Cost Efficient Short Term Capacity Planning for MTO Enterprises

2015 ◽  
Author(s):  
Ketki Kulkarni ◽  
Pallavi Manohar
Keyword(s):  
Capacity Planning ◽  
Short Term ◽  
Cost Efficient

scholarly journals Use of GnRH for Synchronization of the Follicular Wave in Assisted Reproductive Technologies in Sheep: A Preliminary Study

Animals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1208
Author(s):  
Aina Año-Perello ◽  
Zurisaday Santos-Jimenez ◽  
Teresa Encinas ◽  
Paula Martinez-Ros ◽  
Antonio Gonzalez-Bulnes
Keyword(s):  
Luteinizing Hormone ◽  
Assisted Reproductive Technologies ◽  
Ovarian Response ◽  
Reproductive Technologies ◽  
Estrus Synchronization ◽  
Short Term ◽  
Follicular Wave ◽  
Preliminary Study ◽  
Cost Efficient ◽  
Set Up

The present study aimed to set up a short-term protocol for synchronization of follicular wave emergence in sheep, concomitant with estrus synchronization, which would improve ovarian response in assisted reproductive technologies. Administration of a single GnRH dose, concomitant with the insertion of a progesterone-loaded CIDR device, caused regression of gonadotrophin-dependent follicles ≥4 mm in all the GnRH-treated sheep and in around 80% of the controls treated only with CIDR (p < 0.05). Similar percentages of ewes lost all follicles (around 70%) or only the largest one (around 30%) in both groups. Hence, 54.1% and 70% of the sheep lost all large follicles and initiated a new follicular wave in the control and GnRH groups, respectively (p < 0.05). The remaining sheep showed follicles that were still not dependent of luteinizing hormone (LH). So, in fact, all the sheep had non-dominant follicles after treatment. In conclusion, a treatment including GnRH at CIDR insertion would offer a time- and cost-efficient protocol for inducing follicular turnover and synchronizing a new follicular wave at any stage of the estrous cycle.

The New Regulator in Town: The Effect of Walmart's Sustainability Mandate on Supplier Shareholder Value

2018 ◽  
Vol 82 (2) ◽  
pp. 124-141 ◽  
Author(s):  
Katrijn Gielens ◽  
Inge Geyskens ◽  
Barbara Deleersnyder ◽  
Max Nohe
Keyword(s):  
Brand Equity ◽  
Shareholder Value ◽  
Abnormal Returns ◽  
Short Term ◽  
Power Sources ◽  
Expert Power ◽  
Operational Characteristics ◽  
Countervailing Power ◽  
Cost Efficient ◽  
The Impact

Suppliers are increasingly being forced by dominant retailers to clean up their supply chains. These retailers argue that their sustainability mandates may translate into profits for suppliers, but many suppliers are cynical about these mandates because the onus to undertake the required investments is on them while potential gains may be usurped by the mandating retailer. We examine whether supplier fears are justified by studying the impact of Walmart's sustainability mandate on its suppliers’ (short-term) shareholder value. Although about two-thirds of suppliers are indeed financially harmed, approximately one-third benefit. To delve deeper into this variation, we relate suppliers’ short-term abnormal returns to Walmart's appropriation power and explore whether and to what extent a supplier's referent and expert power sources, derived from its marketing and operational characteristics, respectively, can counteract Walmart's appropriation attempts. We find that the supplier's marketing characteristics (its environmental reputation, brand equity, and advertising) provide it with the countervailing power needed to resist Walmart's appropriation attempts. In contrast, cost-efficient suppliers and suppliers that invest heavily in R&D have more difficulty withstanding Walmart's squeeze attempts.

scholarly journals Simulation-Based Capacity Planning of a Biofuel Refinery

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1702
Author(s):  
Sojung Kim ◽  
Evi Ofekeze ◽  
James R. Kiniry ◽  
Sumin Kim
Keyword(s):  
Ethanol Production ◽  
Capacity Planning ◽  
Fossil Fuels ◽  
Sustainable Use ◽  
Real Data ◽  
Optimal Number ◽  
Simulation Software ◽  
Corn Yield ◽  
Model Complex ◽  
Cost Efficient

The reduction in the operational cost of a biofuel refinery is vitally important to make biofuel competitive with fossil fuels. The aim of this paper is to find a cost-efficient and sustainable refinery capacity for grain-based ethanol (i.e., corn-based ethanol) production, which will play an important role in promoting the widespread adoption and sustainable use of ethanol, by improving the productivity of the overall refining process. Continuous-event simulation was utilized in this study to model complex operations of a refinery such as the loading, unloading and treatment of feedstock over nine major phases (e.g., feedstock storage and handling, pretreatment and conditioning, fermentation and hydrolysis, and enzyme production) to produce ethanol. To improve the model prediction, the real data of corn yield produced in Tazewell County, Illinois, U.S. were used. The proposed simulation model is implemented in AnyLogic® University 8.6.0 simulation software, Chicago, IL, USA, and the (near) optimal number of reactors for the hydrolysis and fermentation is found via optimization software known as OptQuest®, Boulder, CO, USA, As a result, the proposed approach found that six reactors showed the optimal daily profit from USD 67,500 to 82,217. This information will help engineers and policy makers to modify the capacity of a biofuel refinery for enhancement of the system efficiency and ethanol production.

