Order Cancellation Research Articles

Ride-sourcing Service Pricing and Cancellation Penalty under Service Arrival Time Uncertainty

This study investigates ride-sourcing service pricing and order cancellation penalty in a duopoly market under service arrival time uncertainty. Order cancellation is wasteful to the service provider and society as a whole due to the incurred unproductive congestion and therefore, should be minimized either by penalty or regulation. Certain cancellations are inevitable, due to customer trip changes; others, however, arise from ordering from two service providers, necessitating the need for cancellation. We model the service arrival time perception via an inhomogeneous Poisson process and derive the cancellation behavior in a stochastic manner. A game-theoretic approach is then developed to formulate the competitive market equilibrium. For comparison, we also study the monopoly market as a benchmark. The monopoly market is found to be better in terms of order cancellations and service waiting times, but worse in trip fares for large markets. Based on these results, we propose possible regulatory interventions to reduce wasteful order cancellations.

A novel predictive-reactive rescheduling method for products assembly lines with optimal dynamic pegging

In this paper a new predictive-reactive rescheduling method is presented based on the dynamic pegging concept. Scheduling adjustments in a multi-level, mixed-model production system are invoked in response to the unplanned events, such as new order arrival, changes in due dates, and order cancellation. By using the dynamic pegging approach at the time of rescheduling, Work-In-Progress (WIP) can be reassigned to the existing or new orders. Based on our previous work on reactive-only rescheduling with dynamic pegging, a Mixed Integer Programming (MIP) model is developed for predictive-reactive scheduling of new orders of different product types that arrive following exponential distribution. A simple example is designed to compare the performance of predictive-reactive rescheduling and the reactive-only rescheduling by changing different input parameters to the MIP model. The results indicate that in certain scenarios implementing the predictive-reactive approach could be significantly more cost-efficient.

Penerapan Data Mining Untuk Memprediksi Ketersediaan Stok Produk HNI HPAI Menggunakan Algoritma C4.5

In a company, the availability of this stock must be considered to avoid product losses caused by expiration and also to avoid order cancellations. The data contained in the HPAI company application, called the HSIS application, can be utilized and applied for data mining in determining which products are in demand in everyday life. This HSIS application can be used by the Business Center (BC) and all Stock Agents in the HPAI HNI business. To maintain the availability of product stock, the purpose of this study is to test the Stock Agent using the C4.5 Algorithm to predict that the availability of products that must be maintained is in accordance with customer needs. The use of the C4.5 algorithm on stock agents can overcome stockpiling and finance or stock agents capital can be used to buy stock that must be available. The results of the analysis using the C4.5 algorithm stated that although the price of the product was expensive, it was still selling well as herbal products. So of the three types of HPAI HNI products (Herbs product, Health food & beverage and Cosmetic & home care), the stock that must be increased or available is the Herbs Product.

Improvement of Umbilical Cord Blood Gas Analysis with Implementation of Clot Catchers.

Umbilical cord blood gas testing is a key component of objective pre- and perinatal evaluation of fetal acid base status to determine presence of intrapartum asphyxia and risk of neonatal encephalopathy. Heparinized cord blood is more likely to form small clots than other blood sources, which can interfere with, or preclude, sample analysis. Cord blood samples are irreplaceable and cannot be recollected, thereby compromising clinical decision-making when analysis is not possible. We evaluated processes to prevent excessive rates of cord blood clotting and quantified their impact on successful testing of blood gas specimens. Verified result and cancellation data were obtained retrospectively from the laboratory information system. Clot catchers were evaluated using noncord remnant specimens. Collection syringes were compared via collection from remnant cord sections. Prior to implementation of any interventions, retrospective analysis indicated a cancellation rate of 18.6% for umbilical cord blood gas specimens (arterial and venous) and 0.7% for noncord blood arterial and venous samples. Clot catchers were validated for clinical use, with a bias of <±4% for all analytes. After clot catchers were implemented for all cord specimens, cancellation rate decreased approximately 5-fold in the first month and remained <5% a year after implementation. A limited comparison of two heparin syringe types revealed a small difference in the overall rate of specimen clotting. Implementation of clot catchers was acceptable for analysis of cord blood samples, and when implemented resulted in a sustained 5-fold decrease in the rate of cord blood gas order cancellation.

