Delivery Quantity Research Articles

Daily Diet and Temporal Variation of Swainson's Hawk Prey Deliveries

ABSTRACT The quantity and timing of prey delivery affect the development of altricial young, can induce trade-offs in parental behavior, and ultimately influence fitness of the young. We investigated the dynamics of prey deliveries in a population of Swainson’s Hawks (Buteo swainsoni) in northern California to better understand its temporal component. We installed 12 motion-triggered cameras in nests between 2013 and 2018, and documented 466 unique prey deliveries. Of those, 455 prey items were identifiable to at least class. Mammals made up the majority of prey in terms of number of deliveries (88.2%) and estimated biomass delivered to the nest (97.1%). Of those, pocket gophers (Thomomys spp.) and voles (Microtus spp.) together made up almost half of the deliveries and biomass. Most prey was delivered to the nest in midday (i.e., 1000–1459 H), suggesting these are important foraging hours for this population. However, afternoon air temperatures in the study area can be high and may require that nestlings be shaded, setting up a potential trade-off between foraging and protecting the nestlings’ thermal homeostasis. Our study provides initial insight into the daily temporal dynamics of prey delivery for one of the latest-breeding raptors in North America.

Optimizing omnichannel retailer inventory replenishment using vehicle capacity-sharing with demand uncertainties and service level requirements

This study explores an inventory replenishment problem for an omnichannel retailer selling a product with demand uncertainties and service level requirements in different channels through a capacity-sharing strategy. The omnichannel retailer allows customers to order products online and then pick them up in retail stores. The capacity-sharing strategy is considered to reduce travel costs when the omnichannel retailer delivers products in different channels. With the capacity-sharing strategy, some products can be transferred from the online delivery vehicle to the retail store delivery vehicle. The expectation and the bounds on the online route travel cost of the capacity-sharing strategy are determined, based on which the capacity-sharing rules are established. Optimization models and a solution procedure are developed to solve the omnichannel retailer inventory replenishment problem. To ensure adequate performance, service level requirements are considered when making delivery decisions. Numerical experiments using randomly generated and artificial instances are performed to verify the effectiveness and practicality of the proposed optimization models and the solution procedure. The results show that the proposed models and solution procedure can effectively reduce travel costs, and can provide effective schedule and delivery quantity decision support for omnichannel retailers.

Optimisation of Fertiliser Delivery Distribution from Distributor to Farmer Group Using Excel Solver in Tanjung Village

Subsidised fertilisers are declared as goods that are monitored for distribution with a specific target, namely farmers. In the distribution process, the government cooperates with various components including the Ministry of Industry and Trade, the Ministry of Agriculture, the Ministry of State-Owned Enterprises and the Ministry of Home Affairs. At the same time, transportation and communication technologies continue to develop rapidly, such as mobile communications and the Internet, which drive the continuous development of the supply chain and technologies related to its regulatory management. This research aims to find out how to optimise the fertiliser transprortation Fertiliser Delivery Distribution and a series of fertiliser supply systems from Distributors to Farmer Groups Using Excel Solvers in Tanjung Village so as to provide better user control. The benefits of this research for distributors can help buffer distributor management to determine the quantity and time of delivery of subsidised fertiliser to official kiosks. The research method uses a case study which is a method of collecting and processing data through review / study of various research reports, flowsheets, research journals, as well as books and other relevant literature.

