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Enhanced risk management through the application of mathematical optimization is the next competitive‐advantage frontier for the primary‐insurance industry. The widespread adoption of catastrophe models for risk management provides the opportunity to exploit mathematical optimization techniques to achieve superior financial results over traditional methods of risk allocation. The purpose of this paper is to conduct a numerical experiment to evaluate the relative performances of the steepest‐ascent method and genetic algorithm in the solution of an optimal risk‐allocation problem in primary‐insurance portfolio management.

The performance of two well‐established optimization methods – steepest ascent and genetic algorithm – are evaluated by applying them to solve the problem of minimizing the catastrophe risk of a US book of policies while concurrently maintaining a minimum level of return.

The steepest‐ascent method was found to be functionally dependent on, but not overly sensitive to, choice of initial starting policy. The genetic algorithm produced a superior solution to the steepest‐ascent method at the cost of increased computation time.

The results provide practical guidelines for algorithm selection and implementation for the reader interested in constructing an optimal insurance portfolio from a set of available policies.

Additive manufacturing has disadvantages, such as the maximum part size being limited by the machine’s working volume. Therefore, if a part more considerable than the working volume is required, the part is produced in parts and joined together. Among the many methods of joining thermoplastic parts, adhesives and mechanical interlocking are considered. This study aims to characterize and optimize mechanically stressed adhesive joints combined with female and male mechanical interlocking on acrylonitrile butadiene styrene (ABS) specimens made with fused filament fabrication (FFF) so that the joint strength is as close as possible to the strength of the base material.

This study characterized the subject’s state of the art to justify the decisions regarding the experimental design planned in this research. Subsequently, this study designed, executed and analyzed the experiment using a statistical analysis of variance. The output variables were yield strength and tensile strength. The input variables were two different cyanoacrylate adhesives, two different types of mechanical interlock (truncated pyramid and cylindrical pin) and the dimensions of each type of mechanical interlock. This study used simple and factorial experiments to select the best adhesive and interlocking to be optimized using the response surface and the steep ascent method.

The two adhesives have no statistical difference, but they show different data dispersion. The design or yield stress was a determining factor for selecting the optimal specimen, with cylindrical geometry exhibiting higher resistance at initial failure. Geometry type is crucial due to the presence of stress concentrators. The cylindrical geometry with fewer stress concentrators demonstrated better tensile strength. Ultimately, the specimen with a mechanically reinforced joint featuring a cylindrical pin of radius 5.45 mm and height of 4.6 mm exhibited the maximum tensile and yield strength.

Previous research suggests that a research opportunity is the combination of bonding methods in FFF or fused deposition modeling, which is not a frequent topic, and this research to enrich that topic combines the adhesive with mechanically interlocked joints and studies it experimentally for FFF materials, to provide unpublished information of the performance of the adhesive joint with mechanical interlocking, to designers and manufacturers of this technology.

The purpose of this study is to develop a practical method for training students how to conduct statistical analysis and do a course project in design of experiments (DOEs) course through the Web-based virtual catapult simulation.

A step-by-step sequential DOE process for investigating the effects of controllable factors on quality characteristic responses was presented as a guideline for conducting a DOE course project. Each team was assigned to create an innovative teaching material and work on the term report by following the recommended guidelines for designing experiments through the Web-based virtual catapult simulation. Hypothesis was defined to test whether doing a course project based on this approach would impact students’ learning outcome.

The Web-based virtual material was an alternative technique for interactive teaching that could improve students’ understanding and achievement in DOE course projects. There was a significant difference in student learning and understanding before and after doing on the course project through the Web-based virtual catapult simulation. The students had improved communication and teamwork skills after following the recommended procedure for practicing DOEs.

Most students could effectively conduct designing experiments, carry out designed experiments, analyze data and gain valuable teamwork experience. After learning the DOE approach based on the catapult simulation, they enjoyed working on their course projects deploying to the innovative toys and other real-life situations with real measurements.

The use of Web-based virtual material, including catapult simulation, was an alternative technique for interactive DOE teaching to improve the students’ understanding and achievement in DOE course projects.

Implementing enterprise resource planning (ERP) is a challenging task for small- and medium-sized enterprises (SMEs). The purpose of this paper is to develop an integrated decision support system (DSS) for ERP implementation (DSS_ERP) to facilitate resource allocations and risk analysis.

