Designing a Relief Distribution Network under Uncertain Situation: Preparedness in Responding to Disaster

None can predict a disaster precisely: where, when, and how big a disaster will strike one area. This situation leads to uncertainty in such as required demand and supply availabilities. To an area that has been identified threatening by a natural hazard, a possible disaster scenario may compile. Since time is vital in disaster response operations, developing strategies to speed up emergency response is necessitated. This study is aimed to develop a stochastic model for a location-allocation problem in responding to a forecasted disaster. Our stochastic approach recommends a number and locations of local distribution centers (LDCs) that are required to be set up in the initial stage of the response phase and a number of relief items that will be dispatched to survivors in the affected areas through the proposed relief network. A mixed delivery strategy is applied in a 3-tier of a relief distribution network encompassing warehouses, LDCs, and shelters. This strategy provides the affected people in some of the shelters to receive relief items directly from nearby warehouses, while the remaining shelters will get supplies indirectly through the opened LDCs. Comparing to the indirect strategy that shelters are permitted to receive aid goods only through LDCs, the proposed mixed delivery strategy provides more efficient and effective relief distribution. The probable tsunami in West Sumatra, Indonesia, known as Mentawai Megathrust, is employed to illustrate the developed model. The model will be beneficial for disaster managers to improve the performance of a disaster relief operation

Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR

A study on design and analysis of a school bus project for a municipality in southern Thailand

The key characteristic of a public transport project is its challenging requirements and the various stakeholders involved, like in the school bus project of this research. To substantiate the design of such a project and analysis of its performance, much information will be required to convince the stakeholders. Hence, acquiring the project information requires a solid framework for understanding and projecting the unseen problems that impact the stakeholders. This research studies the design and analyses a school bus system in Hat Yai City Municipality. The study process was formulated on the feasibility study framework, consisting of market, technical, and economic studies. The quantitative techniques were effectively applied, including behavioural customer study, city transportation design, simulation modelling, and economic analysis. The predicted system performances and analysis results were generated as information made available to the stakeholders, for them to make an insightful assessment. The proposed framework can provide incisive guidelines for the design and analysis of other projects

Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is$1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory

Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is$1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory

Filmless versus film-based systems in radiographic examination costs: an activity-based costing method

Background: Since the shift from a radiographic film-based system to that of a filmless system, the change in radiographic examination costs and costs structure have been undetermined. The activity-based costing (ABC) method measures the cost and performance of activities, resources, and cost objects. The purpose of this study is to identify the cost structure of a radiographic examination comparing a filmless system to that of a film-based system using the ABC method. Methods: We calculated the costs of radiographic examinations for both a filmless and a film-based system, and assessed the costs or cost components by simulating radiographic examinations in a health clinic. The cost objects of the radiographic examinations included lumbar (six views), knee (three views), wrist (two views), and other. Indirect costs were allocated to cost objects using the ABC method. Results: The costs of a radiographic examination using a filmless system are as follows: lumbar 2,085 yen; knee 1,599 yen; wrist 1,165 yen; and other 1,641 yen. The costs for a film-based system are: lumbar 3,407 yen; knee 2,257 yen; wrist 1,602 yen; and other 2,521 yen. The primary activities were "calling patient," "explanation of scan," " take photographs," and "aftercare" for both filmless and film-based systems. The cost of these activities cost represented 36.0% of the total cost for a filmless system and 23.6% of a film-based system. Conclusions: The costs of radiographic examinations using a filmless system and a film-based system were calculated using the ABC method. Our results provide clear evidence that the filmless system is more effective than the film-based system in providing greater value services directly to patients

Benchmarking and reducing length of stay in Dutch hospitals

<p>Abstract</p> <p>Background</p> <p>To assess the development of and variation in lengths of stay in Dutch hospitals and to determine the potential reduction in hospital days if all Dutch hospitals would have an average length of stay equal to that of benchmark hospitals.</p> <p>Methods</p> <p>The potential reduction was calculated using data obtained from 69 hospitals that participated in the National Medical Registration (LMR). For each hospital, the average length of stay was adjusted for differences in type of admission (clinical or day-care admission) and case mix (age, diagnosis and procedure). We calculated the number of hospital days that theoretically could be saved by (i) counting unnecessary clinical admissions as day cases whenever possible, and (ii) treating all remaining clinical patients with a length of stay equal to the benchmark (15^thpercentile length of stay hospital).</p> <p>Results</p> <p>The average (mean) length of stay in Dutch hospitals decreased from 14 days in 1980 to 7 days in 2006. In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15th percentile hospital in the year 2000. In 2006 the mean length of stay ranged from 5.1 to 8.7 days. If the average length of stay of the 15^thpercentile hospital in 2006 is identified as the standard that other hospitals can achieve, a 14% reduction of hospital days can be attained. This percentage varied substantially across medical specialties. Extrapolating the potential reduction of hospital days of the 69 hospitals to all 98 Dutch hospitals yielded a total savings of 1.8 million hospital days (2006). The average length of stay in Dutch hospitals if all hospitals were able to treat their patients as the 15^thpercentile hospital would be 6 days and the number of day cases would increase by 13%.</p> <p>Conclusion</p> <p>Hospitals in the Netherlands vary substantially in case mix adjusted length of stay. Benchmarking – using the method presented – shows the potential for efficiency improvement which can be realized by decreasing inputs (e.g. available beds for inpatient care). Future research should focus on the effect of length of stay reduction programs on outputs such as quality of care.</p

A predictive model for patient length of stay at a teaching hospital

This research attempts to construct mathematical models for estimating length of stay per admission and investigating the effects of patients\u27 characteristics and clinical indicators on the length of stay for the top ten Diagnosis-Related Groups (DRGs) at a teaching hospital. It is also concerned with the development of cost per admission and cost per patient day functions. Further, these functions are used for analysis to determine a value of the length of stay that would minimize cost per patient day. Also, the effects of changing the length of stay (from the actual to the projected levels) on the total cost per year and the cost per patient day are examined. Moreover, the current cost system for the teaching hospital is evaluated and a new cost system (Activity-Based Costing) is proposed. The nuclear medicine unit is selected to implement the new cost system. The results indicate that the patients\u27 characteristics and the clinical indicators explain approximately 64% of the variation in the length of stay; also model prediction is 79% accurate. The effects of the clinical indicators on the length of stay are much stronger than the patients\u27 characteristics. The cost models fit the data as shown by the following indicators: the average of R2 is 0.79 and the mean of MAPE is 15. The cost variation analysis demonstrates that if a hospital can control the length of stay at the projected level, on an average, the cost per admission and the cost per patient day will decrease. Based on the top ten DRGs (6,367 admissions) in the year 1999, the cost per year and the cost per patient day decreased approximately 13% and 11%, respectively using the cost minimization analysis. The research confirms that the Activity-Based Costing can be applied to healthcare industry, and provides more accurate cost information than the current system. It assists management in effective cost reduction by focusing on non-value-added and providing more accurate statistics for pricing. Overall, this research offers a new decision support instrument for healthcare administrators