The generator distribution problem for base stations during emergency power outage: A branch-and-price-and-cut approach

Motivated by the need for uninterrupted service provision in the telecommunications industry, this paper presents a novel problem concerning the transportation of diesel generators during an unplanned power outage. Given a set of base stations, each equipped with a capacitated back-up battery pack, the problem consists in finding an optimal delivery and pick-up schedule that minimises corresponding costs. The problem is significantly complicated by the fact that the delivery quantity is a decision variable, and the pick-up times depend on the delivery time, delivery quantity and constraints related to battery-charging requirements. To address the issue, we develop problem-specific pick-up time functions that can be effectively incorporated into the proposed algorithm. The problem is formulated as an arc-based model and a set-packing model. To solve it to optimality, we design a tailored branch-and-price-and-cut algorithm, including novel customised dominance criteria in the labelling algorithm and adopting heuristics that exploit the problem features and effectively improve the algorithmic performance. The efficacy of the proposed algorithm is validated based on the extensive computational study and compared with a commercial solver. The proposed acceleration techniques are found to be potent with regard to computational times, and useful managerial insights are provided as part of sensitivity analyses.

A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios.

Introduction of Unit-Dose Care in the 1,125 Bed Teaching Hospital: Practical Experience and Time Saving on Wards.

The shortage of nursing staff as well as the slow progress in the German health care system's digitalisation has gained much attention due to COVID-19. Patient-specific medication management using the unit-dose dispensing system (UDDS) has the potential for a lasting and positive influence on both digitalisation and the relief of nursing staff. Nursing staff UDDS-acceptance was determined via a validated online survey. For the evaluation of stock keeping on the wards, the delivery quantities were determined for a comparative period before and after the introduction of the UDDS. The time required for on-ward medication-related processes on ward before and after the introduction of UDDS was recorded based on a survey form and the nursing relief in full-time equivalent (FTE) was calculated using the data obtained. We show that nurses appreciate the UDDS and confirm a significant reduction in drug stocks on the wards. The UDDS reduces the time needed to dispense medications from 4.52 ± 0.35 min to 1.67 ± 0.15 min/day/patient. In relation to the entire medication process, this corresponds to a reduction of 50% per day and per patient. Based on 40,000 patients/year and a supply of 1,125 beds with unit-dose blisters, 7.36 FTE nursing staff can be relieved per year. In contrast, 6.5 FTE in the hospital pharmacy are required for supplying the hospitals. UDDS is well accepted by nurses, reduces stock levels on ward, and fulfils criteria as a nursing-relief measure.

A cooperative optimization method for the layout of shared bicycle parking areas and delivery quantity

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%.

Dairy producers' colostrum practices, marketing, and attitudes toward male dairy calves

Optimal early-life care of surplus calves born on dairy farms is critical for health and welfare. This cross-sectional study aimed to describe the marketing practices of male dairy calves, differences in the colostrum management between male and female calves on dairy farms, and the relationship between discrepant colostrum practices and dairy producers' attitudes toward male calf care. United States dairy producers (n = 1,000) in the states of Florida, Michigan, Ohio, Vermont, and Wisconsin were selected using stratified random sampling. A questionnaire containing questions about farm demographics, colostrum management (including quantity and timeliness of colostrum delivery), and producers' attitudes toward male calf care was mailed in February 2021. Attitudes toward male calf care were assessed using 5-point Likert scales for 10 statements regarding perceptions of the value of male calves and barriers in providing optimal care. Producer responses to questions about the quantity and timeliness of colostrum delivery between male and female calves were compared using a nonparametric Wilcoxon rank-sum test. Multivariable logistic regression models were used to investigate the association between differences in colostrum management and producers' attitudes. By May 2021, 953 surveys were delivered and 315 (33.1%) were returned with complete responses. Most producers (>90%) reported feeding equal volumes of colostrum and performing the first postbirth feeding within a similar time frame for male and female calves. However, compared with females, male calves had a longer delay to the first colostrum feeding. Approximately 40% of producers marketed their male calves through auctions, and over half (54.6%) of farms sold the male calves between 3 and 10 d of age. Large farms (≥500 lactating cows) were found to market male calves at a younger age (≤3 d of age). Most producers (>78%) believed their male calves were receiving optimal care and did not consider the workload and financial costs as obstacles to providing good care to male calves. However, those who viewed the workload as an obstacle to good care and produced organic products were marginally more likely to feed a lower volume of colostrum to male calves in the first feeding after birth. These findings suggest that interventions to improve colostrum practices should be more broadly targeted to all calves born on dairy farms, and that age at transport is markedly different between large and small farms.