Improving the process of obtaining screening and diagnostic breast MRI: A quality improvement project at an urban VA hospital.

e18641 Background: There are two million female veterans across the United States, a number increasing by more than 18,000 per year. 1 in 8 women will be diagnosed with breast cancer in their lifetime, and studies have shown that servicewomen are 40% more likely to get breast cancer than civilians. The American Cancer Society (ACS) recommends screening MRI in women with 20-25% or greater lifetime risk of breast cancer, and both primary care physicians and oncologists are now ordering these more frequently. The Michael E. DeBakey Veteran’s Affairs Medical Center (MEDVAMC) performs screening mammograms and ultrasounds, but breast MRI is not currently available at the facility. Care in the Community arranges for veterans to obtain these studies at outside facilities. This is a complex process, requiring coordination among multiple departments, the outside MRI facility, and the patient. There are a significant number of MRI order cancellations per month, which can lead to delays in diagnosis or treatment. Methods: We created a comprehensive process map for the current breast MRI ordering process to determine areas for improvement. We looked at all breast MRI orders starting in 12/2020 at the MEDVAMC and determined the rate of cancellation and the rate of benign (BIRADS 1-2) vs non benign (BIRADS 0, 3-5) screening outcomes. We created a Pareto analysis to determine the most common cancellation reasons and p-charts of the percent of cancelled MRI orders per month and a run chart of the percent due to incorrect verbiage. Results: Of the 243 orders placed for MRI for 124 patients from 12/2020 to 1/2022, 64.2% were cancelled. Of the 57% of patients (71/124) with complete MRIs, 35.2% had non-benign findings requiring follow up, excluding known malignancies. Our Pareto analysis showed that most cancellations were due to incorrect verbiage in the order. An intervention in 6/2021 changed the MRI order from a free text box to clickable options, followed by an educational intervention in 12/2021. Cancellations due to incorrect verbiage decreased overall after June, however it has not yet reached significance. It is too early to determine if the educational intervention caused a significant change, however the cancellation rate has decreased. Conclusions: Providing discrete, clickable options within the MRI order has reduced the number of cancellations due to incorrect verbiage, though we have not yet reached significance. Early data suggests that the educational intervention has improved the cancellation rate. Fewer cancellations will lead to more timely studies, which will in turn lead to faster follow up of non-benign findings. Further directions include using a nurse navigator to reduce confusion and delays, further simplifying the ordering process, checking patient phone numbers in clinic, and sending patients home with information on the importance of screening MRI to reduce veteran cancellation.

Gegenbauer’s Twin

In Twin Higgs models the dominant source of fine-tuning is the cancellation of order v2/f2 required to obtain a Standard Model-like Higgs, where v and f are the electroweak and new physics scales, respectively. Recently proposed Gegenbauer Goldstone models naturally realise v2/f2 « 1 and hence remove this source of fine-tuning. By combining the two into ‘Gegenbauer’s Twin’, we obtain a symmetry-based model for Higgs-sector naturalness consistent with current collider measurements without fine-tuning of parameters. Single-Higgs coupling deviations of a few percent and trilinear self-coupling deviations of order one are irreducible in the natural parameter space. Thus, notably, the fingerprints of Gegenbauer’s Twin could emerge first through di-Higgs measurements at the High-Luminosity LHC.

Multistate Models for the Recovery Process in the Covid-19 Context: An Empirical Study of Chinese Enterprises

The Covid-19 pandemic has severely affected enterprises worldwide. It is thus of practical significance to study the process of enterprise recovery from Covid-19. However, the research on the effects of relevant determinants of business recovery is limited. This article presents a multistate modeling framework that considers the determinants, recovery time, and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic (recovery state), with the help of an accelerated failure time model. Empirical data from 750 enterprises were used to evaluate the recovery process. The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations. With the increase of supplies and orders, the probability of transition between different recovery states gradually increases, and the recovery time of enterprises becomes shorter. For manufacturing industries, the factors that hinder recovery are more complex. The main problems are employee panic and order cancellations in the initial stage, employee shortages in the middle stage, and raw material shortages in the full recovery stage. This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown

In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately.

