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Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

The Spotted Hyena Optimization (SHO) algorithm is inspired by simulating the predatory behavior of spotted hyenas. While the mathematical model of the SHO algorithm is simple and optimal, it is easy to fall into local optimization and causes premature convergence compared to some metaheuristic algorithms. To the end, we propose an enhanced Spotted Hyena Optimization algorithm, a hybrid SHO algorithm using Elite Opposition-Based Learning coupled with the Simplex Method called EOBL-SM-SHO. The EOBL-SM-SHO algorithm combines the characteristics of the simplex method’s geometric transformations (reflection, inside contraction, expansion, and outside contraction) with more practical information on elite opposition-based learning strategy. They can significantly strengthen the SHO algorithm’s search range and augment the hyena population’s diversity. Furthermore, we employ eleven benchmark functions and three engineering design issues to gauge the effectiveness of the EOBL-SM-SHO algorithm. Our extensive experimental results unveil that EOBL-SM-SHO achieves better accuracy and convergence rate than the state-of-the-art algorithms (e.g., Artificial Gorilla Troops Optimizer (GTO), Cuckoo Search (CS), Farmland Fertility Algorithm (FFA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Spotted Hyena Optimizer (SHO)).

In this work, a methodology that uses the dynamic Bayesian networks (DBNs) in combination with an idea algebra is developed for assessing the dynamic reliability of engineering systems. A network representation of the system topology is first introduced in the form of “idea” objects representing components and their functional interfaces, thus integrating the functional and material descriptions of the system. Various time-dependent functionalities can thus be mapped to segments or loops of the resulting network, which are then translated automatically into the form of a DBN, thereby avoiding the need to manually generate the dynamic fault tree (DFT) logic that would normally serve as a starting point. The methodology is demonstrated in a case study, where reliability analysis of an automobile system is performed. The idea algebra is automatically deployed in Mathematica and evaluated in the GeNIe platform. Weibull distribution was used for the generation of the dynamic values for the reliability analysis of the system within a certain period.

In this paper a new generalization of the Wilcoxon test is proposed for the comparative reliability evaluation of alternative engineering designs. Using Monte Carlo simulations the performance of the new test is assessed against the results provided by four other two-sample tests for censored reliability data, i.e., the Gehan-Wilcoxon, Peto-Prentice, Logrank and modified Kolmogorov-Smirnov tests. The results of the simulations suggest that the Wilcoxon alternatives based on weight functions that are reliability function estimates, such as the Peto-Prentice and the Bohoris-Wilcoxon, are the most preferable for routine use.

This paper considers the role of lean product development (LPD) as part of a company's overall innovation strategy. Our discussion contends that the value-concept in LPD is too strongly tied to product features, and that this may lead to: (i) overemphasis on utilitarian value, (ii) preference for reliability over validity, and (iii) a defensive approach to market-trends. These factors can compromise the philosophy's ability to maximize customer value, and represent a challenge in making LPD fit beyond incremental innovation in the technological domain. Lean innovation (LI) is therefore introduced as an extension of LPD, building on a value concept that embraces emotional as well as utilitarian characteristics. It is suggested that lean principles should not be limited to product development, but should concern all aspects of a company's innovation efforts relevant to offering a pleasurable customer experience.

A need assessment exercise at various resource-limited hospitals in Ghana revealed that a conventional method of monitoring uterine contractions is employed. This method is time consuming and ineffective with a likelihood of misrepresenting data on uterine contractions. There is therefore a need for a system that can potentially overcome the identified challenges. In this paper, the authors present the proof of concept for development of an automated uterine contraction monitoring system designed for use in resource-limited settings. Following the engineering design process, data were gathered to draft product specifications. Various concepts were evaluated and a mathematical model of chosen concept was built and simulated. A functional prototype was constructed to test the system’s ability to measure the frequency and average duration of muscle contractions over a specified interval. The results indicate the capability of the chosen concept to meet design specifications. The design can also be enhanced to provide the intensity of contractions.

Ecological impact of sanitary landfill is discussed in this chapter. Landfill design, leachate control, and landfill gas (LFG) management are developed and their applications are illustrated. Landfill pollution control, collection, and treatment of LFG, and utilization of LFG energy are discussed in detail. Cost data and practical examples for planning, LFG management, and operation are presented.

Thousands of elementary school students participate in Junior Botball Challenge exercises every year. These challenges require students to write programs for their robots and to supplement their basic robots with effectors to carry out the challenge task. This paper presents data gathered from some of the schools that have participated, with a focus on those that did NOT select students based on their interest or ability. It shows that a large percentage of typical elementary school students are able to write working C programs (when given appropriate instruction) that exhibit sequential steps and timing.

The aim of our job is to deal with the problem that experience dependence situation is often occur during the contradiction formulization stage at the beginning of innovation by using TRIZ. The cause of problem mention above is analyzed, the deficiencies of classical TRIZ is descript. Furthermore, ENV model of OTSM-TRIZ is introduced, as well as contradiction's overcoming framework based on ENV model (ENV framework). Finally, a contradiction overcoming flowchart based on ENV model is proposed. The proposal flowchart supports the problem expression and decomposition according to ENV model, and guides the principles concreting process based on ENV framework. A threaded-shaft design case is used to validate the proposal method.