A Comparative Study on Relationship between Delivery Quantity to Total Quantity traded Ratio and Stock returns in Bajaj Auto Ltd. and Bharat Petroleum Corporation Ltd.

A Comparative Study on Relationship between Delivery Quantity to Total Quantity traded Ratio and Stock returns in Bajaj Auto Ltd. and Bharat Petroleum Corporation Ltd.

Development And Service Delivery in Post-Apartheid South Africa: “Quality” Versus “Quantities” of the Pre and Post-Apartheid South Africa

This paper intends to determine the contestation between the quality and quantity of service delivery in comparison between pre-apartheid and post-democratic South Africa. There are relative arguments that persist that quality and better service was provided in South Africa during the apartheid government era as compared to the current democratic system era. Contrary to these arguments, post-apartheid South African politicians boast about the service they are providing at the best quality and having closed the segregated service delivery system that was provided and practiced by the apartheid regime. This paper is conceptual, and it uses a desktop methodology to argue and compare the two perspectives in an attempt to find a truthful answer to it. This paper concludes that within the midst of the contestation of such arguments, the current beneficiaries perpetuating such scale of comparison are ignorant of the different contexts of eras of development and challenges and opportunities within which the two regimes operate.

Stochastic inventory routing with dynamic demands and intra-day depletion

Businesses such as supermarkets face an inventory routing problem with distinct stochastic dynamic demand distributions for various weekdays and times of the day. Most research, however, considers aggregated per-period demand and inventory depletion, thereby oversimplifying actual events. We formulate the problem as a finite-horizon stochastic dynamic program that accounts for the dynamic demand throughout the planning horizon and within each planning period. The models include different levels of information aggregation to decide the replenishment quantities and times by dividing periods into sub-periods with distinct demand distributions. This division helps the supplier create efficient replenishment plans and account for intra-day inventory levels. The modeled characteristics include routing, holding and stock-out costs, delivery time windows, inventory and vehicle capacities, and delivery time windows. The problem is solved using an iterative lookahead algorithm with an adaptive large neighborhood search for the routing and policy learning for the replenishment decisions. Numerical evaluations show the benefit of intra-day planning, effects of instance sizes, different coefficients of variation, and holding costs compared to benchmark replenishment policies. Considering intra-day depletion and demand data for each sub-period helps achieve savings of more than 20% compared to planning for full periods. The savings are obtained by adjusting delivery quantities, incorporating intra-day consumption, creating more efficient delivery routes, and correctly timing replenishments.

COVID-19 Response Efficacy with Biofarma's Cold Chain Vaccine Distribution System in Indonesia: An Analysis of Technology and Policy Synergy

This study delves into the synergy between technology and policy in Biofarma's Cold Chain Vaccine Distribution System in Indonesia amid the pandemic. This research aims to identify and quantify the tangible and intangible benefits of technology-policy integration, assess the system's influence on the timeliness and quantity of vaccine delivery, and evaluate the maintenance of vaccine quality across Indonesia's diverse regions. Utilizing a qualitative methodology, the study conducted extensive interviews with three representatives from different Indonesian provinces to uncover the impacts of technology and policy integration on vaccine distribution. The results spotlight several key patterns those are 1) Technological Backbone: Data Integration & Distribution: Automation has significantly transformed coordination and precision in vaccine distribution. 2) Operational Revolution: Efficiency & Adaptability: The system has notably enhanced operational efficiency and adaptability. 3) Regional Nuances: System Challenges & Refinements: Identifying unique challenges across various regions shed light on factors impacting the efficacy of the distribution system. 4) Vaccine Safeguarding: Real-time Monitoring & Inventory Transparency: Real-time monitoring and transparent inventory management ensure vaccine quality and reliability. 5)Horizon Scanning: Technological Advancements & Environmental Adaptations: Recommendations for technological advancements and proactive measures to refine vaccine distribution strategies have emerged. Refined and named patterns accurately represent the essence of the data, emphasizing the pivotal role of technology and policy integration in effective vaccine distribution amid the COVID-19 crisis. The study underscores the critical alignment of technology and policy to confront challenges and enhance vaccine distribution, impacting the ongoing response to COVID-19 and future global humanitarian operations.

