Order Cancellation Research Articles

Frequency-based zonal flexible bus design considering order cancellation

Order cancellation is ubiquitous in on-demand mobility pre-booking systems, which affects the pricing and operational strategies of on-demand mobility services. This paper investigates the order cancellation behaviour in a frequency-based flexible bus (FB) system, which provides customized door-to-door service, considering stochastic elastic demand volume and stochastic spatial demand distribution. A two-stage stochastic model that simultaneously optimises the FB service price, FB dispatching, and passenger assignment is developed while capturing the effect of pre-booked order cancellation probability. This cancellation probability is calculated based on the pricing and availability of street-hailing taxi and FB services. The availability of FBs is derived from the underlying stochastic demand and order assignment process, which makes the model nonlinear. To solve the mixed integer nonlinear model, a two-phase decomposition scheme is employed, and then approximation techniques are implemented to make each decomposed problem solvable by commercial solvers. Finally, a gradient decent solution algorithm is applied. Numerical studies are conducted to show the necessity of capturing the impact of order cancellation and illustrate the influence of cancellation penalty, taxi price, and zone division. Finally, a case study with Chengdu data is implemented to examine the performance of our solution algorithm for large-scale optimisation.

Two-Phase Model for Demand-Responsive Transit Considering the Cancellation Behavior of Boundedly Rational Passengers

The resurgence of demand-responsive transit (DRT) has been pushed toward a more sharable and user-friendly mobility service to reshape the urban mobility ecosystem. Nevertheless, to fulfill the swiftly growing diversified and personalized demand, how to depict the cancellation behavior of passengers remains an often overlooked but extremely significant challenge. This paper attempts to discuss the cancellation behavior in the two-phase optimization process. With regard to the unconfirmed reservation cancellation, bounded rationality is incorporated to portray the decision-making process of passengers. Bernoulli random variable is utilized to discuss the stochastic possibility of order cancellation. Simultaneously, a two-phase biobjective model considering the cancellation behavior is constructed to balance the inherent trade-off between passengers and operators. In addition, the algorithm based on multidirectional local search is constructed to achieve the Pareto front for the proposed model. Small-scale experiments in the Nguyen-Dupuis network are illustrated to validate the effectiveness of the algorithm. Subsequently, the Beijing case is further exemplified to evaluate the performance of DRT service considering the cancellation behavior. A reasonable penalty mechanism contributes to ensuring operating profit while substantially diminishing the penalty cost. Therefore, this paper facilitates not only capturing the cancellation behavior of passengers but also developing a more flexible and cost-effective DRT service.

Optimal cancellation penalty for competing ride-sourcing platforms under waiting time uncertainty

This paper investigates the optimal fare and penalty strategy of ride-sourcing platforms in a competing market. When ordering ride-sourcing services, passengers encounter waiting time variability due to the uncertainty in the matching and pickup processes. In some scenarios, some passengers order ride-sourcing services from multiple platforms to save waiting time, which results in subsequent order cancellations. To capture the behavior of order cancellation, this paper derives the cancellation probability based on the perceived inhomogeneous Poisson process. In view of the punishing effect and blocking effect of order cancellation penalty, this paper investigates the impact of order cancellation penalties on market equilibrium and platform revenue. We formulate a game-theoretic framework and obtain the duopoly market equilibrium to explore the optimal pricing (including penalties and fares) of different platforms. By incorporating passenger perceptions into the order cancellation behavior, this study produces insightful pricing schemes for the ride-sourcing platforms.

Md-Pred: A Multidimensional Hybrid Prediction Model Based on Machine Learning for Hotel Booking Cancellation Prediction

Hotel order cancellation prediction has always been an influential part of hotel management. A better prediction model can optimize the accuracy of the prediction and thus enhance the value of subsequent business analysis and operational optimization. In this paper, a multidimensional hybrid evaluation prediction model Md-Pred is proposed for the first time. It combines the CatBoost, LGBM classifier, and SARIMAX time series algorithm, which can more effectively balance the influence of various features on classification problems as well as differentiate between objective features and subjective features. Results indicate that the performance of the prototype is significant, a new level of accuracy in predicting hotel order cancellations and future guest flow has been achieved.

