Logistics Demand Research Articles

SARS-CoV-2 Spreads Globally Through the Object-to-Human Transmission of Cross-Border Logistics.

With globalization, the demand for transnational logistics is growing rapidly. However, the object-to-human transmission of SARS-CoV-2 has been reported in transnational logistics production, transportation, storage, sales, and consumption. Every link of transnational logistics has the risk of spreading the COVID-19 pandemic. It is concluded that low temperatures, dry environments, and smooth surfaces are conducive to the long-term survival of SARS-CoV-2 on the surface of transnational goods. Epidemiological investigation and big data analysis show that the object-to-human transmission route of direct contact with contaminated cold chain goods plays a key role in the outbreak and transmission of the COVID-19 pandemic. This may be the most crucial reason for the global spread of SARS-CoV-2 caused by transnational logistics. It is an effective way to prevent the spread of SARS-CoV-2 from object-to-human through transnational logistics by strengthening the management of employees in all aspects of transnational logistics, carrying out comprehensive disinfection and quarantine of and guiding consumers to handle transnational goods properly.

A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period

During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.

Implementing a telehealth-delivered psychoeducational support group for care partners of individuals with primary progressive aphasia

ABSTRACT Background Primary progressive aphasia (PPA) is a language-prominent dementia that fundamentally impacts the lives of not only the person with the diagnosis, but also their family members. While assuming a caregiving role, care partners are vulnerable to negative health and psychosocial consequences of their own. Support groups are one way to meet the needs of care partners, providing opportunities for individuals with common experiences to socialize, obtain knowledge about disorders, and learn coping strategies. Given that PPA is rare and that in-person support groups are sparse in the United States, there is a need for alternative meeting modalities, to overcome the limitations imposed by relative scarcity of potential participants, lack of appropriately-trained clinical professionals, and the logistical demands faced by overburdened care providers. Telehealth-based support groups provide care partners with opportunities to connect virtually with other care partners; however, research regarding their feasibility and benefit is limited. Aims This pilot study investigated whether a telehealth-based support group for care partners of persons with PPA is feasible and yields benefits in psychosocial functioning. Methods & Procedures Ten care partners of persons with PPA (7 females; 3 males) participated in a group intervention comprising psychoeducation about relevant topics, followed by group discussion. Meetings were held twice monthly for four months via teleconference. All participants completed pre- and post-intervention measures to examine support group satisfaction as well as psychosocial functioning, including quality of life, coping, mood, and caregiving perception. Outcomes & Results Consistent group member participation across study phases supports the feasibility of this intervention model. Quantitative results from paired-samples permutation tests indicate no significant changes from pre- to post-intervention on psychometrically-validated psychosocial measures. Qualitatively, results from an in-house Likert-type survey indicate positive outcomes in quality of life, social support, caregiving skills, and psychoeducation. Relatedly, post-intervention themes derived from a thematic analysis of written survey responses included: Enhancing knowledge about PPA, feeling connected and mutually supported, and desire for more time together. Conclusions Consistent with existing literature evaluating virtually-delivered care partner support groups in dementia and other acquired medical conditions, findings from this study support the feasibility and benefit of telehealth-based support groups for care partners of persons with PPA.

Offline approximate value iteration for dynamic solutions to the multivehicle routing problem with stochastic demand

The multivehicle routing problem with stochastic demand (MVRPSD) is an important issue both in theory and practice. However, solving the MVRPSD through traditional methods, such as a priori optimization or rollout-algorithm-based dynamic programming is generally limited by the issues of computation efficiency and solution quality. Under increasing demand for efficient real-time logistics, we propose a novel offline approximate value iteration (OAVI) algorithm for dynamic solutions to the MVRPSD. Our algorithm benefits from offline training and thus can provide fast and effective online dynamic routing solutions. Adopting such a novel and effective algorithm presents two challenges: first, we must define a proper cost structure for the dynamic routing decision; second, we must efficiently address the curse of dimensionality of the multivehicle problem. To solve these problems, we first describe the cost structure through the value function approximation (VFA) with basis functions involving a priori cost and a priori credibility; we then design two strategies, recourse reduction (RR) and neighborhood reduction (NR), to prune the action space. The numerical experiments show that our algorithm can substantially enhance computation efficiency and solution quality compared to traditional methods.