Bayesian Neural Network-Based Demand Forecasting for Express Transportation

The rapid development of e-commerce in recent years has driven the growth of the logistics service industry, which in turn has led to a significant increase in express delivery volume. Predicting express delivery volume accurately and in advance can help companies allocate various resources reasonably and provide the basis for predicting express delivery demand. To predict the specific transport volume of XX Express Company's logistics routes on April 28th and 29th, 2023, this article builds two Bayesian prediction models based on the company's historical transportation data as the training set, one for predicting the opening of logistics routes and the other for predicting express delivery quantity. This approach reduces the impact of inaccurate predictions due to logistics routes not being opened properly and improves the accuracy of the prediction model. It provides decision support for optimizing resource layout for express delivery companies.

Implications of The Rules of Al-Masyaqqah Tajlibu At-Taisir in The Contract of Greetings

This research discusses buying and selling greetings in the context of online trading activities in Indonesia. Buying and selling greetings is a sale and purchase contract allowed in Islam, where the buyer orders goods with agreed criteria and pays in cash when the contract is executed. In buying and selling greetings, there is no element of deception or gharar (uncertainty) involved. This research uses a phenomenological approach with qualitative descriptive research methods of literature. The data used is obtained from books, journals, and texts related to buying and selling online in the marketplace. The article also discusses the contract of salam in a sharia perspective such as the rule of al-Masyaqqah Tajlibu At-Taisir, namely difficulties will encourage convenience, such as transactions using online applications, where salam is the sale and purchase of goods mentioned in nature in dependents with payments made right away. The greeting agreement may be deferred until a certain time and payment can be made in cash. In the greeting contract, there is a delivery of goods at a later date with the price, specifications, quantity, quality, date, and place of delivery that has been agreed before.

A New Family of Route Formulations for Split Delivery Vehicle Routing Problems

We propose a new family of formulations with route-based variables for the split delivery vehicle routing problem with and without time windows. Each formulation in this family is characterized by the maximum number of different demand quantities that can be delivered to a customer during a vehicle visit. As opposed to previous formulations in the literature, the exact delivery quantities are not always explicitly known in this new family. The validity of these formulations is ensured by an exponential set of nonrobust constraints. Additionally, we explore a property of optimal solutions that enables us to determine a minimum delivery quantity based on customer demand and vehicle capacity, and this number is often greater than one. We use this property to reduce the number of possible delivery quantities in our formulations, improving the solution times of the computationally strongest formulation in the family. Furthermore, we propose new variants of nonrobust cutting planes that strengthen the formulations, namely limited-memory subset-row covering inequalities and limited-memory strong k-path inequalities. Finally, we develop a branch-cut-and-price (BCP) algorithm to solve our formulations enriched with the proposed valid inequalities, which resorts to state-of-the-art algorithmic enhancements. We show how to effectively manage the nonrobust cuts when solving the pricing problem that dynamically generates route variables. Numerical results indicate that our formulations and BCP algorithm establish new state-of-the-art results for the variant with time windows, as many benchmark instances with 50 and 100 customers are solved to optimality for the first time. Several instances of the variant without time windows are solved to proven optimality for the first time. Funding: This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 306033/2019-4, 313220/2020-4, and 314088/2021-0], the Région Nouvelle Aquitaine, France [Grant AAPR2020A-2020-8601810], the Agence Nationale de la Recherche [Grant ANR-20-CE40-0021-01], the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 13/07375-0, 16/01860-1, and 19/23596-2], and the Paraíba State Research Foundation [Grants 261/2020 and 041/2023]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0085 .