고객만족 향상을 위한 배달음식의 서비스품질 중요도 및 수행도 분석

This study analyzes the importance and performance and investigates the effect of delivery food service quality on customer satisfaction. The subjects enrolled in this study were adults in their 20s to 50s living in Seoul city who had used food delivery service applications within the last two weeks. A convenience sampling method was conducted, and data were collected via an online survey from August 10 to August 11, 2022. A total of 250 responses were collected from customers. Our study revealed that the delivery food service quality consisted of 5 factors and 16 items. Based on the IPA analysis of delivery food service quality, items that were prioritized for improvement included ‘kitchen hygiene’, ‘sanitary cooking’, ‘safe and clean delivery food’, ‘quick customer complaint handling’, and ‘easy order cancellation and refund processing’. Analyzing the effect of delivery food service quality on customer satisfaction revealed that ‘food’ (p0.05), ‘delivery’ (p0.05), ‘order’ (p0.001), and ‘compensation’ (p0.01) were factors that significantly affected customer satisfaction; however, ‘safety’ had no significant impact on customer satisfaction.

Will the Order Be Canceled? Order Cancellation Probability Prediction Based on Deep Residual Model

Online ride-hailing is gradually becoming one of the indispensable travel modes for people. Online ride-hailing platforms receive tens of thousands of taxi travel orders every day. However, some orders are canceled by the user after the driver answers. User cancellation of orders will disrupt the regular operation of the taxi platform, reduce the efficiency and income of drivers on the online ride-hailing platform, and affect the ride experience of other users. To capture the relationship between the cancellation probability of online taxi orders and users, we first analyze the factors that affect users’ cancellation of online taxi orders. We find that the cancellation probability of online ride-hailing orders is highly correlated with the travel time of the online taxi, the distance between vehicle and passengers, road congestion conditions, and alternative transportation resources around the passengers. Second, a deep residual network-based ride-hailing order cancellation probability prediction model (DeepOCP) is designed to predict the probability of a user canceling an answered online-hailing order. Finally, the Didi Chengdu Express public data set is used to test the model’s effectiveness. This paper is the first study to use a deep-learning model to predict the cancellation probability of online ride-hailing orders.

Engine Order Cancelation in a super sports car cabin

Active noise control (ANC) has been used in several systems to improve acoustic performance while avoiding increases in the size and weight required by passive noise control solutions. This paper considers an ANC application aimed at controlling the sound field in the cabin of a supersports car with a naturally aspirated engine. This work studies the performance of a system that employs microphones and loudspeakers in the car cabin and uses a reference signal obtained from CANbus data. The controller exploits a multichannel, multi-order Fx-LMS algorithm to control the sound only in the region surrounding the driver's ears. Due to the nature of the car, this ANC application provides an interesting and challenging case study due to the rapidly changing acoustic excitation provided by the engine. The paper therefore describes a system for noise reduction, focusing on engine order reduction, aiming at achieving control during the most representative manoeuvre known as the "Wide-Open Throttle" (WOT). The investigated controller - acting locally and only on some orders simultaneously - shows good sound attenuation over the entire spectrum, with peaks of reduction exceeding 10 dB, and achieving a 5% overall decrease in the loudness, which is calculated according to ISO 532-1.

Assessing strategies to mitigate the impacts of a pandemic in apparel supply chains

The COVID-19 pandemic has taught global businesses that a pandemic can put business dynamics in unforeseeable turbulence. The disruptions created by the pandemic in the apparel industry exposed the vulnerabilities of apparel supply chains (SCs). To recover the supply chain impacts (SCIs) during an unprecedented event such as the COVID-19 pandemic, apparel SCs need a robust framework that can identify, measure, and mitigate the severity of SCIs by assessing effective mitigation strategies. This study identifies 12 critical SCIs in apparel SCs during a pandemic and 17 mitigation strategies. To assess SCIs and mitigation strategies, a modified grey-based bi-level analytical network process (ANP) is proposed to deal with the complex relationship between the SCIs and mitigation strategies. A real-life case study is conducted from an apparel supply chain for validation purposes. The findings suggest that policymakers in apparel SCs should prioritize implementing government policies and financial aid to deal with increased material and operational costs, the sudden surge in the unemployment rate, cancellation of orders and delayed payment, and increased transportation costs during a pandemic. This study also contributes to the literature by providing a robust decision-making framework for practitioners to deal with the complexity of SCs during future pandemics.

