Delivery Requests Research Articles

Research in the Time of COVID: Virtual Fellowships at the Linda Hall Library.

When COVID-19 prevented scholars from traveling to libraries, archives, and museums, staff at those institutions devised creative strategies for providing remote access to their holdings. This article examines one such initiative launched by the Linda Hall Library of Science, Engineering, and Technology in Kansas City, Missouri. The Library's prioritization of print over archival materials, experience fulfilling document delivery requests, and investments in digitization infrastructure facilitated the transition to a completely virtual fellowship program during the 2021-22 academic year. Although virtual fellowships could not replicate every aspect of in-person residencies, they nonetheless supplied scholars with much-needed funding, research materials, and membership in a scholarly community. Based on the program's initial success, the Library plans to continue offering virtual fellowships to scholars otherwise unable to conduct research on site.

The Value of Preemptive Pick-Up Services in Dynamic Vehicle Routing for Last-Mile Delivery: Space-Time Network-Based Formulation and Solution Algorithms

In recent years, with the increase of emerging pick-up requests during service, logistics companies have been driven to integrate delivery and pick-up service in a dynamic environment. To provide a balanced and robust approach to cope with delivery requests and emerging pick-up requests, this article aims at considering and modeling a practically useful service principle as preemptive services. To our knowledge, most existing studies assume that the dynamically arriving requests are handled in a non-preemptive processing sequence; that is, once the delivery person is allocated to a task, the process is noninterruptible till it gets completed. In the preemptive service, a service suspension of the delivery process (with low service utility) is allowed to satisfy the pick-up requests (with high service utility) first. To provide a systematic assessment on the value of preemptive service for evolving urban logistics systems, a dynamic vehicle routing problem with preemptive pick-up service (VRPPS) is proposed to systematically describe the problem with potentially complex dynamic priorities among different tasks. Based on a dynamically constructed space-time network, this study formulates a multicommodity flow model that aims at optimizing the generalized service utility and the operating cost simultaneously. To provide a fast value approximation, we present a solution framework deploying the augmented Lagrangian relaxation approach with embedded dynamic programming algorithms. This framework jointly integrates the processes of updating request information and obtaining optimal routes. Finally, the validity and effectiveness of the proposed methods are evaluated on an illustrative network and a real-world last-mile delivery network operated by a logistics company.

DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction

The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.

Caesarean Section on Maternal Request-Ethical and Juridic Issues: A Narrative Review.

In recent decades, the rate of caesarean deliveries has increased worldwide. The reasons for this trend are still largely misunderstood and controversial among researchers. The decision often depends on the obstetrician, his beliefs and experience, the characteristics of the patients, the hospital environment and its internal protocols, the increasing use of induction of labor, the medico-legal implications, and, finally, the mother’s ability to request delivery by caesarean section without medical indication. This review aims to describe the reasons behind the increasing demand for caesarean sections by patients (CDMR) and strategies aimed at reducing caesarean section rates and educating women about the risks and benefits of CS.

A last-mile drone-assisted one-to-one pickup and delivery problem with multi-visit drone trips

Online retailers and food delivery platforms are exploring last-mile package and food delivery using drones to reduce delivery time and improve customer experience. We investigate a novel and pioneering problem, the one-to-one pickup and delivery problem with multi-trucks and multi-visit drone (OPDP-MTMV). This problem involves multiple trucks, each of which is equipped with a single drone for pickup and delivery services. The drones are capable of carrying multiple packages per drone trip. Pickup and delivery requests can be served by either a truck or a drone, subject to the one-to-one precedence constraints of the requests, the capacity constraints of the truck and the drone, and the endurance constraints of the drone. The energy consumption of a drone is based on the flight time and the varying payload along the multi-visit drone trip. The objective is to minimize the sum of the fixed cost and the variable duration cost. We formulate this problem using a mathematical model and propose an iterated local search (ILS) algorithm based on the distinct features of the problem. Finally, we conduct comprehensive computational experiments using test instances of various sizes to analyze the characteristics of the solution, the applicability of the algorithm, and the impact of different drone configurations.

Community logistics for dynamic vehicle dispatching: The effects of community departure “time” and “space”

The rise of e-commerce has completely transformed the dynamicity and problem nature of last-mile delivery, owing to significant B2C customer demand in compact urban areas. Given the unprecedented growths of urban last-mile deliveries, this paper proposes a novel postponement prioritized-route optional approach, namely Community Logistics (CL), as a new logistics tool to manage dynamic arrivals of delivery requests received in e-commerce hubs. Each vehicle is responsible for serving a “community”. At each decision epoch, fragmented e-commerce delivery requests arrived at the depot are either allocated to a community or postponed to later epochs for actual last-mile delivery. With an objective of consolidating newly arrived requests, we develop two dynamic policies – temporal and spatial, respectively for temporally delaying vehicle’s departure and spatially allocating more pre-partitioned geographical cells into one community. The main contribution of this study lies in a spatiotemporal relativity analysis and a comparative analysis. The former demonstrates the essence of incorporating both dynamic community departure times and dynamic community regions into managing urban e-commerce deliveries, whereas the latter validates the merits of Community Logistics against dynamic vehicle routing solutions. In the end, we call for further developments of community logistics strategies to address the impacts of urban deliveries due to the rise of e-commerce and online shopping.

A scalable anticipatory policy for the dynamic pickup and delivery problem

Dynamic vehicle dispatching and routing problems can be tackled by using either reactive policies (that optimize the overall inconvenience on the pending requests) or anticipatory policies (that consider the possible future demands). The anticipatory policies reported in the literature are typically unsuitable for the large instances often encountered in the real-world, where the inter-arrival time can be as little as a few seconds. In this article, we present a new scalable anticipatory policy for the Dynamic Pickup and Delivery Problem which amounts to design routes for a fleet of vehicles that must service a set of pickup and delivery requests, characterized by different priority classes, arriving according to an unknown (possibly time-varying) stochastic process. The algorithm utilizes a parametric policy function approximation in which the best parameter setting is chosen on-line on the basis of a mapping between instance features and policy parameters learned off-line by using simulation experiments. Computational results on large-scale randomly-generated instances indicate that our anticipatory procedure outperforms two reactive approaches while keeping the computational burden at a level suitable for real-world usage.

Robotic Delivery System for Multi-Storey Condominium Complexes

In certain environments like condominium complexes, couriers are only allowed to drop off their payloads at designated drop-off stations instead of delivering it directly to the recipient. These regulations have become increasingly popular in condominiums during the Covid-19 pandemic, in part due to government SOPs encouraging their adoption. A partially automated delivery system using robot couriers that deliver the payloads from the drop-off stations directly to the recipient is developed. The system is made up of a central PostgreSQL database, a Webots simulated delivery robot and two Android applications for interfacing with the system. Two individuals operate the system: the system operator and the resident requesting delivery. The resident sends their delivery requests using the Resident android application. The system operator acknowledges the delivery requests, then loads the robot and configures it to carry the package(s) up to the resident’s housing unit through the Operator android application. Navigation and obstacle avoidance are achieved through usage of the A* pathfinding algorithm. To receive the package(s) once the robot arrives, a QR code enabled compartment on the robot is unlocked using the Resident android application once again. The PostgreSQL database and Advantech WISE-PaaS Dashboard are used to display delivery requests, residents’ data and robot status for system operators. NodeRED and MQTT are used to facilitate communications between robot, applications and database. In the end, the simulated robot is able to navigate a virtual environment with minimal faults and communicate properly with the applications and database. Robot speed is set at 0.27 ms-1 when empty, and maximum carrying weight is tested to be 20.5 kg with a delivery time of 1549 seconds for the arbitrary simulated delivery route.

Community logistics: a dynamic strategy for facilitating immediate parcel delivery to smart lockers

The COVID Pandemic since early 2019 has imposed significant effects on our life. In the retail and logistics sector, the large-scale national lockdown has drastically driven e-commerce sales because the e-marketplace has become the only sales channel. Whilst the pandemic has accelerated the shift towards a more digital world and led to an irreversible dependence on e-commerce retailing, the pressure is on retailers and logistics service providers to respond to the growing demand for immediate delivery in the e-commerce era. Given the integration of smart lockers into developing a more favourable environment which potentially makes immediate delivery more feasible, this paper introduces a novel, dynamic delivery strategy, namely Community Logistics Strategy (CLS), for formulating and updating the new delivery plan in real time as new delivery requests to smart lockers arrive. To shed light on the effect of dynamic order arrival towards delivery planning, the CLS attempts to update the delivery plan by taking new requests into account in real time. Simulation results reveal the superiority of the proposed strategy in managing e-commerce delivery requests, especially within megacities where consumers are highly dense in a compact geographical area.