MLRS-RL: An Energy-Efficient Multilevel Routing Strategy Based on Reinforcement Learning in Multimodal UWSNs

In recent years, Multimodal Underwater Wireless Sensor Networks (M-UWSNs) have attracted widespread concern in academia. Due to the complex underwater communication environment and the increasing marine applications, it is a crucial issue for M-UWSNs to design an energy efficient routing strategy that can satisfy multiple transmission latency requirements of different marine applications. Reinforcement learning (RL) approaches with distributed dynamic optimization ability provide a prospective way to solve the aforementioned problem. Therefore, we propose an improved RL framework and then design an energy efficient multi-level routing strategy (MLRS-RL) based on this framework for multiple transmission latency requirements. In MLRS-RL, a method of model knowledge collection based on the time backoff principle is proposed to preliminarily learn the network environment information before network operates. The convergence speed of the RL framework can be accelerated by utilizing the model knowledge. Then, underwater nodes use the improved RL model to calculate the transmission rewards that data packets with different transmission latency requirements are sent to different candidate relay nodes. Finally, a cooperative transmission strategy using multiple relay nodes is designed to further improve the reliability of data transmission. We verify the effectiveness of the MLRS-RL strategy in terms of packet delivery ratio, transmission latency, energy efficiency, network lifetime, and delivery quantity.

Improvement of supplier delivery performance under uncertain capacity allocation and delivery time window

PurposeOriginal equipment manufacturers and other manufacturing companies rely on the delivery performance of their upstream suppliers to maintain a steady production process. However, supplier capacity uncertainty and delayed delivery often poses a major concern to manufacturers to carry out their production plan as per the desired schedules. The purpose of this paper is to develop a decision model that can improve the delivery performance of suppliers to minimise fluctuations in the supply quantity and the delivery time and thus maximising the performance of the supply chain.Design/methodology/approachThe authors studied a single manufacturer – single supplier supply chain considering supplier uncertain capacity allocation and uncertain time of delivery. Mathematical models are developed to capture expected profit of manufacturer and supplier under this uncertain allocation and delivery behaviour of supplier. A reward–penalty mechanism is proposed to minimise delivery quantity and time of delivery fluctuations from the supplier. Further, an order-fulfilment heuristic based on delivery probability is developed to modify the order quantity which can maximise the probability of a successful deliveries from the supplier.FindingsAnalytical results reveal that the proposed reward–penalty mechanism improves the supplier delivery consistency. This consistent delivery performance helps the manufacturer to maintain a steady production schedule and high market share. Modified ordering schedule developed using proposed probability-based heuristic improves the success probability of delivery from the supplier.Practical implicationsPractitioners can benefit from the findings of this study to comprehend how contracts and ordering policy can improve the supplier delivery performance in a manufacturing supply chain.Originality/valueThis paper improves the supplier delivery performance considering both the uncertain capacity allocation and uncertain time of delivery.

Research on express transport based on the ARIMA model

Based on the express transportation data of express companies, this paper makes an in-depth study of express transportation problems. First, the evaluation model to rank the importance of cities. Second, we predict the number of express deliveries between site cities with the time-series ARIMA model. Then, the random forest model of the CART decision tree was used to predict normal shipments and express delivery quantity. Next, the planning model is established based on the two parameters of railway transportation fixed cost and rated loading volume to plan the transportation scheme with the lowest cost of the express delivery company. Finally, the delivery requirements and model parameter estimates were estimated through the KDE model.

Effects of primary healthcare reform on routine health information systems (RHISs): a mixed-methods study in Lesotho

Background and objectiveThe Ministry of Health of Lesotho and Partners In Health piloted the Lesotho National Primary Health Care Reform (LPHCR) from July 2014 to June 2017 to improve quality...

A software framework for virtual testing of a production control system

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS.