Same-day Delivery Research Articles

Ride Sharing Mobile Application Optimised with Courier Service

The Cab Sharing Mobile Application with Integrated Courier Service merges passenger ride-sharing with parcel delivery to enhance urban transportation and streamline logistics. This dual-function platform maximizes vehicle utilization by enabling both commuter ride-sharing and parcel delivery within a single system. The application pairs passengers traveling in similar directions, facilitating shared rides that lower travel expenses and reduce emissions, which is beneficial for the environment. Additionally, it leverages available space in shared cabs to transport parcels, offering cost-effective, same-day delivery solutions. Through an advanced algorithm, the app efficiently plans routes to balance the needs of passengers and parcel drop-offs, minimizing delays. Key features like real-time tracking, secure payment options, and a feedback mechanism ensure a smooth, reliable experience for users. Index Terms— Cab sharing, Parcel delivery, Eco-friendly transport, Ride-sharing

Preparing for the Implementation of Long-Acting Injectable Cabotegravir for HIV Pre-Exposure Prophylaxis Within the Brazilian Public Health System (ImPrEP CAB Brasil): Qualitative Study.

Although long-acting, injectable cabotegravir (CAB-LA) pre-exposure prophylaxis (PrEP) has proven efficacious for HIV prevention in clinical trials, research is needed to guide effective implementation in real-world settings. Formative work with community members and health care providers (HCPs) is important to provide insight into the needs and contexts of specific populations and reveal potential barriers and facilitators for implementation projects. We aimed to describe the results from formative work to develop an implementation package for CAB-LA PrEP within the ImPrEP CAB Brasil study. ImPrEP CAB Brasil is an implementation study of same-day delivery of CAB-LA PrEP for young sexual and gender minority (SGM) groups (aged 18-30 years) in 6 existing oral PrEP public health clinics. We conducted formative research to prepare for the implementation of ImPrEP CAB Brasil through community mobilization, process mapping with HCPs with experience in CAB-LA, and focus group discussions (FGDs) with young SGM groups (n=92) and HCPs (n=20) to identify initial perceptions of facilitators and barriers for CAB-LA PrEP implementation, refine the mobile health (mHealth) educational tool, and evaluate the acceptability of using a text message appointment reminder intervention through WhatsApp. FGDs were recorded, transcribed, systematically coded, and analyzed with thematic categorization by trained researchers using a qualitative data analysis program ATLAS.ti (version 7). A community mobilization team comprising 34 SGM community leaders collaborated in creating a prototype for an mHealth educational tool and contributed to the planning of peer education activities. We created 3 process maps for each site to describe the initial visit, follow-up visits, and laboratory flow. The main challenge identified for same-day CAB-LA PrEP delivery was the extended duration of clinic visits due to the numerous laboratory tests and HIV counseling steps required. Proposed solutions included having point-of-care HIV rapid tests instead of laboratory tests and additional counseling staff. Barriers for CAB-LA PrEP implementation identified through FGDs were the training of HCPs, support for adherence to injection appointments, and stigma or discrimination against SGM groups and persons using PrEP. The mHealth educational tool and WhatsApp reminders were highly acceptable by SGM groups and HCPs, indicating their potential to support PrEP choice and adherence. Content analysis on the cultural appropriateness of the language and overall clarity of the material contributed to the refinement of the mHealth tool. Structured formative work with SGM persons and HCPs generated important refinements to context-specific materials and plans to launch ImPrEP CAB Brasil in public health clinics. Ongoing implementation monitoring will use the process maps to identify additional barriers and potential solutions to same-day delivery of CAB-LA PrEP. Summative evaluations are needed to measure the effectiveness of the mHealth educational tool to support PrEP choice and the use of WhatsApp appointment reminders.