Supply chain operation evaluation and management decision by fuzzy cognitive map model

AbstractThe components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.

Should have been an email? Meeting redesign for applied educators

Due to the logistic and interpersonal demands of applied teaching, applied educators often simultaneously have more to meet about and less time available for meetings. This can contribute to time poverty and overwhelm in a cycle that erodes productivity over time. While meetings are often necessary, they are frequently inefficient, ineffective, and demoralizing. This paper reviews relevant studies on effective meeting redesign, recommends specific strategies for applied academics, and describes an example of implementation in an applied learning setting.

Innovations in freight transport: a systematic literature evaluation and COVID implications

PurposeThis paper systematically evaluates the existing literature of innovations in freight transport, including all modes, to uncover the key research themes and methodologies employed by researchers to study innovations and their implications in this industry. It analyses the role of transport and the impact of innovations during crises, such as COVID-19.Design/methodology/approachQualitative and quantitative analysis of the innovations in freight transport unravels the pre-requisites of such endeavours in achieving a resilient and sustainable transport network that effectively and efficiently operates during a crisis. The authors performed keyword co-occurrence network (KCON) analysis and research focus parallelship network (RFPN) analysis using BibExcel and Gephi to determine the major resulting research streams in freight transport.FindingsThe RFPN identified five emerging themes: transport operations, technological innovation, transport economics, transport policy and resilience and disaster management. Optimisation and simulation techniques, and more recently, artificial intelligence and machine learning (ML) approaches, have been used to model and solve freight transport problems. Automation innovations have also penetrated freight and supply chains. Information and communication technology (ICT)-based innovations have also been found to be effective in building resilient supply chains.Research limitations/implicationsGiven the growth of e-commerce during COVID-19 and the resulting logistics demand, along with the need for transporting food and medical emergency products, the role of automation, optimisation, monitoring systems and risk management in the transport industry has become more salient. Transport companies need to improve their operational efficiency using innovative technologies and data science for informed decision-making.Originality/valueThis paper advises researchers and practitioners involved in freight transport and innovation about main directions and gaps in the field through an integrated approach for evaluating research undertaken in the area. This paper also highlights the role of crisis, e.g. COVID-19, and its impacts on freight transport. Major contributions of this paper are as follows: (1) a qualitative and quantitative, systematic and effective assessment of the literature on freight transport through a network analysis of keywords supplemented by a review of the text of 148 papers; (2) unravelling major research areas; (3) identifying innovations in freight transport and their classification as technological and non-technological and (4) investigating the impact of crises and disruptions in freight transport.

Estimating consumer preferences for different beverages using the BLP approach

The overconsumption of sugar is a significant problem in many jurisdictions, and one possible method to remedy this problem is the taxation of sugar-sweetened beverages (SSBs). To be able to implement an optimal tax, it is important to know the preferences and price sensitivity of consumers. This article therefore estimates the price elasticity of demand for different beverages in Quebec, using the Berry, Levinsohn and Pakes (BLP) random parameter logistic demand model, combined with Nielsen data from 2010 to 2016 and the 2016 Canadian Census. The results suggest that the average consumer prefers high-calorie beverages containing fruits and vegetables, and the estimated price elasticities are between −4.40 (energy drinks) and −1.59 (regular soft drinks). As a result, consumers of energy drinks appear to reduce their consumption the most in the face of rising prices, whereas consumers of soft drinks will decrease their consumption the least. However, at a general level, the implementation of a tax on SSBs in Quebec should generate a significant reduction in consumption.

