increase in demand for freight in e-commerce has complicated the transportation of goods with increasing transportation costs. The demand for e-commerce has also increased rapidly, especially during the current outbreak of the COVID-19 epidemic. Along with this need, the paper presents the current situation of freight transportation in the last mile of the e-commerce industry and new trends to improve the conditions in the last-mile delivery. Through published studies, several applications in last-mile delivery have also been identified for comparison with other costing methods. Studies also show that the application of new technologies in last-mile delivery can optimise costs, the environment, as well as delivery policies for companies. The paper also points out that the difficulty in applying and managing new technologies is very complicated and requires policies from managers. This can also be considered a new research direction that contributes to promoting new technologies in last-mile delivery to reduce time and costs for delivery companies as well as customer costs for last-mile delivery. Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
The COVID-19 pandemic has caused global disruption that has significantly affected various supply chain systems worldwide. Several garments manufacturers in Indonesia have struggled to get material supplies because of a sudden stop in suppliers' operations. Besides COVID-19, various disruptions, either small or large, can occasionally occur within companies' supply chain systems. Several strategies have been applied to create resilient and robust supply chain systems to both anticipate and recover from the threat of disruption. Three primary strategies are considered in this research: the sourcing strategy, resilience strategy, and supply base strategy. An analysis with quantifiable parameters was conducted to evaluate the application of the strategies within the case study company's supply chain network. An investigation was carried out by developing a two-stage stochastic programming model that includes 128 disruption scenarios. This model also includes two types of disruption: Low Impact High Frequency (LIHF) and High Impact Low Frequency (HILF). Based on the results of the analysis, the strategies are effective in creating a resilient supply chain system. Compared with the past condition, the implementation of the strategies as mentioned earlier leads to a lower total cost and maximum service level. © 2023 Author(s).
ABSTRACT
While supply chain disruptions are a drastic example of changes businesses must react to, being flexible and reactive will prove beneficial for companies. While being able to react to such disruptions is important, it is even more vital for supply chains to be proactive in a Covid-19 reality. Companies need to be adapting to changes in how customer relationships are maintained within digital supply chain (DSC) platforms. With more and more people working remotely, companies must still ensure their customer relationships are being maintained. This is one example of a supply chain challenge that this paper looks to unfold. By highlight the importance of digital platforms in e-procurement, increased technology and transparency are vital to successful companies, either B2C, B2B. Companies who are willing to learn and adapt become more competitive. The paper briefly dives into SAP Ariba and Jaggaer, and their role in aiding e-procurement and CRM. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective. © 2023 Centro Regional de Invest. Cientif. y Tecn.. All rights reserved.
ABSTRACT
In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The emergency department (ED) is the most important section in every hospital. The ED behaviour is adequately complex, because the ED has several uncertain parameters such as the waiting time of patients or arrival time of patients. To deal with ED complexities, this paper presents a simulation-based optimisation-based meta-model (S-BO-BM-M) to minimise total waiting time of the arriving patients in an emergency department under COVID-19 conditions. A full-factorial design used meta-modelling approach to identify scenarios of systems to estimate an integer nonlinear programming model for the patient waiting time minimisation under COVID-19 conditions. Findings showed that the S-BO-BM-M obtains the new key resources configuration. Simulation-based optimisation meta-modelling approach in this paper is an invaluable contribution to the ED and medical managers for the redesign and evaluates of current situation ED system to reduce waiting time of patients and improve resource distribution in the ED under COVID-19 conditions to improve efficiency. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty. © 2022 The Authors
ABSTRACT
To mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, uncertainty in infection probability, and service level. The circular economy aspect is measured by the reduction of infection risks and costs, as well as limiting exhaust emissions. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross-regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.
ABSTRACT
The use of digital manufacturing for the construction of orthosis and prostheses has become common since the popularization of 3D printers and the advent of Industry 4.0. Furthermore, due to the fact that the manufacture of orthosis is interactive and for personal use, generic production is difficult. In this sense, the large-scale production of these products lacks of improvements, standardization of processes and production optimization. An aggravation of this is the recent social distance due to the COVID-19 pandemic, which makes the use of temporary orthosis made in 3D printers to have a recent growth. Parallel to this, the use of multi-lattice inner structures for internal structuring of prints has also been increasing and taking on a more consolidated form. This article aims to present the multi-lattice optimization as a solution to this problem, in order to reduce material waste while maintaining the mechanical behavior of printed parts. © 2022, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.
ABSTRACT
In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries.
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Against the background of the current COVID-19 pandemic and the popularisation of the Internet, the demand for online teaching is increasing in colleges and universities. But a course in music like piano teaching that requires multi-sensory learning still presents great challenges. Given this background, this paper analyses the system functional requirements of online piano teaching in colleges and universities, designs the overall system architecture including software and hardware, selects the wireless network communication method after analysis and judgement, and designs a remote wireless network-based system. Finally, the advantages and disadvantages of different protocol algorithms are compared.
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This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO's recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people's preferences. Second, elderly people can be made aware of appropriate health beverages. Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
Nature-inspired algorithms are a relatively recent field of meta-heuristics introduced to optimise the process of clustering unlabelled data. In recent years, hybridisation of these algorithms has been pursued to combine the best of multiple algorithms for more efficient clustering and overcoming their drawbacks. In this paper, we discuss a novel hybridisation concept where we combine the exploration and exploitation processes of the vanilla bat and vanilla whale algorithm to develop a hybrid meta-heuristic algorithm. We test this algorithm against the existing vanilla meta-heuristic algorithms, including the vanilla bat and whale algorithm. These tests are performed on several single objective CEC functions to compare convergence speed to the minima coordinates. Additional tests are performed on several real-life and artificial clustering datasets to compare convergence speeds and clustering quality. Finally, we test the hybrid on real-world cases with unlabelled clustering data, namely a credit card fraud detection dataset, and a COVID-19 diagnosis dataset, and end with a discussion on the significance of the work, its limitations and future scope. © 2023 Inderscience Enterprises Ltd.
ABSTRACT
A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.