Optimization of direct supply chains in the restaurant industry: Addressing key challenges through technological innovation

This study aimed to explore the potential of different supply channels in the restaurant industry, with a focus on optimizing direct supply using advanced technologies. By applying economic and statistical methods, the research analyses the role of large distributors and wholesalers in the United States food supply chain. While these supply chain participants support restaurant and manufacturer operations, they can also increase costs, reduce marketing and logistics control, and hinder the development of customer relationships. Direct supply presents significant advantages, such as improved brand loyalty, higher profit retention, and access to high-quality products. However, its growth is constrained by challenges like high marketing expenses and the implementation of same-day delivery. These costs include investments in advertising, technology platforms, CRM systems, and staff remuneration. Key obstacles to same-day delivery include staff shortages (especially drivers), high delivery costs, and inefficient logistics. The study suggests that technological solutions, including the development of direct access networks, automated promotional tools, and logistics infrastructure, can help mitigate these challenges. The Sample Box Marathon and Rgand Prime Fulfilment platforms are analysed as practical solutions that reduce costs and improve profitability. The findings of this research can provide valuable insights for producers and restaurants looking to optimize their supply chain operation

380: Clinical experience with same-day delivery of adaptive palliative radiotherapy without a planning CT

380: Clinical experience with same-day delivery of adaptive palliative radiotherapy without a planning CT

Cool ­­­­Guru E-Commerce Website using Sentiment Analysis Prediction

The Cool Guru is a brick-and-mortar establishment that has been repurposed into a fulfillment center. These distribution hubs are not accessible to the general public, allowing for ample inventory space and efficient order fulfillment. Cool Gurus offer an extensive and ever-expanding array of resources, enabling customers to purchase products online with options for same-day delivery or even within hours, as well as in-store pickup. While the concept is not novel, various companies, including Whole Foods, Walmart, Target, Bed Bath & Beyond, and numerous major clothing retailers, have employed similar strategies. However, with brick-and-mortar stores facing challenges during closures, the prevalence of Cool Gurus has surged. Our team is currently developing a Cool Guru utilizing NLP technology to analyze real-time customer feedback and sentiment, enabling us to automate product ranking and enhance customer satisfaction. Keywords: Cool Guru, brick-and-mortar, lockdown, shoppers, Bed Bath & Beyond, Natural Language Processing, Sentiment Analysis.

Economic evaluation alongside a clinical trial of near-to-patient testing for sexually transmitted infections

BackgroundCurrent clinical care for common bacterial STIs (Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG) and Mycoplasma genitalium (MG)) involves empiric antimicrobial therapy when clients are symptomatic, or if asymptomatic, waiting for laboratory testing and recall if indicated. Near-to-patient testing (NPT) can improve pathogen-specific prescribing and reduce unnecessary or inappropriate antibiotic use in treating sexually transmitted infections (STI) by providing same-day delivery of results and treatment. MethodsWe compared the economic cost of NPT to current clinic practice for managing clients with suspected proctitis, non-gonococcal urethritis (NGU), or as an STI contact, from a health provider’s perspective. With a microsimulation of 1000 clients, we calculated the cost per client tested and per STI- and pathogen- detected for each testing strategy. Sensitivity analyses were conducted to assess the robustness of the main outcomes. Costs are reported as Australian dollars (2023). ResultsIn the standard care arm, cost per client tested for proctitis, NGU in men who have sex with men (MSM) and heterosexual men were the highest at $247.96 (95% Prediction Interval (PI): 246.77–249.15), $204.23 (95% PI: 202.70–205.75) and $195.01 (95% PI: 193.81–196.21) respectively. Comparatively, in the NPT arm, it costs $162.36 (95% PI: 161.43–163.28), $158.39 (95% PI: 157.62–159.15) and $149.17 (95% PI: 148.62–149.73), respectively. Using NPT resulted in cost savings of 34.52%, 22.45% and 23.51%, respectively. Among all the testing strategies, substantial difference in cost per client tested between the standard care arm and the NPT arm was observed for contacts of CT or NG, varying from 27.37% to 35.28%. ConclusionWe found that NPT is cost-saving compared with standard clinical care for individuals with STI symptoms and sexual contacts of CT, NG, and MG.

