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Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

Wearing clothes requires specifications for feeling comfortable, derived from the fibres, fabrics and finishing properties. This study aims to deal with the effect of economic blends containing hollow fibres, bamboo and cotton/polyester waste on the mechanical properties of the produced fabrics and the appropriate end use.

This research included two blends: one consisted of cotton/polyester wastes blended with bamboo and the other to which Chorisia fibres were added. Two weft counts 10,6/1 Ne were made from each blend, which were used to produce four fabric samples (S1 Chorisia-free and S2 with Chorisia); additionally, another two samples were dyed that contain Chorisia (S3) from each count. The six samples were tested by Kawabata Evaluation System (KES).

The samples gave a good total hand value (THV) for use as men's winter suits, where the thicker count 6/1, with and without Chorisia had better properties, also both counts 6, 10/1 with dye. The hollow fibres affected the fabrics’ properties, including thickness, shear, bending, thermal conductivity and weight. Both blends had a positive effect on THV.

Cotton/polyester waste, Chorisia and bamboo fibres were tested, and 2% Remazol Yellow GNL dye was used.

The ratio of blending, weft counts and dye affected the fabric’s properties, with consequences for the use of the Kawabata system and its applications.

The fabrics used in this research may be considered to be economical and have good THV.

The study proved the usefulness of fabrics made of two blends. The Chorisia component may be seen as a good alternative to cotton fibres to reduce the cost of producing high-consumption winter suit fabrics.

Denim fabric has become a wardrobe staple due to its versatility to be worn in a variety of fashions. This paper aims to study denim fabrics to understand their unique hand by developing a hand evaluation system using computational method. Also, the effect of various washes was studied on the hand and surface morphology of denim fabrics.

Five different denim samples were manufactured with various washing treatments. The Kawabata Evaluation System was used to measure the low stress mechanical properties. Computation method was used to develop hand equations using multiple regression technique in the MS Excel software. The correlation coefficient analysis was done to determine the authenticity of the developed equations. Five primary hand attributes such as softness, smoothness, fullness, flexibility and stretchability were shortlisted by a panel of judges that influence the fabric handle.

The correlation coefficient between subjective and computational total hand values with thermal properties and without thermal properties was 0.88 and 0.85, respectively. The enzymatic wash fabric has the highest total hand value followed by the acid, bleach and stone-washed fabrics.

Although the hand evaluation system is available for conventional textiles like suiting and shirting fabrics, the method to predict fabric hand of non-conventional textiles such as denim fabrics remains an unexplored topic. The stresses acting on denim fabrics are completely different. Therefore, to the best of the author’s knowledge, a novel attempt has been made in this research work to develop a computational model to predict the total hand value of denim fabrics.

The efforts of researchers in the 21st century have been devoted to developing novel approaches to leave planet earth green for future generations. This study aims to report the synthesis of microcapsules from natural essential peppermint oil and their application to a bleached polyester and cotton (PC) blended fabric.

Microcapsules were prepared by a complex coacervation process and applied through the conventional pad-dry-cure method. The liquid suspension of the microcapsules was examined by optical microscopy to investigate the surface morphology of the microcapsules. Scanning electron microscopy was used to examine the surface morphology of the fabric after the application of the microcapsules. The finished fabric was checked for its mosquito repellent activity at the lab scale using a standard test protocol (cage test) by inserting a human arm and hand enfolded with microcapsules treated fabric.

PC fabric treated with 6% microencapsulated peppermint oil at zero wash showed 95.3% repellency against mosquitoes, and after 30 washes, the repellency was 85.8% which confirmed the durability of the developed finished fabric. The finished samples exhibited excellent air permeabilities and absorbencies.

This study successfully developed peppermint oil microencapsulated fabric with excellent efficacy against three mosquito species.

Natural dyeing has existed in human life from past to present. Although it lost its importance after the industrial revolution, it has started to make again a name for itself today with the increase in environmental awareness. The purpose of this study is to investigate whether the Hibiscus sabdariffa L. can be used in the coloring of woolen fabrics and as a natural antibacterial agent for these fabrics.