Crowdshipping: An open VRP variant with stochastic destinations

E-commerce continues to grow throughout the world due to people’s preference to stay at home rather than going to a brick-and-mortar retail store. COVID-19 has exacerbated this trend. Concurrently, crowd-shipping has been gaining in popularity due to both the increase in e-commerce and the current pressures due to COVID-19. We consider a setting where a crowd-shipping platform can fulfill heterogeneous delivery requests from a central depot with a fleet of professionally driven vehicles and a pool of capacitated occasional drivers. We divide delivery requests into sectors to represent different neighborhoods in a city. Occasional drivers have unknown destinations that can be anywhere inside the sectors. Route duration constraints are modeled to motivate participation and increase the probability of route-acceptance by keeping routes short. We assume that occasional drivers will choose routes that are better compensated and that the probability of route-acceptance is dependent on other routes being offered. We propose a two-stage stochastic model to formulate the problem. We use a branch-and-price algorithm capable of solving 50-customer instances, and develop a heuristic that can solve larger 100-customer instances quickly. An upper bound for the total number of occasional drivers is used to reduce the number of constraints in the master problem and reduce the complexity of the pricing problems. We show that occasional drivers with destinations far from the depot reduce the cost by over 30%, while occasional drivers with destinations that are near the depot reduce the cost by 20%. We show that route duration constraints and capacity constraints can restrict the occasional driver routes and both need to simultaneously increase in order to have cost reductions. This setting of crowd-shipping is a viable option for last-mile deliveries.

Integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies

The widespread application of electric vehicles for city last-mile logistics has been enhanced by the emerging trend of urban sustainable mobility, which intends to reduce vehicle emissions and the dependence on fossil fuels. The recharging facility location is critical for electric logistics network planning, because it significantly affects future operation costs and efficiency. This study proposes an integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies, which is formulated as a time-discretized multicommodity network flow optimization model based on a space–time–state–energy representation network. This study aims to optimize the selection of the recharging station location considering route planning with complicated constraints of recharging capacity, facility construction budget, vehicle loading capacity, battery remaining capacity, spatial structure of real road networks, mixed pickup and delivery requests, and service time windows. A hybrid Lagrangian relaxation and alternating direction method of multipliers (LR-ADMM) decomposition solution framework is constructed to decouple the proposed integrated problem into a recharging station location problem for strategic planning and an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies for operational decisions. These two subproblems are solved alternately by time-dependent forward dynamic programming algorithms embedded into the LR-ADMM framework. The solution quality is guaranteed by calculating the optimality gap between the best lower and upper bounds for each iteration. The experimental results based on the Sioux-Falls network and real-world West Jordan network prove the computational effectiveness and optimization quality of the proposed solution approach.

FOOD DELIVERY APP: TRENDS, FEATURES & TECHNOLOGIES USED FOR APP DEVELOPMENT

The demand for food delivery app development is rising. Digital orders and deliveries have grown 300 faster than traditional dine-in deals since 2020. According to Statista, the‘ platform-to-consumer’ delivery member is anticipated to reachUS$96.8 million worldwide by 2024. Considering the consequences of COVID-19, this growth will remain steady for the foreseeable future. On- demand food delivery services came especially popular during COVID-19 lockdowns. The epidemic has further than doubled the food delivery business .Starting a food delivery service moment will turn into a prosperous business in the future. This paper will help you learn how to produce a food delivery app from both business and specialized perspectives The competition may be fierce, but beginners to the food delivery request should n’t be discouraged by rivalry. However, there’s every chance of success, If you ’re fortified with an original idea and robust software with advanced features. Analyzing challengers and looking for gaps in their services can help you come up with a brilliant result. As the operation of food delivery Apps grows in the metropolitan metropolises in India, a growing trend is also being seen in lower metropolises. It has thus come a necessity for online food delivery service providers to identify the preferences and comprehensions of consumers to enable them to meet their prospects. Through this study the literal background, current script and possible unborn developments are anatomized in order to help online food delivery services develop better strategies to ameliorate deals and increase the client base. Keywords--- Food Delivery, Perception, Consuming Process, Food Technology