Impact of COVID-19 on Small- and Medium-sized Enterprises

The COVID-19 pandemic has severely impacted global business operations. Most importantly, practitioners of small- and medium-sized enterprises (SMEs) have been facing devastating economic shocks emanated from the COVID-19 pandemic. The purposes of this study are to explore the business status of SMEs, business sustenance period, types of problems faced by the practitioners and impact on production volume, due to the COVID-19 pandemic. For these purposes, primary survey data from 284 SMEs in Bangladesh are collected in this study. The survey data are descriptively analyzed using appropriate graphs. This study finds that SMEs are facing several problems such as demand fall (92.96%), cancellation of orders (85.56%), liquidity crisis (74.30%), delayed transportation (72.53%), unsecured working environment (68.31%), raw material shortage (63.73%) and employees’ absenteeism (62.67%). The SMEs face beyond 40% decrease in production volume against the pre-pandemic year. Furthermore, this study formulates several strategic measures, which will certainly guide policymakers and practitioners to combat the COVID-19 economic shocks for the SMEs and also for other industries.

Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm

Dynamic scheduling methods are essential and critical to manufacturing systems because of uncertain events in the production process, such as new job insertions, order cancellations, worker absences, and machine breakdowns. Emerging digital twin (DT) technology can help detect disturbances by continuously comparing physical space with virtual space and triggering a rescheduling policy immediately after a disturbance. This enables dynamic scheduling and greatly reduces the deviation between preschedules and actual schedules. This study focuses on a DT-enabled integrated optimisation problem of flexible job shop scheduling and flexible preventive maintenance (PM) considering both machine and worker resources. A double-layer Q-learning algorithm (DLQL) is designed as the underlying key optimisation method to simultaneously learn the selection process of machines and operations to achieve efficient real-time scheduling. The superior solution performance of DLQL was verified by comparing it with two well-known metaheuristic algorithms and a single-layer Q-learning algorithm under several benchmarks. Furthermore, different disturbance settings were designed to illustrate the DLQL-based dynamic scheduling process in detail. The proposed reinforcement learning (RL)-driven DT enables efficient collaborative scheduling between production and maintenance departments and helps manufacturing companies improve the real-time decision-making process under uncertain perturbations.

The Garment Industry in Bangladesh, Corporate Social Responsibility of Multinational Corporations, and The Impact of COVID-19

AbstractThis article investigates the Bangladeshi garment industry that supplies ready-made garments for global brands and the corporate social responsibilities (CSRs) of the brands/multinational corporations (MNCs) towards their supply chains. Although outsourcing and global trade have boosted the living standards of many people in the Bangladeshi garment industry, there are some significant concerns regarding the working conditions and treatment of workers in these supply chains. This, in turn, cannot, and should not, be detached from the legal relationship between the Bangladeshi supply chains and the MNCs contracting with them. This article examines the impact of COVID-19, which has exposed the fragility and the pre-existing flaws in the relationship between the MNCs and their suppliers more clearly than at any other time in history. There is a huge governance gap between MNCs and supply chains that needs to be addressed urgently. This article assesses the legality of the cancellation of orders by the brands that invoked the force majeure clause. In doing so, this paper briefly deals with the responses of three American retail companies, namely Sears, Kohl’s, and the Arcadia Group, in the wake of the COVID-19 pandemic. It illustrates that the power asymmetry between the MNCs and their suppliers has put the supply chains in an unreasonably disadvantageous position, creating unfair and even unconscionable conduct by several MNCs. This article also looks at solutions for the existing problems, focusing, inter alia, on non-financial reporting requirements in the US and the EU to implement CSR in supply chains. However, disappointingly, the authors had to conclude that MNCs typically have the propensity to disregard CSR, whenever it is convenient for them and use CSR for mere “greenwashing” purposes for their strategic benefit only. It is a problem that requires constant attention and continuing research to find long-term solutions. The article employs doctrinal methodology and, by conducting a meta-analysis of literature and case-studies, it provides a comprehensive understanding of how the industry works.