Value‐added service decision and coordination under fresh produce e‐commerce considering order cancelation

AbstractThis paper proposes an analytic model under fresh produce e‐commerce considering value‐added service and order cancelation behavior. We first examine the optimal prices, value‐added service decisions, and profits in the centralized and decentralized channels. Then, the revenue‐cost‐sharing contract is designed to eliminate the double marginal effect in the decentralized channel. The main findings are threefold. First, with the improvement of product quality, the optimal value‐added service may increase and then decrease in both centralized and decentralized channels. Second, compared with the centralized channel, the decentralized channel prefers a lower retail price only when both salvage value coefficient and product quality are high. Besides, the proposed revenue‐cost‐sharing contract performs well in improving the value‐added service and profits in the decentralized channel.

Pre-sale ordering strategy based on the new retail context considering bounded consumer rationality

<p style='text-indent:20px;'>The purpose of this paper is to study the impact of bounded consumer rationality on the order quantity and profitability of the seller in the advance period and the spot period in the context of the combination of new retail and pre-sale. In this paper, we develop a seller order model in the context of the combination of new retail and pre-sale, with and without reference price dependence. Besides, the model considers the order cancellation and delayed purchase behavior of consumers. We then discuss the optimal profit and optimal order quantity under different conditions and the effect of different reference price dependence and value-added offline service on them. Our research shows that: First, the seller tends to set the deposit too low in pre-sales. Second, reference price dependence has different effects on order quantities in different periods. The seller should pay more attention to the impact of reference price dependence. Third, on the whole, consumer rationality benefits the seller. The seller, or the public policymaker, can benefit new retail businesses by increasing consumer rationality. Last, in the new retail context, an increase in offline service value-added, even if it increases total order quantity, is not always beneficial to the seller and may reduce profits. Therefore, the seller should weigh all factors to determine the optimal value-added offline services.</p>

Optimal ordering and pricing decision for items following price sensitive quadratic demand under combined payment scheme

Three types of payments confront in business dealings viz. advance, cash and credit. A cash payment is ubiquitously practiced, while a credit payment scheme escalates sales and advance payment technique is implemented to avoid cancellation of orders. These are recognized as advance-cash-credit (ACC) payment scheme which is conventional in business transactions. By implicating ACC the aim is to determine optimal pricing and ordering policies. Inventory items observes quadratic demand being sensitive to unit selling price. Promising concavity of the profit function for both selling price and cycle time is posed numerically. Managerial insights are revealed as a concluding note.

Order Cancellation Behavior in Online Retailing: An Empirical Investigation

Order Cancellation Behavior in Online Retailing: An Empirical Investigation

Order Cancellation Law in a Semigroup of Closed Convex Sets

In this paper we generalize Robinson's version of an order cancellation law in which some unbounded subsets of a vector space are cancellative elements. We introduce the notion of weakly narrow sets in normed spaces, study their properties and prove the order cancellation law where the canceled set is weakly narrow. Also, we prove the order cancellation law for closed convex subsets of topological vector space where the canceled set has bounded Hausdorff-like distance from its recession cone. We topologically embed the semigroup of closed convex sets sharing a recession cone having bounded Hausdorff-like distance from it into a topological vector space. This result extends Bielawski and Tabor's generalization of Rådström theorem.

Pre-holiday limit order cancellation of individual and institutional investors

This paper shows that the time-to-cancellation is longer on trading days immediately before holidays. Pre-holiday time-to-cancellation is significantly higher for individual investors but not for institutional investors. Moreover, individual investors have a lower execution ratio and underperform institutional investors by a larger margin on days before holidays. These findings are consistent with the notion that pre-holiday inattention entails a higher opportunity cost for monitoring the market, and limit orders are more subject to the non-execution and pick-off risks on pre-holiday trading days.

Hospitality order cancellation prediction from a profit-driven perspective

Purpose This study/paper aims to reach the core objective of hospitality order cancellation prediction (HOCP), that is, to identify potential cancellers from many customer bases, thereby enhancing the effectiveness of customer retention campaigns. However, few studies have focused on predicting hospitality order cancellation. Design/methodology/approach A novel profit-driven model for predicting hospitality order cancellation is proposed to bridge this research gap. The authors construct profit-driven extreme gradient boosting (XGBoost) based on a grid search on HOCP to maximize profit by selecting optimal hyperparameters of XGBoost. Findings Real-world data set is analyzed, and the proposed model yields more profits than other predictive models. Sensitivity analysis proves that the proposed model is robust to the key hyperparameter and application scenario. Furthermore, some preventive measures based on visual analysis results are provided to reduce the cancelled probability of orders. Research limitations/implications This research will help hotel managers to transfer the modeling goal to profit orientation and encourage relevant researchers to interpret the prediction results of models for hotel order cancellation prediction in a post hoc manner. Besides, the proposed model can be applied to various enterprises with different average order profits and help managers optimize revenue management. Originality/value This research expands the relevant literature and offers guidance for predicting hospitality order cancellation from a profit-driven perspective at the customer level. The proposed model can provide macro-control to hotel managers and obtain the most satisfactory profits in micro-control.

О совершенствовании нормативных требований к конструкциям зданий и сооружений на взрывопожароопасных производственных объектах

The general approach to the industrial safety of capital construction projects in Russia, the EU countries, the USA is provided based on an analysis of the requirements in terms of explosion-proof design of buildings and structures. The analysis was carried out in order to determine the requirements for the construction of buildings and structures requiring revision or cancellation in order to harmonize Russian regulatory technical documents in construction and industrial safety with the foreign ones. The relevance of the chosen topic is determined by its compliance with the main goals and objectives of the state policy of Russia in the field of industrial safety, aimed at consistently reducing the risk of accidents at industrial facilities and minimizing their negative consequences. The list of the main current regulatory and technical documents of the EU countries and the USA with the main requirements for the explosion resistance of buildings and structures and the assessment of explosive impact is presented. The methodology for calculating the explosive loads on buildings and structures is briefly described. It is shown how the response of a structure to the impact of an explosion is determined within the framework of a foreign approach. The main proposals for the creation of new requirements and updating previously approved Russian codes of practice in construction have been developed and compiled. The results of the implementation of the submitted proposals will contribute to improving the efficiency of the processes of domestic industrial construction of buildings and structures, updating the regulatory and technical base, ensuring the safety of hazardous production facilities. Also, it will maintaining the compliance of buildings and structures with their functional purpose, reducing the risk of harm to health, property of individuals or legal entities, public or municipal property and the natural environment.

High-Frequency Trading Strategies

We examine the effect of high-frequency trading on market quality from the perspective of a limit order trader. By competing with slower limit order traders, high-frequency traders impose a welfare externality by selectively crowding out the most profitable limit orders. The order book imbalance immediately before each order submission, cancellation, and trade suggests that high-frequency traders strategically use limit order book information to supply liquidity on the thick side of the order book and demand liquidity from the thin side. This strategic behavior is more pronounced during volatile periods and when trading speeds increase. This paper was accepted by Bruno Biais, finance. Funding: This work was supported by the Centre for International Finance and Regulation [Grant T013]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4539 .

Improving the breast MRI process: A longitudinal QI project at the Houston VA.

