Package Delivery Research Articles

Efficient Package Delivery Task Assignment for Truck and High Capacity Drone

Efficient Package Delivery Task Assignment for Truck and High Capacity Drone

Orgatma – the 3PL challenge

Learning outcomes This case study would help students in understanding the dynamics of logistics and logistics vendor roles and contributions to overall business operations. The case study covers real-time information for applying the theoretical knowledge students gain related to the selection of logistics vendor. It would help students to understand and evaluate the dynamics of a new start-up related to cost, profits and dependency; understand and analyze the importance of third-party logistics (3PL) service providers in the supply chain; become aware of the key performance indicators (KPIs) important in the selection of logistics vendor; and develop and create measures for selecting logistics vendors on the basis of KPIs. Case overview/synopsis This case study was about an innovative start-up operating in the field of organic edible oils. The company catered to end consumers with its indigenous technology and processes. The innovative and healthy products were appreciated by the consumers, as was reflected in the surging demand figures. With the increasing popularity of organic products, the orders were surging. At the same time, issues such as damaged product delivery, increased cost per delivery of small packages and failure to deliver because of unserved pin codes by their logistics partners were being faced by the company. The case discusses the dilemma faced by the protagonist regarding the selection of the right 3PL partner. The case study is suitable for teaching courses in operations and logistics, supply chain management and entrepreneurship-related courses. Complexity academic level This case study is appropriate for postgraduate courses in entrepreneurship, operations management, logistics and supply chain management and general management. Supplementary materials Teaching notes are available for educators only. Subject code CSS9: Operations and logistics.

Multi-Functional Arduino Robot “Anima”: Review

Abstract: In 21st century, Robots are playing a very important role for the development of human and various technologies that are being used in our day-to-day life. This review paper is on the robot made by the same Team. Robots are being used in various sectors like industrial, transportation, medical, space exploration programs, etc. and also by various E-commerce companies like Amazon, Flipkart, Alibaba for the delivery of packages to the consumers. Tracking, automation and obstacle avoidance are the three most important features that is needed for every automated mobile robots. In this review there is a robot named ‘ANIMA’ which runs automatically on four wheels which all are controlled by Arduino UNO (Microcontroller) , Ultrasonic Sensor, Bluetooth module. Programming is done by Arduino Software (IDE) with version of v2.2.1 . It is a wheeled robot which can also be controlled by bluetooth module by any external controllers or smartphone

Acoustic Localization of Drones in Precise Landing: The Research and Practice with MicNest

Delivery drones have the potential to revolutionize transportation and distribution of goods. With their autonomous navigation capabilities, they offer an efficient solution to bypass the challenges posed by complex urban traffic and enable instant package delivery. Many industrial firms are actively exploring the commercial feasibility of instant drone deliveries. Meituan, one of the largest companies in this area, devised a systematic approach to instant delivery using drones. The process begins with loading the package onto the drone, which then takes off, ascends to cruising altitude, and sets a direct course towards the designated destination. Typically, this destination is a Meituan-operated self-collection station located near the customer.

Lived experiences of students with virological failure on antiretrovirals at a university in Limpopo.

Human immunodeficiency virus (HIV)-positive students at a rural university in Limpopo province are followed-up according to the national guidelines for the treatment of HIV. Blood monitoring revealed that some students on antiretroviral (ARV) treatment were not virologically suppressed despite adherence and compliance being emphasised at every visit. The study sought to identify the students' experiences that were hindering the viral load from improving. A two-phase qualitative, explorative, descriptive study design was followed. Convenience purposive sampling methods were taken on. By means of a semi-structured interview guide, face-to-face interviews were directed. Thematic content analysis was applied. Non-disclosure, noisy ARV packaging, stigma, and service delivery played a role in determining levels of student adherence and compliance with ARVs in the study sample. Study findings suggest practical recommendations to improve compliance among students on ARVs: provision of HIV education to all students to help reduce stigma and make it easier to disclose HIV status; use of user-friendly noise-free packaging by pharmaceutical companies to enclose medication, such as blister packs; a supermarket approach in service delivery points to reduce the stigmatising effects of consulting rooms for ARV services.Contribution:There is scope to examine the relevance of these findings for other students in the country, to compare them, and to use material from larger studies to guide targeted interventions that could improve adherence among young people.