Dynamic pricing and penalty strategies in a coupled market with ridesourcing service and taxi considering time-dependent order cancellation behaviour

Time-dependent order cancellation behaviour affects ridesourcing platform operation and overall system performance. This paper models impacts of time-dependent order cancellation behaviour on the driver–rider matching efficiency and examines ridesourcing platform operation strategies, where taxi is the alternative to the ridesourcing mode. A time-dependent matching model is developed to characterize the complex interaction among participants (drivers and riders) considering ridesourcing order cancellation behaviour, where the impact of the cancelled orders on the numbers of drivers and riders is explicitly modelled. In particular, we formulate the time-dependent order cancellation rates before and after the matching stage (i.e., cancellation of unconfirmed and confirmed orders). Under the proposed model, a platform profit maximization problem is formulated and three pricing strategies are examined. Our numerical studies demonstrate that the dynamic pricing (i.e., customer/rider fare and driver wage) can well accommodate time-dependent system inputs (e.g., demand rates) and thus enable the platform to increase profit via better market segmentation. We also investigate the objective of maximizing the number of completed trips and examine the trade-off between the platform profit and the number of completed trips. In addition, we show that the relaxation of upper bounds of the ridesourcing fare and order cancellation penalty can increase the ridesourcing platform’s profit and indirectly improve the utilization rate of taxis as well as the taxi company’s profit.

High-frequency trading, stock volatility, and intraday crashes

We examine the effect of high-frequency trading (HFT) on the price volatility of Euronext-listed stocks. Under stable market conditions, greater HFT intensity is associated with decreased stock price volatility. However, during periods of intraday crashes, rapid interactions between HFT algorithms lead to high rates of order cancellations and simultaneous withdrawals of high-frequency traders from the limit order book. High-frequency traders submit aggressive orders during these periods and consume more liquidity than they provide, resulting in increased stock price volatility.

A Sustainable Advance Payment Scheme for Deteriorating Items with Preservation Technology

Profitably managing inventories is always a big challenge for retailers in the current context of transparent and competitive business. A general retailer always needs to handle both deteriorating and non-deteriorating products simultaneously to run a business. Deterioration of products sometimes impacts a retailer’s profits badly—a situation which can be alleviated by implementing proper preservation technology. In addition, to improve profits and minimize costs, a retailer always seeks some credit facilities (e.g., advance payment, trade credit facilities, etc.) from the supplier to continue the business smoothly with minimum investment. Advance payment is renowned for preventing the possibility of business orders being canceled and helping the retailer to minimize the risk of investing significant amounts at a single time. The foremost objective of this research is to analyze the facilities of advance payment and preservation technology investment and concurrent attempts to deal with shortages. This study shows that, given the presence of preservation technology, the result of case II is 68.06% higher than that of case I, whereas when preservation technology is absent, the result of case II is 71.93% higher than that of case I. The managerial insights of this analysis reveal that preservation technology attempts to prolong product life by preventing deterioration, which contributes to the retailer’s profitable business. On the other hand, in the case of an advance payment scheme, although the costs are relatively high, the study emphasizes the importance of the advance payment facility as it limits the risk of order cancellation and makes business more flexible for both supplier and retailer. The proposed model is solved by the classical optimization technique. Some theoretical derivations with numerical analysis support the model and provide some managerial insights for practitioners.

Pricing and matching for on-demand platform considering customer queuing and order cancellation

Queuing on an on-demand platform may make some customers disgust and give up using it, and the customers who have confirmed the orders may also cancel the orders due to some uncertain factors, which causes certain opportunity loss to the platform. This article considers both customer queuing and order cancellation (COC) behaviour, and studies optimal pricing and matching of the profit-maximizing platform. We first construct models without and with COC behaviour (cases N and C), and then propose two strategies of the platform to deal with COC behaviour, including the penalty strategy (case PC) and the penalty-subsidy strategy (case PSC). By solving these models and analysing, we find that although the penalty strategy intuitively discourages some customers from using on-demand services, the platform reduces the service price because of penalty fee, which indirectly encourages more customers who may not cancel orders to request services. We also find that when the COCR is greater than a certain critical point, both the penalty strategy and penalty-subsidy strategy are advantageous, while the penalty strategy is the best. However, when the COCR is less than the critical point, the penalty strategy is unfavourable, while the penalty-subsidy strategy is advantageous. Abbreviations: COC: customer queuing and order cancellation; COCR: customer order cancellation rate.