371 Background: The number of female veterans is increasing by more than 18,000 per year, and 700 veterans enrolled in VA healthcare are diagnosed with breast cancer each year. The American Cancer Society recommends screening MRI in women with 20-25% or greater lifetime risk of breast cancer, and primary care physicians and oncologists order breast MRIs regularly. The Michael E. DeBakey Veteran’s Affairs Medical Center (MEDVAMC) performs mammograms and ultrasounds on site, but breast MRI is not currently available. Care in the Community arranges for veterans to obtain these studies at outside facilities. This is a complex process, requiring coordination among multiple departments at the MEDVAMC, outside MRI facilities, and patients. Methods: We created a comprehensive process map for the current breast MRI ordering process to determine areas for improvement. We reviewed all breast MRI orders from 1/2019 to 5/2021 at the MEDVAMC and determined the cancellation rate and the rate of benign (BIRADS 1-2) vs non benign (BIRADS 0, 3-5) screening outcomes. We created a Pareto analysis of cancellation reasons. Our process measures were percentage of placed breast MRI orders that were cancelled and delay from MRI order to MRI performed. Our outcome measures were percentage of order cancellations due to incorrect or incomplete orders and delay from MRI order placement to results uploaded. Results: Of the 434 orders that were placed for breast MRIs, 64% were cancelled. Only 117 of the 167 patients that had MRIs ordered ultimately received one. Of the 104 patients without known malignancy who competed an MRI, 45% had non-benign findings requiring follow-up. Our pareto analysis showed that the top cancellation reason was incorrect orders, usually contrast or side verbiage. In June 2021, the CITC order set for MRI was changed from a free text box to discrete, clickable options for side and contrast. In December 2021, we held educational interventions on how to avoid delay or cancellation. Our p-chart of percentage of cancellations due to incorrect ordering shows an extremely promising decrease, with the last seven consecutive subgroups of 15 patients below the mean. The order cancellation percentage is also decreasing. The average delay pre-intervention from MRI order placement to results uploaded was 67 days. In January 2022, we implemented a breast MRI nurse navigator to help acquire results and expedite scheduling. Post-intervention, the average delay has decreased to 38.5 days. Using XmR and S charts, we have also shown an increase in precision. Conclusions: Providing discrete, clickable options within the MRI order set reduced the number of cancellations due to incorrect verbiage. Early data suggests that the educational intervention improved the cancellation rate. Instituting a nurse navigator decreased both the delay and variability for the time from order placement to results received, which will reduce the delay to follow-up of non-benign findings.

Predicting Order Cancellations for E-Commerce Domain: A Proposed Model Based on Retailing Experience

E-Commerce technologies enable contact between businesses and their suppliers for the aim of exchanging information such as purchase orders, invoices, and payments thank to the rapid development in information technologies. E-Commerce has become a particularly important concept and has revolutionized the retail space. Understanding customer behavior patterns is key to gaining competitive advantage and achieving business goals. Predicting the probability of order cancellations has become a very urgent need as it causes loss of revenue for the retailer. When dealing with day-to-day operations such as order processing, tracking and order cancellations, finding enough time to grow the business is difficult. Cancellations are an important aspect of retail industry revenue management. In fact, little is known about the factors that cause customers to cancel or how to avoid them. The aim of this study is to propose a model that predicts the tendency to cancel an order and the parameters that affect the cancellation of the order. This solution can identify key factors that cause orders to be canceled by analyzing historical transaction data. A custom modeling application has been created that helps automate the process of tracking order cancellations in real time and predict the probability of an order being cancelled. For this purpose, machine learning techniques (ML) such as Artificial Neural Network, Support Vector Machine, Linear and Logistic Regression, XGBoost, Random Forest are applied to provide a tool for predicting order cancellations. The Random Forest algorithm achieves the best performance with 86% accuracy and 88% F1-Score compared to the other algorithm. This work will help firms manage their inventories well and strengthen their actions regarding customer behavior.

A Study on Influencing Factors of Consumers Satisfaction Level towards E-Shopping in Gujarat State

Day by day the trend of the shopping is diversified physical shopping to E-Shopping. It is a very popular now a days and it is helpful to shop quickly, safely and conveniently. E-Shopping is a future of the traditional shopping trend. This way has no Brand, Product boundaries. That’s why the consumers are shop directly without confusion.
 For this study the primary data has been collected from 100 respondents of Gujarat state with the help of structured questionnaire. The identify the influencing factors of consumers satisfaction level towards E-Shopping. Data are analyzed by various techniques like factor analysis and Reliability test and the result shows Factor 1 namely as product and payment which includes EMI scheme and cancellation of order has highest impact in factor analysis. Factor 2 namely as quality which includes no compromise on product quality has highest impact in factor analysis. Factor 3 namely as safety which includes various brand of products, safe packaging has highest impact in factor analysis. Factor 4 namely as security which includes secured as traditional shopping, product advertisement has highest impact in factor analysis. Factor 5 namely as website layout which includes company’s website layout has highest impact in factor analysis and last factor namely as availability of product which includes availability of various brands and products has highest impact in factor analysis.