A Non-dominated Genetic Algorithm Based on Decoding Rule of Heat Treatment Equipment Volume and Job Delivery Date

This paper investigated the flexible job-shop scheduling problem with the heat treatment process. To solve this problem, we built an unified mathematical model of the heat treatment process and machining process. Up to now, this problem has not been investigated much. Based on the features of this problem, we are intended to minimize Cmax, maximize the space utilization rate of heat treatment equipment, and minimize the total delay penalty to optimize the scheduling. By taking the dynamic process arrival under consideration, this paper proposed a set of decoding rules based on the heat treatment equipment volume and job delivery date to achieve a hybrid dynamic scheduling solution during one scheduling procedure. When the utilization rate of heat treatment equipment volume is maximized, and the job delivery date is taken under consideration, it is preferred to minimize the number of workpiece batches in the same job, and reduce the waiting time of the pending job. In combination with the improved adaptive non-dominated genetic algorithm, we worked out the solution. Furthermore, we verified the effectiveness of the proposed decoding rules and improved algorithm through algorithm comparison and calculation results. Finally, a software system for algorithm verification and algorithm comparison was developed to verify the validity of our proposed algorithm.

Impact of a hospital specialty pharmacy in partnership with a free-standing care coordination organization on time to delivery and receipt of oral anticancer drugs.

43 Background: Most oral anti-cancer drugs (OACD) prescriptions require extensive coordination between providers and payers, which can delay drug receipt. Specialty pharmacies are intended to facilitate communication between multiple entities to deliver OACDs with increased efficiency. In 2018, our cancer center partnered with Shields Health Solutions (SHS), a freestanding organization providing care coordination to implement a hospital-based specialty pharmacy. We evaluated the rate of failed drug receipt (FR) and time to drug receipt (TTR) before and after specialty pharmacy implementation. Methods: We prospectively collected data on all new OACD prescriptions for adult oncology patients at a large, urban cancer center from 1/1/2018 to 12/31/2019. In fall 2018, a specialty pharmacy was opened to facilitate drug procurement for patients. We collected patient demographic, clinical, and insurance data, OACD name, date prescribed, delivery date, and interactions with payers and financial assistance groups. For prescriptions received, TTR was the number of days from OACD prescription to patient receipt of the drug. FR was defined as failure to receive a prescribed OACD. We excluded OACD prescriptions for a washout period of two months during pharmacy initiation. We used multivariable logistic regression to examine factors associated with TTR > 7 days and FR before and after specialty pharmacy implementation. Results: In total, 883 patients were prescribed 1145 new OACDs. The majority of prescribed drugs were targeted treatment (56%, N = 646) and 72% (N = 819) required prior authorization (PA). Of all prescriptions, 86% (N = 999) were successfully received with an overall median TTR of 7 days. Adjusted analyses showed that patients were more likely to receive their drugs in less than 7 days after specialty pharmacy implementation (OR: 1.4 95% CI 1.04 – 1.81), p = 0.03). In an unadjusted analysis, patients were more likely to receive their initial medications after specialty pharmacy implementation, compared to before specialty pharmacy implementation (89% vs. 84%, p = 0.04). Multivariable analysis showed a trend toward more patients receiving drugs after specialty pharmacy implementation (OR: 1.42, 95% CI 0.98 – 2.03, p = 0.06). Conclusions: The implementation of a hospital-based specialty pharmacy in partnership with SHS decreased TTR. This difference is in part attributable to improved care coordination and communication. A centralized approach may improve overall efficiency due to fewer clinical practice disruptions.

Customer-Classified Intracity Fruit Distribution Path Optimization: A Case Study in Jining, China

With the development of technology and economy, customer satisfaction is becoming increasingly important to businesses. Customers expect more in regard to product quality, personalised service and delivery date. This study randomly selected customers from a small-to-medium sized company in China as case study and used a K-Means cluster analysis approach to present the available management in logistics. The mileage saving method was also used to contribute the distribution path planning. This resulted in a more scientific distribution route based on comparative analysis, which helped the target company save resources and improve efficiency. This study helped companies effectively identify customer value by combining customer classification with intracity distribution path optimization. Simultaneously, it provides possible empirical reference to service quality improvement, the distribution path optimization, the resource wasting reduction and the companies’ operation efficiency enhancement. It enriches the current literature about food distribution path optimization for small and medium sized food company.

Sensitivity of pregnancy field on the COVID-19 case report form among pregnancies completed through December 31, 2020 — Illinois and Tennessee

Introduction: The considerable volume of infections from SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), has made it challenging for health departments to collect complete data for national disease reporting. We sought to examine sensitivity of the COVID-19 case report form (CRF) pregnancy field by comparing CRF data to the gold standard of CRF data linked to birth and fetal death certificates. Methods: CRFs for women aged 15–44 years with laboratory-confirmed SARS-CoV-2 infection were linked to birth and fetal death certificates for pregnancies completed during January 1–December 31, 2020 in Illinois and Tennessee. Among linked records, pregnancy was considered confirmed for women with a SARS-CoV-2 specimen collection date on or prior to the delivery date. Sensitivity of the COVID-19 CRF pregnancy field was calculated by dividing the number of confirmed pregnant women with SARS-CoV-2 infection with pregnancy indicated on the CRF by the number of confirmed pregnant women with SARS-CoV-2 infection. Results: Among 4,276 (Illinois) and 2,070 (Tennessee) CRFs that linked with a birth or fetal death certificate, CRF pregnancy field sensitivity was 45.3% and 42.1%, respectively. In both states, sensitivity varied significantly by maternal race/ethnicity, insurance, trimester of prenatal care entry, month of specimen collection, and trimester of specimen collection. Sensitivity also varied by maternal education in Illinois but not in Tennessee. Discussion: Sensitivity of the COVID-19 CRF pregnancy field varied by state and demographic factors. To more accurately assess outcomes for pregnant women, jurisdictions might consider utilizing additional data sources and linkages to obtain pregnancy status.