Dynamic vehicle routing problem with drone resupply for same-day delivery

We study a same-day delivery problem where customer orders arrive dynamically throughout the day and the service operator must determine, in real-time, whether to accept the orders and how to adjust the ongoing distribution plan. We develop a route-based Markov Decision Process and an efficient online policy to dynamically route a truck that can receive newly arrived orders along its route via drones dispatched from a depot. Numerical experiments show that our online policy has an average fill rate decrease of at most 20% over the perfect-information counterpart. Further, this online policy has a fill rate increase of up to 8% over a naïve greedy policy. We also show that drone resupply increases fill rates by up to 21% compared to a conventional truck-only resupply system. Computational times to make each decision are in the hundredths of a second, thus allowing real-time feedback to customers regarding their eligibility for same-day delivery.

An Iterative Sample Scenario Approach for the Dynamic Dispatch Waves Problem

A challenge in same-day delivery operations is that delivery requests are typically not known beforehand, but are instead revealed dynamically during the day. This uncertainty introduces a trade-off between dispatching vehicles to serve requests as soon as they are revealed to ensure timely delivery and delaying the dispatching decision to consolidate routing decisions with future, currently unknown requests. In this paper, we study the dynamic dispatch waves problem, a same-day delivery problem in which vehicles are dispatched at fixed decision moments. At each decision moment, the system operator must decide which of the known requests to dispatch and how to route these dispatched requests. The operator’s goal is to minimize the total routing cost while ensuring that all requests are served on time. We propose iterative conditional dispatch (ICD), an iterative solution construction procedure based on a sample scenario approach. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until a final dispatching decision is made in the last iteration. We develop two variants of ICD: one variant based on thresholds, and another variant based on similarity. A significant strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: through rigorous numerical experiments, we show that both variants efficiently navigate the large state and action spaces of the dynamic dispatch waves problem and quickly converge to a high-quality solution. Finally, we demonstrate that the threshold-based ICD variant achieves excellent results on instances from the EURO Meets NeurIPS 2022 Vehicle Routing Competition, nearly matching the performance of the winning machine learning–based strategy. History: This paper has been accepted for the Transportation Science Special Issue on DIMACS Implementation Challenge: Vehicle Routing Problems. Funding: This work was supported by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0111 .

Consistent routing for local same-day delivery via micro-hubs

An increasing number of local shops offer same-day delivery in order to compete with the online giants. However, the distribution of parcels from individual shops to customers reduces the rare consolidation opportunities in the last mile even further. Thus, shops start collaborating on urban same-day delivery by using shared vehicles and micro-depots for consolidated transportation of parcels. At this, many stakeholders (storekeepers, drivers, and customers) need to be coordinated. Consistent routes between micro-hubs simplify the distribution process and increase reliability for all stakeholders involved. The shared vehicles thus conduct consistent daily routes between micro-hubs in the city, serving as transshipment and consolidation centres. This allows stores to bring orders to the next micro-hub, where the parcel is picked up by a vehicle and delivered to the micro-hub closest to its destination—if it is feasible with respect to the vehicle’s consistent daily schedule. Creating effective schedules is therefore very important. The difficulty of finding an effective consistent route is amplified by the daily uncertainty in order placements. We model the problem as a two-stage stochastic program. While the first stage determines the vehicle schedules, the second stage optimises the flow of realised orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a time-expanded network formulation of the problem which is solved to optimality using commercial MIP-software. We assess our model against a non-consistent upper bound and a practically-inspired heuristic to evaluate the cost of consistency and the consolidation of goods. We analyse the performance of our method for a variety of instance settings. We observe that collaborative delivery via micro-hubs is worthwhile for delivery time promises of two hours or more. Noticeably, for these service promises, the costs of consistency are surprisingly low.

Deploying an RNA-Based Gene Writer System and Lipid Nanoparticle (LNP) Delivery to Generate Functional Chimeric Antigen Receptor (CAR) T Cells with in Vitro and In Vivo Anti-Tumor Activity

