Arrival processes in port modeling: insights from a case study

This paper investigates the impact of arrival processes on the ship handling process. Two types of arrival processes are considered: controlled and uncontrolled. Simulation results show that uncontrolled arrivals of ships perform worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to large vessel delays and storage capacity. The combination of stock-controlled arrivals for large vessels and equidistant arrivals for barges also performs better than the uncontrolled process. Careful allocation of ships to the mooring points of a jetty further improves the efficiency.supply chain management;logistics;simulation;transportation;case study

Arrival processes in port modeling: insights from a case study

This paper investigates the impact of arrival processes on the ship handling process. Two types of arrival processes are considered: controlled and uncontrolled. Simulation results show that uncontrolled arrivals of ships perform worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to large vessel delays and storage capacity. The combination of stock-controlled arrivals for large vessels and equidistant arrivals for barges also performs better than the uncontrolled process. Careful allocation of ships to the mooring points of a jetty further improves the efficiency

On the effect of ship arrival processes on jetty and storage capacity

Ports provide jetty facilities for ships to load and unload their cargo. Jetty capacity is costly and therefore limited, causing delays for arriving ships. However, ship delays are also costly, so terminal operators attempt to minimize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This report considers three arrival processes: stock-controlled, equidistant, and uncontrolled. We assess how their deployment in a port simulation model,based on data from a real case study, affects the efficiency of the loading and unloading process, making a case for careful modeling of arrival processes in port simulations. Uncontrolled, which is an assumed arrival process property in many client-oriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to large vessel delays and storage capacity. Additional control of the arrival process through the application of a priority scheme in processing ships further impacts efficiency in all three cases

A family history of breast cancer will not predict female early onset breast cancer in a population-based setting

ABSTRACT: BACKGROUND: An increased risk of breast cancer for relatives of breast cancer patients has been demonstrated in many studies, and having a relative diagnosed with breast cancer at an early age is an indication for breast cancer screening. This indication has been derived from estimates based on data from cancer-prone families or from BRCA1/2 mutation families, and might be biased because BRCA1/2 mutations explain only a small proportion of the familial clustering of breast cancer. The aim of the current study was to determine the predictive value of a family history of cancer with regard to early onset of female breast cancer in a population based setting. METHODS: An unselected sample of 1,987 women with and without breast cancer was studied with regard to the age of diagnosis of breast cancer. RESULTS: The risk of early-onset breast cancer was increased when there were: (1) at least 2 cases of female breast cancer in first-degree relatives (yes/no; HR at age 30: 3.09; 95% CI: 128-7.44), (2) at least 2 cases of female breast cancer in first or second-degree relatives under the age of 50 (yes/no; HR at age 30: 3.36; 95% CI: 1.12-10.08), (3) at least 1 case of female breast cancer under the age of 40 in a first- or second-degree relative (yes/no; HR at age 30: 2.06; 95% CI: 0.83-5.12) and (4) any case of bilateral breast cancer (yes/no; HR at age 30: 3.47; 95%: 1.33-9.05). The positive predictive value of having 2 or more of these characteristics was 13% for breast cancer before the age of 70, 11% for breast cancer before the age of 50, and 1% for breast cancer before the age of 30. CONCLUSION: Applying family history related criteria in an unselected population could result in the screening of many women who will not develop breast cancer at an early age

Arrival Processes for Vessels in a Port Simulation

Ports provide jetty facilities for ships to load and unload their cargo. Jetty capacity is costly and therefore limited, causing delays for arriving ships. However, ship delays are also costly, so terminal operators attempt to min imize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This report considers three arrival processes: stock-controlled, equidistant, and uncontrolled. We assess how their deployment in a port simulation model, based on data from a real case study, affects the efficiency of the loading and unloading process, making a case for careful modeling of arrival processes in port simulations. Uncontrolled, which is an assumed arrival process property in many client-oriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to large vessel delays and storage capacity. Additional control of the arrival process through the application of a priority scheme in processing ships further impacts efficiency in all three cases.Ports provide jetty facilities for ships to load and unload their cargo. Jetty capacity is costly and therefore limited, causing delays for arriving ships. However, ship delays are also costly, so terminal operators attempt to min imize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This report considers three arrival processes: stock-controlled, equidistant, and uncontrolled. We assess how their deployment in a port simulation model, based on data from a real case study, affects the efficiency of the loading and unloading process, making a case for careful modeling of arrival processes in port simulations. Uncontrolled, which is an assumed arrival process property in many client-oriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to large vessel delays and storage capacity. Additional control of the arrival process through the application of a priority scheme in processing ships further impacts efficiency in all three cases

The predictive performance and impact of pediatric early warning systems in hospitalized pediatric oncology patients-A systematic review

Pediatric early warning systems (PEWS) arewidely used to identify clinically deteriorating patients. Hospitalized pediatric oncology patients are particularly prone to clinical deterioration. We assessed the PEWS performance to predict early clinical deterioration and the effect of PEWS implementation on patient outcomes in pediatric oncology patients. PubMED, EMBASE, and CINAHL databases were systematically searched from inception up to March 2020. Quality assessment was performed using the Prediction model study Risk-Of-Bias Assessment Tool (PROBAST) and the Cochrane Risk-of-Bias Tool. Nine studies were included. Due to heterogeneity of study designs, outcome measures, and diversity of PEWS, it was not possible to conduct a meta-analysis. Although the studies reported high sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) of PEWS detecting inpatient deterioration, overall risk of bias of the studies was high. This review highlights limited evidence on the predictive performance of PEWS for clinical deterioration and the effect of PEWS implementation