Exploring location factors of logistics facilities from a spatiotemporal perspective: A case study from Shanghai

In the era of economic globalization and the e-commerce boom, the demand for urban logistics has been rapidly growing in terms of both scale and intensity. The spatial clustering of logistics facilities and their associated environmental consequences has caused growing concern. This study analyzes the spatial evolution of logistics facilities and establishes a series of regression models to explore the driving factors of logistics facility location choice using a high-resolution grid-based data set. The results show that as the urban highway network gets improved, transportation costs are more balanced across space. Therefore, the distribution of logistics facilities is less regionally constrained and more widely located in space. The spatial evolution of logistics facilities was driven not only by demand of land uses and industries but also by transport supply and urban structure. Land use planning including the planning of industrial parks is among the important driving forces affecting the spatial location of logistics facilities, although the effects vary across types of logistics facilities. This study aims to provide a sustainable development-oriented policy path for regional development by proposing an analytical framework that reconsiders spatial location choice factors as a means of controlling logistics environmental negative externalities.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Intelligent Measurement and Monitoring of Carbon Emissions for 5G Shared Smart Logistics

With the upgrading of logistics demand and the innovation of modern information technology, the smart logistics platform integrates advanced concepts, technologies, and management methods, maximizes the integration of logistics resources and circulation channels, and effectively improves the efficiency of logistics transactions, but its energy consumption problem is particularly prominent. The study of intelligent measurement and monitoring of carbon emissions in smart logistics is of great value to reduce energy consumption, reduce carbon emissions in buildings, and improve the environment. In this paper, by comparing and analyzing the accounting standards of carbon emissions and their calculation methods, the carbon emission factor method is selected as the method to study the carbon emissions of the smart logistics process in this paper. The working principle of each key storage technology in the smart logistics process is analyzed to find out the equipment factors affecting the carbon emission of each storage technology in the smart logistics process, and the carbon emission calculation model of each key storage technology is established separately by using the carbon emission factor method. Meanwhile, according to the development history of energy consumption assessment, the assessment process of different stages from logistics storage energy consumption assessment to smart logistics energy consumption assessment is analyzed, and based on this, a carbon emission energy consumption assessment framework based on 5G shared smart logistics is constructed. This paper applies the supply chain idea to define the smart logistics supply chain, constructs a conceptual model of the smart logistics supply chain considering carbon emissions, and at the same time combines the characteristics of the smart logistics supply chain to analyze the correlation between the carbon emissions of the smart logistics supply chain and the related social, environmental, and economic systems.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Opportunities of on-demand logistics operations

As a result of information technology and digitalization, it is possible to get to know the previous customer demands and more details. This allows companies to operate flexible and adaptive, more efficient logistics processes using the known demands. This is the basis of on-demand activities. However, in addition to understanding and using the tools, it is important to know the timeliness of the processes, because only in this way can the necessary tools be determined and applied effectively. In our article first we will examine the typical logistical demand processing processes. Based on these studies, we define a general demand fulfillment case study, which includes the characteristics of typical processes and formulates the risks associated with changes in demands and helps to develop adaptivity. The circumstances related to logistics are constantly changing due to the impact of digitalization. Practical solutions that have been thought to be rock-solid need to be reinterpreted in such a way that they can be implemented for the technical solutions of the future. Current production planning solutions operate on-demand planning. This includes (old) stocking concepts that want to ensure the number of raw materials needed for this type of design, but in many cases, this can result redundant and unnecessary high stock. The problem is the constantly changing on-demand supply of production, which requires a high level of IT support. On the other hand, only a very limited percentage of companies have systems or processes for providing highly automated care. As a consequence, production planning is forced to adapt its production programmed to the available stocks and processes. Thus, the role of information and the extent to which related analytics solutions are applied are appreciated in these processes, as they are the foundations of the entire organizational performance. In our research, therefore, we focus on the processes by which we can help bridge the gaps between current production planning and supply. The goal of this paper to highlight the necessity of the on-demand operation optimization and the possible on-demand solutions.