The FDA approval of autologous Chimeric Antigen Receptor (CAR) T-cell therapies have added a powerful tool in the armamentarium to treat relapsed and refractory leukemias and lymphomas. However, significant challenges limit these therapies from accommodating current patient demand including supply chain limitations and wait times associated with availability of GMP quality viral vectors and long needle-to-needle time from initiation of treatment to receiving the final drug product. As of yet other competitive therapies including bi-specific antibodies and allogeneic CAR-T have not achieved the same level of therapeutic efficacy as conventional autologous T cell therapies highlighting the critical unmet need for same day treatment options for autologous CAR-T products. RNA Gene Writers leverage target-primed reverse transcription (TPRT) biochemistry evolved from non-LTR retrotransposon mobile genetic elements to modify the genetic information in cells using RNA templates and without the need to introduce DNA breaks. Moreover, RNA Gene Writers can be engineered to catalyze a variety of editing reactions, such as the introduction of gene-length DNA sequences, substitutions, insertions, and deletions. These edits can be achieved with all-RNA delivery in primary cells and in vivo, eliminating the necessity for viral vectors and DNA template-based gene editing. LNP-RNA delivery of RNA Gene Writers resulted in the integration and expression of transgenes in >60% of primary human T cells in vitro. Furthermore, our LNPs deliver RNA to T cells in vivo, with 80% reporter expression in a humanized mouse model and 45% in non-human primates (NHP). We have demonstrated LNP delivery of RNA Writers into primary human T cells with an RNA template encoding a CAR cassette can achieve >20% CAR+ T cells without detriments to cell viability or proliferative capacity and have demonstrated ability to mediate tumor cell killing in vitro. Moreover, RNA Gene Writer derived CAR-T cells can be introduced into mouse xenograft models to clear antigen specific tumors in vivo. CAR mRNA can also be packaged into proprietary LNP formulations and delivered to primary human T cells in a mixture of lymphocytes commonly found in patient leukapheresis to generate CAR-T cells in vitro. This opens the possibility of using RNA Gene Writers to develop a same-day CAR-T treatment. In addition, the modularity of our RNA Gene Writing technology allows multiplex editing to co-introduce multiple genetic changes including generation of universal and more potent CAR-T cells through the knock-out of B2M and TRAC at comparable levels (>80% double knockout) to Cas9 nucleases. Further, we show that we can achieve both edits simultaneously in 80% of T cells that have achieved CAR transgene insertion via another RNA Gene Writer enzyme. Importantly, we have demonstrated successful B2M and TRAC double knock-out without inducing translocations (undetectable vs 8% translocations with Cas9). RNA Gene Writing technology is uniquely positioned for same-day delivery of all-RNA components toenable a wide range of editing applications, from gene knock-out to gene integration. The ability to package the RNA Gene Writer system into LNPs unlocks the potential for the genetic engineering of autologous T cell therapies to enable same-day delivery a current limitation of conventional lentiviral-based CAR-T therapies.

Same-day delivery time-guarantee problem in online retail

This study introduces the same-day delivery time-guarantee (SDDTG) problem for supporting online retail. In the SDDTG, orders are placed online and are processed and delivered from a depot to customer locations in the same day. The problem seeks optimal delivery time guarantees to offer customers as they consider making purchases to increase purchase decision likelihood while accounting for delivery-related, supply-side costs that can affect profits. Time guarantees are decision variables rather than parameters (as is typical) and are designed around anticipated customer satisfaction levels and purchase likelihoods. Delivery deadlines are not merely given to customers once they make their purchases, but rather the attractiveness of the offered delivery guarantees affects whether they make their purchases, i.e., whether demand is realized. The problem is conceptualized as a multi-stage, stochastic, mixed-integer program in which uncertainties associated with customer reaction to delivery time guarantee offers and their arrival over time are captured. Given a shrinking horizon over a fixed planning horizon, the multi-stage program is approximated by a series of two-stage programs. A parallelized progressive hedging solution methodology is proposed and insights from its application on a case study. The problem recognizes tradeoffs between meeting promised delivery times and capturing the market.

Lateness in production systems - In a nutshell: How to determine the causes of lateness at work systems?