Analisis Sentimen Mengenai Kualitas Layanan Jasa Ekspedisi Shopee Xpress Pada Media Sosial Twitter

Technological advances in Indonesia has developed rapidly so that it cannot be separated from people's lives. This technological advancement has had a significant impact on e-commerce and increased usage of delivery services. The more shipping services available, the tighter the competition will be, so the freight forwarders must prepare well if they want to survive in this business competition. Shopee Xpress is one of the expedition services in Indonesia which was developed directly by Shopee so that every package that uses this expedition will be sent and handled directly by the official team from Shopee. However, many customers complain about Shopee Xpress expedition services regarding late delivery, untracked, damaged or lost packages and unfriendly employees. This study aims to understand consumer opinion on the quality of Shopee Xpress services on Twitter using positive and negative sentiments. Research data was collected for 3 months from 01 November 2022 to 30 January 2023. The data obtained was then processed using the Naïve Bayes Classifier algorithm with a sentiment analysis method based on the dimensions of service quality. The word cloud is used to describe the words that appear most frequently in each dimension of service quality based on their sentiments. The results show that the model produces an accuracy value of 86.33% which indicates that the model has described the accuracy of the data properly and is valid. Negative sentiment dominates consumer opinion regarding Shopee Xpress with the reliability dimension being the most discussed. Furthermore, based on word cloud visualization, there are several things that consumers often complain about in each dimension as a material for consideration to improve the quality of Shopee Xpress services. The results of research related to Shopee Xpress consumer opinion on Twitter social media can be used by the company as material for consideration to improve and improve the quality of services provided in order to maintain customer loyalty.

Prognosis of Li-ion Batteries Under Large Load Variations Using Hybrid Physics-Informed Neural Networks

The development of new modes of transportation such as electric vertical takeoff and landing aircraft and the use of drones for package and medical delivery have increased the demand for reliable and powerful electric batteries. Therefore, accurately predicting the degradation of a battery’s state-ofhealth (SOH) and state-of-charge (SOC) is a crucial albeit still challenging task. There is a need for models that can accurately predict the SOH and SOC while taking into account the specific characteristics of a battery cell and its usage profile. While traditional physics-based and data-driven approaches are used to monitor the SOH and SOC, they both have limitations related to computational costs or that require engineers to continually update their prediction models as new battery cells are developed and put into use in battery-powered vehiclefleets.Battery capacity degradation can vary from battery to battery and can also be influenced by changes in load due to internal thermal stress. While sophisticated electrochemistry-based models can provide precise predictions of the SOC during adischarge cycle when parameters are well-tuned, using highfidelity models for prognostics purposes can be computationally expensive. Those models also require tuning to specific battery types and at times to specific specimens, thus hindering generalization. In contrast, purely data-driven approaches can learn the relationship between input and output for SOC prediction based on load input, but they require a large and diverse training dataset and lack any physical or electrochemical understanding, making far-ahead predictions challenging if test loading conditions fall outside the training distribution. To address some of the drawbacks of the aforementioned modeling approaches, in this paper, we enhance a hybrid physics-informed machine learning version of a battery SOC model we presented in previous work to predict voltage dropduring discharge. The enhanced model captures the effect of wide variation of load levels, in the form of input current,which causes large thermal stress cycles. The cell temperature build-up during a discharge cycle is used to identify temperature-sensitive model parameters. Additionally, we enhance an existing aging model built upon cumulative energy drawn by introducing the effect of the load level. We then map cumulative energy and load level to battery capacity with a Gaussian process model. To validate our approach we use a battery aging dataset collected on a self-developed testbed, where we used a wide current level range to age battery packs in accelerated fashion. Prediction results show that our model can be successfully calibrated and generalizes across all applied load levels.