Analytical regression models are developed using data collected through a survey conducted on 400 SMEs that have implemented ERP systems, and are validated by a simulation model. The validated analytical regression models are used to construct a nonlinear programming model that generates solutions for resource allocations, such as time and budget.

ERP implementation cost increases along the time horizon, while performance level increases up to a point and remains unchanged. To maximise or achieve a certain level of performance within a budget limitation, CSFs are prioritised as: project management (highest), top management, information technology, users and vendor support (lowest). SMEs are recommended to concentrate effort and resources on CSFs that have a greater impact on achieving their desired goals while optimising utilisation of resources.

DSS_ERP proves to be beneficial to SMEs in identifying required resources and allocating resources, but could be further tested in case studies for its practical use and benefits.

DSS_ERP serves as a useful tool for SMEs to predict required resources and allocate them prior to ERP implementation, which maximises the probability of achieving predetermined targets. It also enables SMEs to analyse risk caused by changes to resources during ERP implementation, and helps them to be better prepared for the risks.

The research contributes to the scarce research on ERP implementation using scientific methods. A novel nonlinear programming model is constructed for ERP implementation under time and budget limitations, facilitating resource allocations in an ERP implementation, which has not been reported in any previous research. The research offers a theoretical basis for empirical studies of resource allocations in ERP implementation.

IN wishing all our readers happiness and prosperity throughout 1973 we are very conscious of the fact that it is a climateric year for the people of these islands. As these words are read we shall be a part of the European Economic Community.

The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.

Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic.

The shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure.

A new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.

Experimental design methods are tools for conducting informative, time‐ and cost‐effective experiments. Used during product development, these methods can contribute to building quality into products as well as shortening the development cycle time. These techniques make it possible to study the effect of many factors (parameters) simultaneously, to select the factor combination that results in both improved quality and reduced cost, and hence allow for the development of reliable and robust products of high quality. In addition, these methods provide a systematic approach for problem solving during the product development process. This article provides a non‐technical discussion of the role of experimentation and the advantage of using experimental design during product development. Different experimental design methods and examples of their application during product development will also be presented.

The purpose of this paper is to integrate resilience, robustness, and resplendence (R 3) with sustainable enterprise excellence (SEE) and social-ecological innovation (SEI) that assist firms to progress toward continuously relevant performance proceeding from continuously responsible strategy, behavior, and other actions.

Sustainable enterprise excellence, resilience, robustness, and resplendence (SEER3) model and the associated means of SEER 3 maturity assessment are introduced to explain the organizational concept.

SEER3 balances the complementary and competing interests of key stakeholder segments, including society and the natural environment and increases the likelihood of superior and sustainable competitive positioning and hence long-term enterprise success that is defined by continuously relevant and responsible governance, strategy, actions, and performance consistent with high-level organizational R3.

This paper adapts the established principles from physics to characterize enterprise R3 to come up with SEE model.

Planning an accelerated life test (ALT) for a product is an important task for reliability practitioners. Traditional methods to create an optimal design of an ALT are often computationally burdensome and numerically difficult. In this paper, the authors introduce a practical method to find an optimal design of experiments for ALTs by using simulation and empirical model building.

Instead of developing the Fisher information matrix-based objective function and analytic optimization, the authors suggest “experiments for experiments” approach to create optimal planning. The authors generate simulated data to evaluate the quantity of interest, e.g. 10th percentile of failure time and apply the response surface methodology (RSM) to find an optimal solution with respect to the design parameters, e.g. test conditions and test unit allocations. The authors illustrate their approach applied to the thermal ALT with right censoring and lognormal failure time distribution.

The design found by the proposed approach shows substantially improved statistical performance in terms of the standard error of estimates of 10th percentile of failure time. In addition, the approach provides useful insights about the sensitivity of each decision variable to the objective function.

More comprehensive experiments might be needed to test its scalability of the method.

This method is practically useful to find a reasonably efficient optimal ALT design. It can be applied to any quantities of interest and objective functions as long as those quantities can be computed from a set of simulated datasets.

This is a novel approach to create an optimal ALT design by using RSM and simulated data.

A new multivariate heavy-tailed distribution is proposed as an extension of the univariate distribution of Politis (2004). The properties of the new distribution are discussed, as well as its effectiveness in modeling ARCH/GARCH residuals. A practical procedure for multi-parameter numerical maximum likelihood is also given, and a real data example is worked out.