Adherence to customer due dates is the yardstick for the performance of manufacturing companies. In the era of same-day delivery, consumers expect reliable delivery of ordered goods and short delivery times. Also, in the field of business-to-business supply, it is evident that adherence to delivery dates is a fundamental logistical objective for companies. Contract manufacturers, in particular, are confronted with significant challenges: strong fluctuations in customer demand, shorter requested delivery times, and high competitive pressure require appropriate organisation, planning and control of production. However, companies often miss their schedule reliability targets and fail to identify the right causes for these failures. This raises the question of what factors influence the failure to meet schedule reliability targets, how to identify such factors, and what options are available to counteract them. This contribution addresses this issue and focuses on ways to analyse the emerging lateness at work systems in production areas as a deviation of the actual form the planned throughput time. We present existing approaches to analysing the lateness behaviour at work systems and extend the current theory of logistical modelling to determine the three drivers of the so-called relative lateness – planning influences, variance of work-in-process (WIP) and sequence deviations – at work systems systematically. Through this analysis, we enable the practical applicator to initiate target-oriented countermeasures to improve the schedule reliability of their work systems with acceptable analysis expenditure.

A branch-and-regret algorithm for the same-day delivery problem

We study a dynamic vehicle routing problem where stochastic customers request urgent deliveries characterized by restricted time windows. The aim is to use a fleet of vehicles to maximize the number of served requests and minimize the traveled distance. The problem is known in the literature as the same-day delivery problem, and it is of high importance because it models a number of real-world applications, including the delivery of online purchases. We solve the same-day delivery problem by proposing a novel branch-and-regret algorithm in which sampled scenarios are used to anticipate future events and an adaptive large neighborhood search is iteratively invoked to optimize routing plans. The branch-and-regret is equipped with four innovation elements: a new way to model the subproblem, a new policy to generate scenarios, new consensus functions, and a new branching scheme Extensive computational experiments on a large variety of instances prove the outstanding performance of the branch-and-regret, also in comparison with recent literature, in terms of served requests, traveled distance, and computational effort.

Deadline-Aware Multi-Agent Tour Planning

The increasing demand for same-day delivery and the commitment of e-commerce companies to this service raise a number of challenges in logistics. One of these challenges for fulfillment centers is to coordinate hundreds of mobile robots in their automated warehouses efficiently to allow for the retrieval and packing of thousands of ordered items within the promised delivery deadlines. We formulate this challenge as the new problem of deadline-aware multi-agent tour planning, where the objective is to coordinate robots to visit multiple picking stations in congested warehouses to allow as many orders to be packed on time as possible. To solve it, we propose LaRge NeighbOrhood Search for DEadline-Aware MulTi-Agent Tour PlAnning (ROSETTA). We conduct extensive experiments to evaluate ROSETTA with up to 350 robots in simulated warehouses inspired by KIVA systems. We show that it increases the number of orders completed on time by up to 38% compared to several baseline algorithms and also significantly outperforms them in terms of throughput and station utilization.

Routing and scheduling decisions for a single-hub same-day delivery network

The present research addresses the problem of routing and scheduling in a home delivery network with distinct pickups and deliveries. A fleet of capacitated vehicles belonging to the only hub center of the network transports the demands between the network nodes. The proposed mixed integer programming model minimizes the total transportation costs in a situation where demand splitting is possible. This model can be used in general hub networks and many-to-many networks. Due to the complexity of the problem, valid inequalities are used to reinforce the formulations. Then, a genetic algorithm serves to solve problems of various sizes. The performance of the reinforced model along with the valid inequalities and the genetic algorithm is examined through a case study of a same-day postal delivery company. The computational results show the good performance of the solution methods.

Ride-hailing app use for same-day delivery services of foods and groceries during the implementation of social activity restrictions in Indonesia

This study investigates e-shopping behavior change through ride-hailing applications (RHAs) for grocery and food as an alternative way to minimize out-of-home activities during the pandemic. Exploratory factor analysis and structural equation modeling were applied, which utilized data collected from a web-based questionnaire survey during the implementation of social activity restrictions in August 2021. The modeling results show a complementary effect between food and grocery delivery services, where an increase in food delivery is followed by an increase in grocery delivery, but not vice versa. Meanwhile, grocery delivery could substitute in-store grocery shopping. The frequency of food delivery before the pandemic also significantly affects food and grocery deliveries during the pandemic. The more individuals avail food delivery services before the pandemic, the more they avail grocery delivery services during the pandemic. In contrast, the less likely people are to avail food delivery services before the pandemic, the more likely they are to avail food delivery services during the pandemic. The study also found that RHA use for food delivery is influenced by the latent variable of e-shopping enjoyment, whereas the latent variable of e-shopping benefits affects RHA use for grocery delivery. Regarding the socio-demographic effect, females and well-educated people tend to increase RHA use for grocery delivery, and millennials are more likely to participate in grocery shopping and dining out. The findings provide valuable insights into the suppression of virus spread in the short term and travel demand management in the medium term.