Mission-Specific Prognosis of Li-ion Batteries using Hybrid Physics-Informed Neural Networks

New transportation modalities such as electric powered verticaltakeoff and landing aircraft and logistic applicationslike delivery of packages with drones require highly reliableand powerful electric batteries for operation. A challengingbut very important task hereby is the precise forecasting ofthe degradation of battery state-of-health (SOH) and stateof-charge (SOC). While high-fidelity electrochemistry basedmodels can provide precise predictions of the SOC, they canbe computationally expensive. On the other hand, purely datadrivenapproaches require a large amount of training data inorder to learn the input to output relation. In this research animproved hybrid physics-informed machine learning modelis introduced, that conserves the electrochemistry based lawsand is implemented with data-driven layers to compensate forunknown portions of internal voltage drop during discharge.Preliminary results indicate that the model can predict dischargefor a large variety of loads, accurately predicts capacitydegradation over age and can be enhanced through extractinginformation from cell temperature data as surrogate for aging.

Research on optimization technology based on mobile terminals convolutional neural networks

Convolutional neural networks play a very important role in computer vision, such as image classification, image segmentation, and handwriting recognition have been widely used. In daily life, this technology is used in the photo recognition of e-commerce platforms. However, the timing of the identification process grows into a major problem. Therefore, it is particularly important to reduce the recognition time by optimizing the deep learning model. To solve this problem, two experimental methods are proposed to optimize the volume of the convolutional neural network model. The first is to reduce the size of the model by scaling down the convolutional kernel. The second is to prune the model with L-1 norm to reduce the size of the model and improve the running speed. According to the experimental results, the two experimental methods have achieved remarkable optimization effects. In the first experiment, the method of scaling down convolutional kernel has an important optimization effect for training the deep learning model of small data sets. In another experiment using L-1 pruning algorithm greatly improves the running speed of models by reducing the size of models. To sum up, the optimization method proposed above for the convolutional neural network model on the mobile end can be applied in the field that requires a large amount of image classification, such as delivery package sorting. At the same time, to better improve the performance of the model, it will become feasible to use a variety of optimization methods to tune it.

Investigating and Analyzing the Potential for Regenerating Excess Energy in a Helicopter UAV

Energy consumption is a critical parameter in the development of helicopter Unmanned Aerial Vehicles (UAVs). Today, helicopter UAVs are playing an increasingly pivotal role in various applications, from surveillance and reconnaissance to package delivery and search and rescue missions. However, their energy efficiency remains a pressing issue, as it directly impacts their operational duration and payload capacity. One of the key challenges in optimizing energy consumption is the existence of excess power during flight, arising from the intricate interplay between helicopter aerodynamic behavior and safety design. Typically, this excess energy is dissipated, resulting in a suboptimal performance and efficiency. This study investigated the behavior of excess power in a helicopter Unmanned Aerial Vehicle (UAV). Typically, this excess energy is wasted in conventional helicopters and helicopter UAVs. A dual-method approach, encompassing numerical and experimental methodologies, was employed to provide comprehensive insights into the helicopter UAV’s performance under various conditions. Computational fluid dynamics (CFD) simulations were performed to analyze the UAV’s aerodynamics. The simulations were validated by comparing the lift force with wind tunnel experimental data, resulting in acceptable deviations. The experimental analysis was conducted using a wind tunnel and a small-sized helicopter UAV. The experiments were designed to examine the excess power behavior of the UAV under two distinct flight conditions: hover and forward flight. The power output from the generator and power input from the battery were measured under various angular velocities and pitch angles. The results revealed a maximum excess power of 6.84% for hover conditions and 9.83% for forward flight conditions. This indicates that the maximum excess power percentage attributable to the helicopter UAV’s safety measure is 6.84% and that resulting from aerodynamics is 2.99%. The findings of this study contribute valuable knowledge to the optimization of helicopter UAV performance and the potential for harnessing excess power during flight operations. When this excess energy is harnessed, it can contribute significantly to the overall performance and efficiency of the UAV, potentially extending its flight duration or accommodating additional payload capacity that could potentially pave the way for the development of hybrid helicopter UAV models in the future.

