Search results

The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a scientific pricing framework.

The generating process of the historical loss cost ratio's (LCR's) are reviewed, and the Erlang mixture distribution is proposed. A modified credibility approach is developed based on the Erlang mixture distribution and the liability weighted LCR, and information from the observed data of the individual region/province is integrated with the collective experience of the entire crop reinsurance program in Canada.

A comprehensive data set representing the entire crop insurance sector in Canada is used to show that the Erlang mixture distribution captures the tails of the data more accurately compared to conventional distributions. Further, the heterogeneous credibility premium based on the liability weighted LCR's is more conservative, and provides a more scientific approach to enhance the reinsurance pricing.

Credibility models are in the early stages of application in the area of agriculture insurance, therefore, the credibility models presented in this paper could be verified with data from other geographical regions.

The credibility-based Erlang mixture model proposed in this paper should be useful for crop insurers and reinsurers to enhance their ratemaking frameworks.

This is the first paper to introduce the Erlang mixture model in the context of agricultural risk modeling. Two modified versions of the Bühlmann-Straub credibility model are also presented based on the liability weighted LCR to enhance the reinsurance pricing framework.

To evaluate and optimize asphalt paving operations taking place in the reconstruction project of Interstate 74 in the USA using simulation.

Data collected during construction was used to determine useful probabilistic density functions for the activities duration and to validate a developed simulation model. Upon validation, the developed model was used to study the impact of resources on the flow of operations, and on the cost‐effectiveness of the construction process.

The developed simulation model was successful in simulating the paving operation and its accuracy was acceptable compared to field measurements. Based on the results of a sensitivity analysis of the critical resources, multiple factors were identified in the decision‐making process to ensure that all aspects of the operation were considered. For the conditions pertinent to this construction site, 30 trucks, one paver, and two rollers are recommended. Using this set of resources results in a prompt and effective execution of the construction process.

The developed model is applicable only to asphalt paving operations. The predictive capability of the developed model has not yet been investigated.

Implementation of the presented model in practical applications can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing project‐specifics conditions and limitations. If this approach is not feasible, production charts may be developed to easily determine the optimum resources that should be used given specific time constraints and quantity, issues.

The presented methodology has great potential to optimize resources and production rates in similar asphalt paving operations. Moreover, benefits of simulation would be maximized if it was used during the planning phase, as it will affect equipment orders and material shipments.

The purpose of this paper is to consider the estimation of multicomponent stress-strength reliability. The system is regarded as alive only if at least s out of k (s<k) strengths exceed the stress. The reliability of such a system is obtained when strength, stress variates are from Erlang-truncated exponential (ETE) distribution with different shape parameters. The reliability is estimated using the maximum likelihood (ML) method of estimation when samples are drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets the authors illustrate the procedure.

The authors have developed multicomponent stress-strength reliability based on ETE distribution. To estimate reliability, the parameters are estimated by using ML method.

The simulation results indicate that the average bias and average mean square error decreases as sample size increases for both methods of estimation in reliability. The length of the confidence interval also decreases as the sample size increases and simulated actual coverage probability is close to the nominal value in all sets of parameters considered here. Using real data, the authors illustrate the estimation process.

This research work has conducted independently and the results of the author’s research work are very useful for fresh researchers.

The purpose of this paper is to provide delay announcements for call centers with hyperexponential patience modeling. The paper aims to employ a state-dependent Markovian approximation for informing arriving customers about anticipated delay in a real call center.

Motivated by real call center data, the patience distribution is modeled by the hyperexponential distribution and is analyzed by its realistic significance, with and without delay information. Appropriate M/M/s/r+H₂ queueing model is structured, including a voice response system that is employed in practice, and a state-dependent Markovian approximation is applied for computing abandonment. Based on this approximation, a method is proposed for estimating virtual delays, and it is investigated about the problem of announcing virtual delays to customers upon their arrival.

There are two parts of findings from the results obtained from the case study and a numerical study of simulation comparisons. First, using an H₂ distribution for the abandonment distribution is driven by an empirical study which shows its good fit to real-life call center data. Second, simulation experiments indicate that the model and approximation are reasonable, and the state-dependent Markovian approximation works very well for call centers with larger pooling. It is concluded that our approach can be applied in a voice response system of real call centers.

Many results pertain to announcing delay information, customer reactions and links to estimating hyperexponential distribution based on real data that have not been established in previous studies; however, this paper analytically characterizes these performance measures for delay announcements.

This article examines the underlying paradigm of the total management process of productivity so as to observe the underlying drives. Given the variety of conditions prevailing in developing countries, an organisation may take a number of concepts and alternatives to improve its ability to maintain values and to create values for the future. It has been hypothesised that productivity may be enhanced by recognising the need to maximise the availability of organisational assets.

Work sampling focuses mainly on determining the proportion of times for a specified category within a predetermined tolerance at a specified statistical risk. Conventional practices are based on numerous snap (instantaneous) observations taken randomly. This study employs the extensively used method of “production study” or so‐called continuous observation work sampling (COWS). Data from continuous observation of activity are used to make point estimates of the average time spent in each category and also the proportion of time occupied by a specified category. This study concentrates mainly on determining and verifying the interval estimates that derived from two different process assumptions, alternating Poisson process (APP) and alternating unspecified process (AUP), for a proportion when using COWS. Simulation results indicate that the confidence interval formulae derived for AUP assumptions are robust if the sample sizes for both modes exceed 100. Although the exact formulae are derived from APP, poor results yield if the true process deviated from APP.

The purpose of this paper is to estimate the required number of robots consisting of some non-repairable components, by employing a renewal model. Considering the importance of the availability of standby autonomous robots for reducing and preventing down-times of advanced production systems, which imposes a considerable loss, the present research tries to introduce a practical model for the determination of the required number of autonomous robots.

Most of the available research on the estimation of the required standby components based on the reliability characteristics of components has not considered the environmental factors influencing the reliability characteristics. Therefore, such estimations are not accurate enough. In contrast, this paper focuses on the influence of the environmental and human factors (e.g. the operators’ skill) on the robot reliability characteristics.

A model based on the Weibull renewal process combined with the cold standby strategy is developed for reliability evaluation of the system. The effectiveness of the proposed integrated reliability evaluation model is worked out in some cases.

Determining a required number of robots is an important issue in availability and utilization of a complex robotic production system. In an advanced production system, while the estimation process of a required number of robots can be performed through different approaches, one of the realistic estimation methods is based on the system’s reliability that takes into consideration the system operating environment. To forecast the required number of robots for an existing production system, in some cases, the assumption of a constant failure rate does not differ much from the assumption of a non-constant failure rate and can be made with an acceptable error.

In this paper, the authors elaborate the notion of a hybrid factory: factories in which some production capacity is dedicated to process‐oriented manufacturing, and some is dedicated to product‐oriented manufacturing. We present the results of a field study of one such factory and, using it as an example, develop a list of eight issues that may arise as a part of implementing and managing a hybrid factory. Where possible, we tie these issues into existing research streams. We show how our field site addressed these issues and, based on their experience, suggest new areas for investigation by researchers. An examination of the way our field site implemented and managed a hybrid factory also provides insight for managers facing similar situations.

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.