Smart Short Term Capacity Planning: A Reinforcement Learning Approach

2021 ◽  
pp. 258-266
Author(s):  
Manuel Schneckenreither ◽  
Sebastian Windmueller ◽  
Stefan Haeussler
Keyword(s):  
Reinforcement Learning ◽  
Capacity Planning ◽  
Learning Approach ◽  
Short Term

scholarly journals Gene Teller: an extensible Alexa Skill for gene-relevant databases

2020 ◽  
Author(s):  
Jon D Hill
Keyword(s):  
Short Term Memory ◽  
Consumer Electronics ◽  
Biological Research ◽  
Short Term ◽  
Conversational Interaction ◽  
Amazon Web Services ◽  
Cost Efficient ◽  
The Cost ◽  
Intuitive Manner

Abstract Summary Voice assistants have become increasingly embedded in consumer electronics, as the quality of their interaction improves and the cost of hardware continues to drop. Despite their ubiquity, these assistants remain underutilized as a means of accessing biological research data. Gene Teller is a voice assistant service based on the Alexa Skills Kit and Amazon Lambda functions that enables scientists to query for gene-centric information in an intuitive manner. It includes several features, such as synonym disambiguation and short-term memory, that enable a natural conversational interaction, and is extensible to include new resources. The underlying architecture, based on Simple Storage Service and Amazon Web Services Lambda, is cost efficient and scalable. Availability and implementation A publicly accessible version of Gene Teller is available as an Alexa Skill from the Amazon Marketplace at https://www.amazon.com/dp/B08BRD8SS8. The source code is freely available on GitHub at https://github.com/solinvicta/geneTeller.

scholarly journals Effectiveness of a Time-limited Incentive on Participation by Hard-to-reach Respondents in a Panel Study

Field Methods ◽  
2016 ◽  
Vol 29 (3) ◽  
pp. 238-251 ◽  
Author(s):  
Paula Fomby ◽  
Narayan Sastry ◽  
Katherine A. McGonagle
Keyword(s):  
Telephone Interview ◽  
Negative Impact ◽  
Panel Study ◽  
Primary Caregivers ◽  
Response Rates ◽  
Income Dynamics ◽  
Short Term ◽  
Final Response ◽  
Cost Efficient ◽  
Term Response

We describe an experiment to provide a time-limited incentive among a random sample of 594 hard-to-reach respondents, 200 of whom were offered the incentive to complete all survey components of a study during a three-week winter holiday period. Sample members were primary caregivers of children included in the 2014 Child Development Supplement to the U.S. Panel Study of Income Dynamics. The incentive provided US$50 to caregivers who completed a 75-minute telephone interview and whose eligible children each completed a 30-minute interview. Results indicate that the incentive was an effective and cost-efficient strategy to increase short-term response rates with hard-to-reach respondents and had no negative impact on final response rates.

scholarly journals THE MODEL OF ALLOTMENTS BOOKING IN A SHUTTLE TRAIN

Transport ◽  
2005 ◽  
Vol 20 (6) ◽  
pp. 236-239 ◽  
Author(s):  
Andrius Jaržemskis
Keyword(s):  
Capacity Planning ◽  
Stochastic Dynamic Programming ◽  
Programming Model ◽  
Rolling Stock ◽  
Stochastic Dynamic ◽  
Short Term ◽  
Trade Off ◽  
Departure Time ◽  
Dynamic Programming Model ◽  
The Cost

The article presents the model of allotments booking for trucks or containers in shuttle trains. This model could be used in practice if railway companies sale free allotments in rolling stock for forwarders. This paper proposes a stochastic dynamic programming model of a short‐term capacity planning model for necessary allotment. The allotment could be acquired by freight forwarders many months ahead on a contract basis, but usually the forecast demand is unreliable. The preplanning of allotment for transport units is needed when the date draws nearer to the train departure time. The presented model evaluates the optimal cost policy based on economic trade‐off between the cost of backlogged shipment and the cost of acquiring additional allotment.

scholarly journals COVID-19: Short-term forecast of ICU beds in times of crisis

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245272
Author(s):  
Marcel Goic ◽  
Mirko S. Bozanic-Leal ◽  
Magdalena Badal ◽  
Leonardo J. Basso
Keyword(s):  
Incomplete Data ◽  
Capacity Planning ◽  
Urgent Care ◽  
Epidemiological Models ◽  
Short Term ◽  
Short Term Forecast ◽  
First Order ◽  
Term Forecast ◽  
Health Authorities ◽  
Forecasting Errors

By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.

Sign in / Sign up

Export Citation Format

Share Document