Experimental Investigation on Effect of Temperature on FDM 3D Printing Polymers: ABS, PETG, and PLA

Four-dimensional printing is a process in which a 3D-printed object is intentionally transformed in response to an external stimulus such as temperature, which is useful when the final geometry of a 3D-printed part is not easily manufacturable. One method to demonstrate this is to print a part made of thin strips of material on a sheet of paper, heat the part, and allow it to cool. This causes the part to curl due to the difference in the thermal expansion coefficients of the paper and plastic. In an attempt to quantify the effect of different temperatures on various materials, samples of three common 3D printing filaments, acrylonitrile butadiene styrene (ABS), polyethylene terephthalate glycol (PETG), and polylactic acid (PLA), were heated at different temperatures (85 °C, 105 °C, and 125 °C) for intervals of 15 min and then allowed to cool until curling stopped. This heating and cooling cycle was repeated three times for each sample to determine if repeated heating and cooling influenced the curling. Each sample was filmed as it was cooling, which allowed the radius of curvature to be measured by tracking the uppermost point of the part, knowing the arc length, and calibrating the video based on a known linear length. After three cycles, all three materials showed a decrease in the radius of curvature (tighter curl) as heating temperature increased, with PLA showing the trend much more predominantly than ABS and PETG. Furthermore, for PETG and PLA, the radius of curvature decreased with each cycle at all temperatures, with the decrease being more significant from cycle 1 to 2 than cycle 2 to 3. Conversely, ABS only shared this trend at 125 °C. The findings of this work can provide guidelines to users on the temperature dosage for the mass manufacturing of complex geometries such as packaging, self-assembly robots, and drug delivery applications.

Internet of Things (IoT): Wireless Communications for Unmanned Aircraft System

In times of technological advancements, the use of aerial vehicles (UAVs), commonly known as drones, has become increasingly prevalent across various commercial and industrial sectors. UAVs find applications in agriculture, filmmaking, law enforcement, package delivery, aerial photography, videography, etc. There are benefits associated with utilizing UAVs. One key advantage is their ability to cover areas quickly and efficiently while accessing locations that may be challenging or hazardous for humans. Wireless communication technologies play a role in ensuring the functioning of Unmanned Aircraft Systems (UAS). Without these technologies, the United States of America would face difficulties communicating with ground control stations or relaying information to operators. This would significantly impede the country’s mission execution and overall responsibilities. Wireless communication technologies (WCT) enable the United States to maintain awareness—essential for achieving successful and secure operations. Additionally, wireless technologies allow for Unmanned Aerial Systems (UAS) control, which is crucial for missions carried out in hostile or dangerous environments. The increasing usage of drones has highlighted the need to improve networks in the United States focusing on their ability to work together effectively handle volumes of data and provide reliable broadband connectivity. This research delves into communication technologies, in this domain emphasizing their advantages, limitations, industry standards and potential areas for future investigation to address the challenges revealed by this study.

E-commerce, delivery drones and their impact on cities

E-commerce has gained importance over the last years and the COVID-19 pandemic has further strengthened this development. With the increasing opportunities that novel transport technologies such as cargo drones offer, e-commerce is likely to even become more attractive in upcoming years. This paper therefore analyses the long-run effects of increasing importance of e-commerce and, more specifically, the impact of a shift towards package delivery via drones. Using a polycentric urban spatial general equilibrium model that incorporates the different retail channels, we model the consumer’s choice between local and online shopping. In the case of online shopping, delivery takes place either via drone or via truck. Due to the equilibrium mechanisms, changes in shopping behavior potentially result in different home and work location choices or labor supply choices. The model thus allows assessing the long-run effects of e-commerce and drone delivery, also on related markets such as the markets for land, labor and goods. A numerical simulation of the model shows that each additional retail channel adds welfare and that changes in the shopping channels result in changes in location choices of households and companies.