Within the scope of the study, it was investigated whether the antibacterial activity of the Hibiscus sabdariffa L. can be transferred to woolen fabrics. In the study, woolen fabric samples were dyed with and without mordant with the help of Hibiscus sabdariffa L. extract. After the dyed fabric samples were washed and dried, their color values were measured, and antibacterial effect tests (against Staphylococcus aureus – Escherichia coli), washing and rubbing fastness tests were carried out. In addition to these, scanning electron microscopy images of dyed fabric samples were taken and Fourier transform infrared microspectroscopy analyzes were also performed.

As a result of the study, it has been determined that the antibacterial activity of the Hibiscus sabdariffa L. can be transferred to woolen fabrics without the use of any chemicals.

Natural antibacterial agent for woolen fabrics was obtained within the scope of the study without the use of chemicals.

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country?

Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis.

The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021.

The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism.

This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes.

Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

本文旨在探寻疫情影响下中国大陆城市游客规模演化规律, 具体而言, 疫情影响下, 全国及七大地理分区的国内游客量规模变化如何？七大地理地区的变化有何差异？以及疫情影响下, 全国国内游客空间规模的热点区域和空间集聚程度有何变化？

利用2018-2021年中国大陆337各城市的国内游客量数据, 借助极差、标准差、变异系数、赫芬达尔指等指标分析全国及七大地理分区国内游客规模的绝对差异和相对差异; 借助核密度分析、空间自相关分析等ArcGIS分析工具, 探寻疫情影响下全国国内游客空间规模的热点区域和空间集聚程度的变化情况。

①绝对差异方面, 七大地理分区的绝对差异均持续增大。西南地区的游客量的绝对差异巨大, 国内游发展极不均衡。西北地区、东北地区绝对差异相对较小, 在吸引国内游客方面发展较为均衡。②相对差异方面, 西南地区的国内游发展相对差异较大, 发展不均衡趋势明显; 东北地区、西北地区、华东地区的国内游发展相对差异较小, 发展较为均衡。③热点区域变化方面, 京津冀地区、长三角地区的国内旅游热度持续下降, 在2021年有所回升; 内陆西南地区在2021年成为新的国内游热点区域。④2018年至2021年城市国内游客量规模均呈现出显著的空间正相关的关系, 存在着空间集聚现象, 但部分区域集聚类型在2018到2021年间发生变化。

①理论意义：明晰了疫情对中国国内旅游人次的数量规模和空间规模的影响, 弥补了当前疫情前后国内旅游业变化对比的研究; 阐明了疫情前后中国城市国内游客空间格局的变化, 拓展了研究情景, 丰富了中国旅游业时空变化的相关研究。②实践意义：明晰了疫后不同地区国内旅游人次的数量规模和空间规模变化情况, 以及国内旅游市场热点变化和游客空间偏好变化, 有利于各地区城市对症下药, 制定符合各省份实际情况的国内旅游业振兴策略, 促进中国旅游业内循环。

Bacterial cellulose (BC) is an ideal alternative filtering material. However, current functionalization approaches for BC have not been fully discovered industrially as well as academically applying textile processing. This study aims to create a sustainable fabric-like membrane made of BC/activated carbon (AC) for applications in filtration using textile padding method, to protect people from respiratory pandemics.

Fabric-like BC is first mechanically dehydrated then AC is loaded via a textile padding step. The finishing efficacy, properties of fabric-like BC/AC and NaOH pretreatment are analyzed and characterized by scanning electron microscope (SEM), field emission scanning electron microscope (FE SEM), X-ray diffraction (XRD), CIELab color space, color strength (K/S), nitrogen adsorption-desorption isotherm including Brunauer–Emmett–Teller (BET) specific surface area and Barrett–Joyner–Halenda (BJH) pore size and volume.

This research results in a fabric-like BC/AC with pore diameters of 3.407 ± 0.310 nm, specific surface area of 115.28 m²/g and an efficient scalable padding process, which uses 8 times less amount of chemical and nearly 30 times shorter treating duration than conventional methods.

Our globe is now consuming an alarming amount of non-degradable disposable masks resulting in massive trash buildup as a future environmental problem. Besides, current disposable masks requiring a significant upfront technological investment have posed challenges in human protection from respiratory diseases, especially for countries with limited conditions. By combining a sustainable material (BC) with popular padding method of textile industry, the fabric-like BC/AC will offer sustainable and practical values for both humankind and nature.

This research has offered an effective padding process to functionalize BC, and a unique fabric-like BC/AC membrane for filtration applications.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.