Exploring customer satisfaction in cold chain logistics using a text mining approach

PurposeWith the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.Design/methodology/approachThis research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.FindingsThe results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.Research limitations/implicationsThe data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.Originality/valuePrior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.<br/

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Investigating the effect of carbon tax and carbon quota policy to achieve low carbon logistics operations

Developing a low-carbon economy and reducing carbon dioxide emission have become a consensus for both academics and practitioners. However, the existing literature did not pay enough attention in interrogating the impacts of Carbon Tax (CT) and Carbon Quota (CQ) policy on distribution costs and carbon dioxide emission in the field of vehicle routing problem. Moreover, the investigated subsidies factor is also incomplete. This research stands on the position of the company to study the impact of CT and CQ policy on aforementioned two aspects. A mathematical model is developed to achieve the best low carbon vehicle routing under the optimal policy. The optimization goal of this research is to minimize the total cost that includes vehicle-using, transportation, CT, CQ, and raw material subsidy costs. An improved optimization algorithm, namely Genetic Algorithm-Tabu Search (GA-TS), is proposed to solve a given business case. In the simulation experiments, GA-TS and a traditional GA are compared and the results show the advantage of GA-TS on reducing the total cost and carbon dioxide emission. Furthermore, the experiments also explore the total cost and carbon dioxide emission under three scenarios (Benchmark, CT and CQ), incorporating four policies: CT, Carbon Tax Subsidy (CTS), CQ, and Carbon Quota Subsidy (CQS). It is concluded that CQS is the ideal policy to minimize distribution cost and carbon dioxide emission. In addition, the impact of vehicles’ capacities on the total cost and carbon dioxide emission is also analyzed in this research. This research also aimed at assisting practitioners in better formulating delivery routes, as well as policy makers in developing carbon policies. Finally, the limitations and the future research directions of this research are also discussed

Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization

In this paper, we propose particle swarm optimization (PSO)-enhanced ensemble deep neural networks and hybrid clustering models for skin lesion segmentation. A PSO variant is proposed, which embeds diverse search actions including simulated annealing, levy flight, helix behavior, modified PSO, and differential evolution operations with spiral search coefficients. These search actions work in a cascade manner to not only equip each individual with different search operations throughout the search process but also assign distinctive search actions to different particles simultaneously in every single iteration. The proposed PSO variant is used to optimize the learning hyper-parameters of convolutional neural networks (CNNs) and the cluster centroids of classical Fuzzy C-Means clustering respectively to overcome performance barriers. Ensemble deep networks and hybrid clustering models are subsequently constructed based on the optimized CNN and hybrid clustering segmenters for lesion segmentation. We evaluate the proposed ensemble models using three skin lesion databases, i.e., PH2, ISIC 2017, and Dermofit Image Library, and a blood cancer data set, i.e., ALL-IDB2. The empirical results indicate that our models outperform other hybrid ensemble clustering models combined with advanced PSO variants, as well as state-of-the-art deep networks in the literature for diverse challenging image segmentation tasks

Magnetic rogue wave in a perpendicular anisotropic ferromagnetic nanowire with spin-transfer torque

We present the current controlled motion of dynamic soliton embedded in spin wave background in ferromagnetic nanowire. With the stronger breather character we get the novel magnetic rogue wave and clarify its formation mechanism. The generation of magnetic rogue wave is mainly arose from the accumulation of energy and magnons toward to its central part. We also observe that the spin-polarized current can control the exchange rate of magnons between envelope soliton and background, and the critical current condition is obtained analytically. Even more interesting is that the spin-transfer torque plays the completely opposite role for the cases of below and above the critical value.Comment: 5 figure