Novel online routing algorithms for smart people‐parcel taxi sharing services

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

Evaluasi Strategi E-Commerce sebagai Upaya Penguatan Ekonomi pada Himpunan Pengusaha Pribumi Indonesia

Digitalization is an important need, during the pandemic there is an increase in consumer demand for e-commerce, digital literacy, and delivery requests. This is felt in the Micro, Small and Medium Enterprises of food and beverages through the increasing demand for take away, delivery, and e-commerce sales. The understanding of Micro, Small and Medium Enterprises actors on technology, limited online marketing, production processes, and online market access are still considered not optimal enough. On the other hand, consumers feel insecure in conducting digital transactions. The production sector must foster awareness and build a framework of thinking on business strategies in e-commerce, namely producing competitive products, targeted promotions and creativity in product delivery, while management must provide knowledge of e-commerce law. , regarding trademark protection, electronic payments, obligations and responsibilities of e-commerce actors. The conclusion that the authors get is that the evaluation of the strategy of the Micro, Small and Medium Enterprises of the Indonesian Indigenous Entrepreneurs Association in e-commerce is that there is no proper e-commerce business strategy for Micro, Small, and Medium Enterprises which results in problems or obstacles in supporting business digitization. Micro, Small, and Medium.

VeRoViz: A Vehicle Routing Visualization Toolkit

VeRoViz is an open-source vehicle routing visualization package consisting of both Python and web-based components. It was developed to streamline the workflow for vehicle routing researchers by simplifying and automating many of the tedious tasks associated with generating realistic test problems. VeRoViz also provides new functionality to produce customizable visualizations of complex vehicle routing problems. These visualization tools—including Gantt charts, static maps, and dynamic 3-D videos—assist researchers in validating models and communicating results. Additionally, a comprehensive collection of utility functions within VeRoViz provides researchers with useful tools to assess features of their problems. This paper provides an overview of VeRoViz and highlights the flexibility and ease-of-use of the toolkit. Summary of Contribution: This paper describes an open-source software package designed to assist vehicle routing researchers. The software package, named VeRoViz (vehicle routing visualization), streamlines the process of collecting and visualizing data relevant to vehicle routing research. This includes capturing road network travel times (and distances) between locations, displaying turn-by-turn vehicle routes, generating Gantt charts of vehicle assignments, and producing 3-D “movies” of vehicle routing solutions. The VeRoViz suite consists of three components. First, a web-based interface allows researchers (and instructors) to quickly “sketch” elements of a vehicle routing problem, providing visual representations of nodes and arcs on a map. Second, the VeRoViz Python package provides an extensive collection of functions that assist operations researchers in collecting data and displaying solutions. Finally, an HTML/JavaScript plugin is available for displaying time-dynamic 3-D visualization of vehicle routes. VeRoViz is not a vehicle routing solver; it is a software suite that complements and supports researchers who are developing solution approaches to increasingly realistic vehicle routing problems (e.g., problems related to drone delivery, electric vehicles, or dynamic delivery requests).

Analisis Determinan Permintaan Masyarakat Kota Padang Terhadap Jasa Pengiriman Paket Dikala Pandemi

This research is intended to interpret and explain the effect of income, price, timeliness of delivery, and quality of service on delivery requests at PT. Pos Indonesia (Persero) Padang during the pandemic. This research is a descriptive and inductive research, the data used are primary data and analyzed using Multiple Linear Regression. The estimation results show that revenue, price, on time delivery and service quality have a positive influence on the demand for package delivery at PT. Pos Padang during the pandemic.

A mixed truck and robot delivery approach for the daily supply of customers

Innovative last-mile logistics solutions are needed to reduce delivery costs, traffic congestion, and pollution in cities. A promising concept in this context are truck-and-robot systems with robot depots, as they enable significant cost and traffic reduction compared to classic truck deliveries. The system relies on small autonomous delivery robots to cover the last meters to a customer. Existing concepts consider home deliveries by robots, while trucks are only used to transport robots and not for deliveries. This assumption disregards the fact that regular truck deliveries are still needed for some delivery requests, such as for the delivery of bulky items, or for customers who do not accept robots.Our research addresses this issue and proposes the first mixed truck and robot delivery concept with robot depots in which both robots and the delivery truck can visit customers. Our tailored solution approach is based on a General Variable Neighborhood Search that efficiently solves the routing problem and outperforms existing truck-and-robot routing algorithms (reducing the runtime by 37 to 94% in experiments). The numerical experiments show that this approach enables cost reductions of up to 43% compared to classical truck deliveries and up to 22% compared to a truck-and-robot system that does not allow deliveries by both truck and robots on the same tour. Further analyses reveal additional benefits of such mixed tours and the robustness of our approach for different problem settings.

Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems

We study the electric vehicle (EV) relocation problem that arises in the practice of one-way carsharing systems. In this problem, users are allowed to pick up an EV from one parking station and return it to another station. To maintain a spatially balanced distribution of vehicles, the system creates a set of pickup and delivery requests of EVs, which are to be served by a team of workers. The objective of this problem is to find a set of routes for workers to fulfill EV pickup and delivery requests, such that the total operational profit is maximized. The battery level of EV and the working duration limit for workers make the problem complicated and challenging. We formulate a set-packing model for this problem, and design a branch-and-cut-and-price algorithm to solve it. Particularly, we design a bi-directional label-setting algorithm to deal with the pricing subproblem, and implement two acceleration techniques, namely, decremental space relaxation and heuristic column generator. We also consider another objective of minimizing the total route duration for this problem, and the proposed algorithm still works with some adaptations applied. On a set of randomly generated instances, we conduct extensive computational experiments, and the results demonstrate the effectiveness of the proposed algorithm. Besides, the impacts of instance parameters and the efficiency of acceleration techniques have been analyzed.

Reconsideration of the Bill of Lading as a Document of Title

The bill of lading is a primary transport document generally signed by or on behalf of the carrier by sea. Because of its comprehensive characteristics, it also plays an important role in the field of international trade. It is widely treated as a document of title in maritime law research and legal practice, but as to the specific meaning of the document of title, there is no accurate and universally accepted interpretation. It is an academic topic left to scholars to discuss. With the changes in the form of bills of lading in practice, it is also a fundamental issue to reduce the disputes arising from the delivery of goods and payment settlement in trade. This paper reviews the different expressions and variations of the bill of lading in relevant conventions and different domestic laws and analyzing the provisions or presumed meanings of the document of title. Based on the various functions of the bill of lading in transportation and trade, it further emphasizes that the dual legal nature of the document of title is mainly for delivery requests and constructive possession of goods. The correction of deviation regarding the implication of document of title in the Chinese context, especially the controversy about the bill of lading as a document of property and the document of debt, and the further clarification of legal nature of a different bill of lading in particular application stage could be obtained.

Vehicle Routing with Stochastic Supply of Crowd Vehicles and Time Windows

The growth of e-commerce has increased demand for last-mile deliveries, increasing the level of congestion in the existing transportation infrastructure in urban areas. Crowdsourcing deliveries can provide the additional capacity needed to meet the growing demand in a cost-effective way. We introduce a setting where a crowd-shipping platform sells heterogeneous products of different sizes from a central depot. Items sold vary from groceries to electronics. Some items must be delivered within a time window, whereas others need a customer signature. Furthermore, customer presence is not guaranteed, and some deliveries may need to be returned to the depot. Delivery requests are fulfilled by a fleet of professional drivers and a pool of crowd drivers. We present a crowd-shipping platform that standardizes crowd drivers’ capacities and compensates them to return undelivered packages back to the depot. We formulate a two-stage stochastic model, and we propose a branch and price algorithm to solve the problem exactly and a column generation heuristic to solve larger problems quickly. We further develop an analytical method to calculate upper bounds on the supply of vehicles and an innovative cohesive pricing problem to generate columns for the pool of crowd drivers. Computational experiments are carried out on modified Solomon instances with a pool of 100 crowd vehicles. The branch and price algorithm is able to solve instances of up to 100 customers. We show that the value of the stochastic solution can be as high as 18% when compared with the solution obtained from a deterministic simplification of the model. Significant cost reductions of up to 28% are achieved by implementing crowd drivers with low compensations or higher capacities. Finally, we evaluate what happens when crowd drivers are given the autonomy to select routes based on rational and irrational behavior. There is no cost increase when crowd drivers are rational and select routes that have a higher compensation first. However, when crowd drivers are irrational and select routes randomly, the cost can increase up to 4.2% for some instances.