A Dynamic Just-in-Time Component Delivery Framework for Off-Site Construction

Off-site construction entails various advantages compared with the traditional construction method; however, the fragmentation of the prefabrication and assembly results in a complex supply chain. Both general contractors and factories often encounter production deviation, making the original component delivery plan nonoptimal. Traditionally, both parties tend to rely on internal resources or third-party resources to manage schedule changes, paying little attention to the optimisation of the component delivery process. The static compensation mechanisms reported in existing literature require factories to manage demand fluctuations but fail to encourage general contractors to control schedule deviations. Therefore, a dynamic compensation mechanism is proposed to achieve just-in-time component delivery, with which a factory shares possible changes for each component’s delivery date to its clients on an inverse Kanban system. First, unfavourable changes for the factory schedule are allocated with surcharges, and the general contractor should compensate the factory if it accepts the date changes; secondly, schedule changes that are beneficial for the factory are assigned as incentives, and the general contractor receives the factory’s incentive upon agreeing to the changes. Based on these two scenarios, genetic algorithm-based optimisation models are developed to achieve optimal delivery planning solutions. General contractors can obtain an optimal component delivery date to reduce the additional cost when they have changed the assembly schedule. General contractors can also optimise their component delivery schedule to trade their duration flexibility for incentives offered by factories. The models can help both parties to reduce component delivery waste when either side has the motivation to change the original component delivery schedules.

Oadrp Having Issues with Due Date and Limitations with Information

The "Online Dial-A-Ride Problem (ODARP)" is discussed in this article and time constraints and demand specifics include a deadline to reach the time limit. If a proposal is not filed by its delivery date, it will be cancelled. For data limit, only data on the origin is presented whenever the application is received public. And until the basis of the request is met, the target of the server is not statistics. The purpose of the problem is to schedule a server movement so that its deadlines can be met for a determined figure of queries (or the extreme amount of goods). We examine competitively several deterministic strategies with various constraints on the problem. And in various cases this paper shows many lower limits on the competitiveness of any deterministic algorithm for the problem.

Pregnancy and Childbirth Expectations During COVID-19 in a Convenience Sample of Women in the United States

The COVID-19 pandemic has transformed the health care landscape and shifted individuals’ expectations for and interactions with essential health services, including pregnancy-related care. This study explores alterations to individuals’ pregnancy and childbirth decisions during an infectious disease pandemic. A convenience sample of 380 pregnant individuals with an expected delivery date between April and December 2020 consented to enroll and complete an online questionnaire on their pregnancy and childbirth expectations during the COVID-19 pandemic; a subset of respondents ( n = 18) participated in semi-structured phone interviews. Survey data were analyzed quantitatively while interview data were analyzed using a thematic content analysis until a consensus on key themes was achieved. Respondents reported substantial stressors related to shifting policies of health care facilities and rapidly changing information about COVID-19 disease risks. As a result, respondents considered modifying their prenatal and childbirth plans, including the location of their birth (25%), health care provider (19%), and delivery mode (13%). These findings illuminate the concerns and choices pregnant individuals face during the COVID-19 pandemic and offer recommendations to engage in compassionate, supportive, and person-centered care during a time of unprecedented risk and uncertainty.

Monitoring of the Time and Action Calendar of a T-shirt Manufacturing

This study was calculated to investigate the time and action (TNA) plan of a T-shirt manufacturing. A time and action calendar determine the final date/time in which the main activities of an order should be against a scheduled distribution window. To ensure timely supply within a specific delivery date of buyers in the knit garments industry, Time and Action Calendar or TNA, a popular tool that is used for tracking and following up in preproduction processes. The acquaintance about the TNA plan will also help to organize the production time-efficient manner that is better production on lead time from buyers. Creating a TNA The calendar doesn't just flow the name and duration of the activity; It’s also about technically The duration of the activity works, understandably determining the foregoing and subsequent activities. TNA is the table of activities of specific order and Process flow Sort by the table of tasks that require to be finished. The two key important dates are the cut-off date (PCD) and the ex-factory date as per the TNA plan. TNA largely turned on the order, the prerequisites of the machine, and the specific approach flow of available yield capacity. TNA plan is measured by the time frame which is related to buyers' lead time to export a particular order. The TNA obtained by taking it the requisition of 8101 pieces of a basic t-shirt, made out of 95% cotton and 5% elastane 160 GSM single jersey fabrics. The order is for SS 2019 and the buyer is GAP, delivery date October 8, 2019, and shipment at New York, USA.