Dark Store E-Commerce Website using Sentiment Analysis Prediction

Abstract: The Dark Store is a physical store that has been closed and turned into a fulfilment centre. These distribution points are not open to the public, which allows more space for inventory and the ability to fill orders quickly and accurately. Dark Stores has the facility to provide customers with innumerable and expanding resources availability and provides options such as of buying online products, the very same-day delivery or even in instance of hours, or in-store pickup. However, this concept is not new.

Enabling same-day delivery using a drone resupply model with transshipment points

We study a hybrid logistics delivery model which incorporates trucks and drones. In this model, named the Drone Resupply Model with Transshipment Points (DRMTP), drones are used to resupply transshipment points where trucks visit to pick up packages for delivery to customers. A simulation model is developed to simulate the delivery operations. The model is evaluated in two case studies in an urban area and a suburban area in Massachusetts, USA. Using this resupply model, the average delivery time is reduced compared to the conventional truck-only delivery model. The experimental results in the suburban region show that the DRMTP model with one transshipment point achieves 36% lower delivery time compared to the truck-only model. In the urban region, the model with four transshipment points obtains 66% delivery time reduction compared to the truck-only model. The results indicate that the proposed model can be used to enhance the efficiency of the last-mile delivery operations.

쿠팡 플랫폼의 물류 서비스 품질이 만족과 신뢰, 재구매의도에 미치는 영향에 관한 연구

In modern society, with the increasing number of online shopping platforms and their development, various platforms have also launched logistics services such as same-day delivery and morning delivery, and the competition among platforms has become more intense. Therefore, it is necessary to study the influence of logistics service quality, user satisfaction and trust on user repurchase intention. In addition, the flexibility of platform logistics services is an important boost to help the platform succeed and meet the needs of different groups, but there is currently a lack of relevant demonstration research. Therefore, based on the theoretical background of logistics service quality in online shopping, this study combines order quality, delivery quality, information quality, customer service and the flexibility of the Coupang shopping platform to construct a research model and establish hypotheses. From January 6 to February 3, 2023, a questionnaire survey was conducted in the form of a Google questionnaire for Coupang users with experience in using and purchasing. At the end of the investigation, a total of 500 questionnaires were received, except for 47 questionnaires with no purchase history and 49 dishonest ones, the remaining 404 questionnaires were statistically analyzed using SPSS, and finally analyzed. The results of this study show that, first of all, in terms of logistics service quality, order quality, delivery quality, customer service, and flexibility have certain positive (+) effects on satisfaction. However, information quality did not affect satisfaction. This means that the quality of information related to orders, transactions, and logistics on the platform cannot meet consumers’ expectations. Second, the quality of logistics services has a positive (+) impact on information products. Third, satisfaction and trust have a positive (+) impact on all users’ repurchase intentions. Many people are likely to become loyal users of the platform. This study reveals a causal relationship between flexibility and satisfaction and trust, with important implications. And it proves that consumers’ repurchase intention is based on excellent product and logistics service quality

Who has access to e-commerce and when? Time-varying service regions in same-day delivery

We study the design of same-day delivery (SDD) systems under the assumption that service regions are allowed to vary over the course of the day; equivalently, that customers in different locations may have access to SDD for different lengths of time over the service day or may have no access at all. This contrasts with the bulk of the literature, in which a service region is defined in advance and all customers in the service region can place SDD orders during the same time window. Leveraging continuous approximation techniques to capture average-case system behavior, we derive optimal service region areas and corresponding SDD order cutoff times to maximize the expected number of orders served per day. We quantify the benefit of allowing the service regions to vary, both theoretically and empirically, and discuss related equity issues in SDD systems. We illustrate and validate our results with a case study set in the Phoenix, Arizona metropolitan area.