Examining behavior specific praise as an individual behavior management strategy in a high-need educational setting

Behavior specific praise (BSP) is widely regarded as an evidence-based classroom management strategy, with decades of research supporting its use. However, relatively few studies have examined the effect of BSP as a targeted intervention strategy, particularly for traditionally underrepresented students. The present study evaluated the utility of BSP as an individual behavior management strategy for diverse students in the context of a high-need educational setting. Specifically, it examined the effects of BSP on teacher’s ratings of academically engaged and disruptive behavior in the classroom using Direct Behavior Ratings–Single Item Scales (DBR-SIS). A concurrent multiple baseline design was implemented with three racially/ethnically minoritized (REM) target students across one third and one fourth grade classroom. Results demonstrated that teachers’ estimated rates of BSP delivered to target students were successfully increased to a criterion level of six per hour following delivery of a multicomponent training package, with high levels of treatment integrity. All participating students demonstrated statistically significant improvements in behavior, although visual analysis suggested only one clear demonstration and one replication of effect. Social validity data revealed high levels of teacher acceptability and willingness to implement the BSP strategy, with mixed perceptions from students. Implications for research and practice are discussed.

Marketing Strategy Analysis in Increasing the Volume of Package Delivery Services PT. Xyz

Logistics is one of the main needs and various efforts are being made to fulfill it. These efforts include trading and exchanging goods and conducting social relations, namely by sending goods by individuals, companies and social groups. PT. XYZ is present as an expedition service startup company that offers various types of services with digital technology-based selling points that can be accessed via smartphones to make it easier for consumers. However, the very tight competition makes the company must determine an effective and efficient marketing strategy in increasing the volume of package delivery. This is what makes the author want to know how the right marketing strategy in increasing package delivery, the obstacles encountered and the solutions made by PT. XYZ. In this research method is descriptive qualitative approach, with data collection techniques through interviews, observation and documentation. Based on the results of marketing strategy research conducted by PT. XYZ is doing quite well in increasing the volume of package delivery, by collaborating with the marketing mix in the form of product, price, promotion and place to the fullest. Which at the same time can be used to overcome obstacles and present the solutions needed for companies.

Optimizing Drone delivery: An efficient design for shipper applications

<p class="Abstract">The concept of using Unmanned Aerial Vehicles (UAVs) for package delivery is gaining momentum in recent times. These Drones are capable of transporting various types of packages, including medical supplies, food, and other goods to remote or hard-to-reach areas. With the increasing demand for rapid deliveries, Drones have become a viable solution for delivering items such as blood products, vaccines, pharmaceuticals, and medical samples. The use of Drones in food delivery is also on the rise, with pizzas, tacos, and frozen beverages being some of the popular items delivered via Drones. To ensure accurate and timely deliveries, this proposed Drone delivery system would employ GPS technology to track the package’s location and reach its intended destination. To optimize the efficiency of the system, the Drone would pick up the package from the nearest warehouse to the delivery location, and both the customer and the dispatcher would have access to track the package’s live location. In this proposed system, the Drone would pick up the package from the hub and proceed to the consumer’s location, dropping the package once the consumer provides the correct OTP. To enhance the system’s performance, intelligent control methods and smart sensors are used. Additionally, this system would also have the capability of verifying package delivery via facial recognition technology when handling confidential packages. The Intelligent controllers, sensors and Actuator makes the Drone delivery more optimal.</p>

The Effect of the Number of Hidden Layers on The Performance of Deep Q-Network for Traveling Salesman Problem

The Traveling Salesman Problem (TSP) effectively represents the complex distribution issues encountered by couriers, who must carefully plan a route that includes all customer addresses while minimizing the distance traveled. As the magnitude of deliveries and the range of destinations expand, the courier's responsibility becomes progressively challenging. In this particular context, the objective of our research is to expand the existing knowledge and explore the complete capabilities of Deep Q-Network (DQN) models in order to achieve the most efficient route determination. This endeavor can potentially bring about significant changes in the courier and delivery service sector. The foundation of our unique methodology relies on an empirical inquiry, utilizing a comprehensive dataset including 178 observations obtained from motorcycle-based package delivery agents. Our research is carefully planned and executed using a comprehensive factorial experimental design. This design incorporates three crucial factors: the number of hidden layers, episodes, and epochs. The hidden layer parameter is set to a singular level, while the episode parameter is configured to explore five levels, and the epoch parameter is designed to travel four levels. The evaluation of our DQN models' performance is conducted utilizing the MSE metric as a measure. This assessment is carried out at every iterative cycle, ensuring thorough scrutiny. The central focus of our research centers on the intricate connection between episodes and epochs, and their influence on MSE. The findings of our study reveal that the association between episodes, epochs, and errors is not statistically significant although different level of episodes and epochs produces slightly different level of error.