Analysis of Individual High-Frequency Traders' Buy-Sell Order Strategy Based on Multivariate Hawkes Process.

Traders who instantly react to changes in the financial market and place orders in milliseconds are called high-frequency traders (HFTs). HFTs have recently become more prevalent and attracting attention in the study of market microstructures. In this study, we used data to track the order history of individual HFTs in the USD/JPY forex market to reveal how individual HFTs interact with the order book and what strategies they use to place their limit orders. Specifically, we introduced an 8-dimensional multivariate Hawkes process that included the excitations due to the occurrence of limit orders, cancel orders, and executions in the order book change, and performed maximum likelihood estimations of the limit order processes for 134 HFTs. As a result, we found that the limit order generation processes of 104 of the 134 HFTs were modeled by a multivariate Hawkes process. In this analysis of the EBS market, the HFTs whose strategies were modeled by the Hawkes process were categorized into three groups according to their excitation mechanisms: (1) those excited by executions; (2) those that were excited by the occurrences or cancellations of limit orders; and (3) those that were excited by their own orders.

Price discovery during parallel stocks and options preopening: Information distortion and hints of manipulation

Many exchanges act to prevent manipulative orders from distorting informative price discovery during stock and options’ markets preopening. Most preopening sessions run in parallel: indicative book-based stock prices alongside traded index options, whose underlying asset is the indicative index. Lead-lag patterns between the options-implied and the indicative indexes may point to differences in informational efficiency and/or manipulated prices. With three regulatory events throughout our sample, serving as natural experiments, we explore price discovery properties in both markets. We find significant lead-lag, price reversal, and order cancellation patterns similar to those predicted by theoretical models of manipulation, together with informational inefficiencies.

The Inventory Control System of Reverse Logistics for E-Commerce Packaging Recovery Based on BP Neural Network

The paper combines BP neural network to optimize the control system of e-commerce packaging and reverse logistics inventory. Through improving the hardware configuration structure of the system, the system can be improved and the operation effect of the system can be improved. The software flow and operation algorithm of the storage control system of e-commerce packaging recycling reverse logistics are optimized step by step, and the logistics is delivered by following the vehicle on the spot and visiting the logistics The distribution personnel collect the relevant data and data in the process of logistics and transportation, draw the reverse logistics business flow chart, point out the situation of reverse logistics before and after the goods distribution and distribution due to the cancellation of orders or transactions by customers, and the application for return of goods after the transaction. Meanwhile, it points out that the sales return operation site in the reverse logistics management process is chaotic and not formed the clear business process specification and other problems can effectively control the reverse logistics inventory of e-commerce packaging recovery. Finally, the experiment proves that the e-commerce packaging recycling reverse logistics inventory control system is more practical in the practical application process, and fully meets the research requirements.

The effect of amended order on firm resilience through supply chain coordination

Pandemic Covid 19 has resulted in manufacturing companies experiencing disruptions in fulfilling customer orders and changes in product forecasting results from calculation. Manufacturing companies essential in handling covid experience increased demands, while other companies not related to covid experienced a decrease even in cancellation of orders. Order changes determine the company's performance. This research is related to the effect of order changes on firm resilience through supply chain coordination. The distribution of questionnaires to manufacturing companies in Indonesia is done by sending a google form link in the social media group. Data obtained as many as 446 questionnaires and analyzed using the partial least square technique. The study results showed that amended orders positively impacted 0.728 to the internal operational coordination, the positive influence of 0.201 to coordinate with the supplier, and positive effect with a coefficient of 0.213 to coordination with the customer. Internal operational coordinated influence coordinated with the customer of 0.593 and coordinated with the customer of 0.515. Supply chain coordinated impact on the company's supply chain resilience determined by internal operational coordination by 0.371, coordinated with the supplier of 0.183 and coordinated with the customer of 0.199. The practical contribution of the research is to make managers able to build a coordinated Supply chain to overcome amended orders and increase supply chain deficiencies.

Frequent Batch Auctions vs Continuous Time Auctions Under Order Cancellation and Maker-Taker Fee

Frequent Batch Auctions vs Continuous Time Auctions Under Order Cancellation and Maker-Taker Fee