Manpower Allocation with Time Windows and Job Teaming Constraints

In the Manpower Allocation Problem with Time Windows and Job-Teaming Constraints (MAPTWTC), we have a set of jobs located at various locations where each job requires a team of workers. Each job has a time window and a job duration, during which everyone on the team has to be present. The job requirement is satisfied if and only if the required composite team works for long enough duration within the job's time window. The objective of the problem is find a schedule to minimize a weighted sum of the total number of workers, the total travelling distances of all workers and their total waiting time. Two main approaches are proposed in the paper which are shown to be able to obtain very good performance

Trust-Oriented Composite Services Selection and Discovery

In Service-Oriented Computing (SOC) environments, service clients interact with service providers for consuming services. From the viewpoint of service clients, the trust level of a service or a service provider is a critical issue to consider in service selection and discovery, particularly when a client is looking for a service from a large set of services or service providers. However, a service may invoke other services offered by different providers forming composite services. The complex invocations in composite services greatly increase the complexity of trust-oriented service selection and discovery. In this paper, we propose novel approaches for composite service representation, trust evaluation and trust-oriented service selection and discovery. Our experiments illustrate that compared with the existing approaches our proposed trust-oriented service selection and discovery algorithm is realistic and more efficient.18 page(s

Sexual and reproductive health knowledge, contraception uptake, and factors associated with unmet need for modern contraception among adolescent female sex workers in China

Objective: In China, policy and social taboo prevent unmarried adolescents from accessing sexual and reproductive health (SRH) services. Research is needed to determine the SRH needs of highly disadvantaged groups, such as adolescent female sex workers (FSWs). This study describes SRH knowledge, contraception use, pregnancy, and factors associated with unmet need for modern contraception among adolescent FSWs in Kunming, China. Methods: A cross-sectional study using a one-stage cluster sampling method was employed to recruit adolescents aged 15 to 20 years, and who self-reported having received money or gifts in exchange for sex in the past 6 months. A semi-structured questionnaire was administered by trained peer educators or health workers. Multivariable logistic regression was conducted to determine correlates of low knowledge and unmet need for modern contraception. Results: SRH knowledge was poor among the 310 adolescents surveyed; only 39% had heard of any long-acting reversible contraception (implant, injection or IUD). Despite 98% reporting not wanting to get pregnant, just 43% reported consistent condom use and 28% currently used another form of modern contraception. Unmet need for modern contraception was found in 35% of adolescents, and was associated with having a current non-paying partner, regular alcohol use, and having poorer SRH knowledge. Past abortion was common (136, 44%). In the past year, 76% had reported a contraception consultation but only 27% reported ever receiving SRH information from a health service. Conclusions: This study demonstrated a low level of SRH knowledge, a high unmet need for modern contraception and a high prevalence of unintended pregnancy among adolescent FSWs in Kunming. Most girls relied on condoms, emergency contraception, or traditional methods, putting them at risk of unwanted pregnancy. This study identifies an urgent need for Chinese adolescent FSWs to be able to access quality SRH information and effective modern contraceptio

Study of pyridine-mediated electrochemical reduction of CO2 to methanol at high CO2 pressure

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The recently proposed highly efficient route of pyridine-catalyzed CO 2 reduction to methanol was explored on platinum electrodes at high CO 2 pressure. At 55 bar (5.5 MPa) of CO 2 , the bulk electrolysis in both potentiostatic and galvanostatic regimes resulted in methanol production with Faradaic yields of up to 10 % for the first 5–10 C cm −2 of charge passed. For longer electrolysis, the methanol concentration failed to increase proportionally and was limited to sub-ppm levels irrespective of biasing conditions and pyridine concentration. This limitation cannot be removed by electrode reactivation and/or pre-electrolysis and appears to be an inherent feature of the reduction process. In agreement with bulk electrolysis findings, the CV analysis supported by simulation indicated that hydrogen evolution is still the dominant electrode reaction in pyridine-containing electrolyte solution, even with an excess CO 2 concentration in the solution. No prominent contribution from either a direct or coupled CO 2 reduction was found. The results obtained suggest that the reduction of CO 2 to methanol is a transient process that is largely decoupled from the electrode charge transfer