Analysis of Logistics Industry Demand Forecast in China

The outbreak of covid2019 coronavirus disease has a certain impact on the development of logistics industry. This paper aims to find out the typical factors affecting the logistics demand before and after covid2019, and predict the logistics demand in the next decade. This paper selects the total amount of social logistics from 2010 to 2020 as the index to measure the logistics demand, analyzes the factors affecting the total amount of social logistics, and establishes a logistics demand prediction model based on multiple linear regression. The results show that the demand of social logistics is most closely related to the total cost of social logistics and the investment in fixed assets of the whole society, but weakly related to the total import and export. At the same time, this paper also forecasts the logistics demand in the next decade to provide decision support for relevant departments.

Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.

Retracted] Research on the Prediction of Port Economic Synergy Development Trend Based on Deep Neural Networks

After entering the new century, along with the further deepening of global economic and trade cooperation, the industrial division of labor has been newly developed globally, which brings the cooperation among countries in the international logistics chain more and more closely. As the core node linking domestic and foreign water transportation, ports play a very key role in the international logistics chain and have an extremely central position in the national logistics planning. The coordinated development of port economy is an important part of the economic development planning of port cities, and it is also the premise and basis for the comprehensive planning of port logistics infrastructure construction scale, logistics space layout, and port city logistics development direction and function positioning. Therefore, according to the availability of realistic data, this paper establishes a deep neural network prediction model for the collaborative development of ports and uses various port logistics indicators to predict the economic development trend, so as to realize a nonlinear mapping relationship between the level of port economic development and the side of port logistics demand. Meanwhile, the research of this paper will provide theoretical basis and corresponding practical tools for the coordinated development between regional economy and port logistics industry.

Theory versus practice in the duplicate selections from the British New Guinea Official collection

Reconstructing the concepts and methods used to select objects from the British New Guinea Official collection at the Queensland Museum for exchange with other museums exposes intriguing differences between theory and practice that shed light on how ethnographic material was valued at this specific time and place. The large scale of the Official collection coupled with a lack of storage space had a major effect on how Charles de Vis, curator at the Queensland Museum, identified supposedly ‘duplicate’ material for distribution to the British Museum, the Queensland Museum and two additional museums in the Australian colonies. Although his relationships with curators at two of the museums probably had a minor influence on his selections, the bulk of the objects was chosen from categories with the largest number of specimens. Multiple copies of undecorated forms that de Vis had registered sequentially were also targeted, especially in cases where the original uses by the source communities were obscure to the curator. Although the concept of ‘uniqueness,’ probably influenced by concepts derived from biological taxonomy, guided how de Vis made his choices, the logistical demands of curating such a large collection had the most impact on the duplicate selection.

Demand forecast and influential factors of cold chain logistics based on a grey model.

Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021-2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.

Novel Grey Forecasting Model with Bi-Level Structure for Application to Logistic Demand

Novel Grey Forecasting Model with Bi-Level Structure for Application to Logistic Demand

Ten Years of Universal Testing: How the Rapid Diagnostic Test Became a Game Changer for Malaria Case Management and Improved Disease Reporting.

ABSTRACT.In 2010, the World Health Organization changed its guidance on malaria case management, recommending parasitological confirmation of all suspected cases before treatment with an antimalarial. This recommendation was in large part as a result of the availability of quality assured malaria rapid diagnostic tests (RDTs) that made it possible for malaria diagnosis to be performed by laboratory staff in all health facilities irrespective of the facility’s place in the tiered health system. Community health workers and other non-laboratory health workers who traditionally did not perform malaria testing due to the technical and logistic demands of smear microscopy were now able to test for malaria. The use of RDTs has led to substantial increases in testing rates, improved quality of case management, as well as more accurate reporting of malaria cases. Although current RDTs have limitations, they remain one of the most important tools in contemporary malaria control. Further improvements to existing products, such as increased sensitivity for non-falciparum tests, diversification of Plasmodium falciparum antigen targets, along with strengthened health system support for current RDTs will further enhance their utility in malaria control and prevention.