식자재 플랫폼 비지니스의 서비스품질 영향요인이 재구매의도에 미치는 영향

As non-face-to-face consumption patterns have rapidly spread due to COVID-19, the online market has also grown rapidly, and in particular, general malls have expanded the sales of food ingredients into new portfolios, and the consumer purchasing trend is for 1-2 day delivery for raw materials where freshness is key. Service can be said to be the key driving force that made this possible. As the majority of food ingredient platforms have established similar delivery systems, it can be said that the services of each company have become standardized. In this situation where competition is intensifying, understanding what food ingredient buyers using online platforms truly want should be a priority, and an overall evaluation and measurement of factors affecting consumer behavior is necessary. In this study, in order to analyze the impact of service quality influencing factors of platform business on repurchase intention, the service quality influencing factors were transaction convenience, convenience convenience, personalized information, information quality, delivery timeliness, and packaging based on existing prior research. They were categorized by friendliness, and a survey was conducted targeting the general public who had experience purchasing food ingredients using the platform. An empirical analysis was conducted using the statistical package SPSS 24.0 on 208 responses received through a Google online survey over a period of about 3 weeks from June 20, 2023.
 The research results are as follows. First, transaction convenience, information quality, and packaging friendliness had a positive influence on repurchase intention. Second, in the relationship between service quality factors, reliability, and repurchase intention, the mediating effect of trust fully mediates the relationship between information quality → trust → repurchase intention, and partially mediates the relationship between packaging friendliness → trustworthiness → repurchase intention. It was found that Based on the results of this empirical analysis, academic and practical implications were presented. First, the significance of the study is that it confirms that branding and friendliness of delivery packaging are recognized as new differentiation points in a situation where convenience and timeliness are being standardized beyond essential factors such as price and product. Second, continuous research should be conducted and reflected in that service quality improvement points for long-term profit generation are evolving in various ways by analyzing the influence of repurchase intention rather than one-time purchase intention. Third, although the study focused on cases on large platforms, small startups targeting niche markets can also transform and apply it as a point of differentiation by varying the scale.

Performance analysis of a Vehicular Ad Hoc network Using LoRa technology and IoT devices in Amazon Rivers

Several remote communities in the Amazon region are isolated from means of access to data communication. Thus, this work proposes a communication alternative with low cost and low energy consumption based on LoRa devices. The proposal is to create an Ad Hoc Network using a transmitter device fixed on a boat and a receiver device fixed on a riverside house on the banks of the Solimões River - Amazonas. The metrics evaluated in this experiment were: Maximum Range, Maximum Time, Communication Distance, Communication Time, and Packet Delivery Rate. The results showed a 78% package delivery rate at the Maximum Range of 1563 m as the boat moved away from or approached the riverside man’s house. We conclude that the results were satisfactory within the parameters that the devices were configured and the conditions of the study scenario.

Collaborative Search and Package Delivery Strategy for UAV Swarms Under Area Restrictions

The rapid implementation of multi-task decoupling in restricted flight areas for unmanned aerial vehicle swarms is crucial to ensure swarm effectiveness. This study introduces a task-switching mechanism in the bio-inspired rule-based (Bio-RB) decision-making algorithm and establishes a mapping relationship from behavioral rules to task modes. A complete decision model is constructed for the cooperative search and package delivery tasks. To further improve the search efficiency of swarms in restricted areas, a boundary-handling strategy based on the combination of path prediction and virtual agents is proposed. The overall scheme is termed the task-driven rule-based (Task-RB) decision-making algorithm. The proposed Task-RB method is evaluated under full-flow simulation. Numerical experiments demonstrate the superior performance of the proposed Task-RB method against the Bio-RB method under different instances.