A branch-and-cut algorithm for quadratic assignment problems based on linearizations

The quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems known. Exact solution attempts proposed for instances of size larger than 15 have been generally unsuccessful even though successful implementations have been reported on some test problems from the QAPLIB up to size 36. In this study, we focus on the Koopmans–Beckmann formulation and exploit the structure of the flow and distance matrices based on a flow-based linearization technique that we propose. We present two new IP formulations based on the flow-based linearization technique that require fewer variables and yield stronger lower bounds than existing formulations. We strengthen the formulations with valid inequalities and report computational experience with a branch-and-cut algorithm. The proposed method performs quite well on QAPLIB instances for which certain metrics (indices) that we proposed that are related to the degree of difficulty of solving the problem are relatively high (?0.3$?0.3$). Many of the well-known instances up to size 25 from the QAPLIB (e.g. nug24, chr25a) are in this class and solved in a matter of days on a single PC using the proposed algorithm.     

Changing the Status Quo in Environmental Engineering Education in Response to Emerging Markets

The job market and skill needs have been expanding for environmental engineers requiring more interdisciplinary training and global citizenship. The 1970s was a decade of regulatory activities, the 1980s was a decade of major environmental disasters, the 1990s was a decade for global awakening, and the first decade of the 21st century is becoming the decade of concern for increasing global environmental stress. In parallel with the environmental trends, the environmental engineering programs have evolved from strictly water/wastewater focus to interdisciplinary programs with a wide selection of courses such as computer science, meteorology, aquatic biology, and ecology in addition to the classical environmental engineering curriculum. Advancements in science and technology, changing demographics, new delivery structures, changes in educational programs and policies, new regulatory requirements, increased global interactions, and recent large scale events with significant environmental impacts have increased the needs for engineers who are trained in environmental engineering discipline with adequate skills for addressing the emerging challenges. This technical note presents the emerging job markets and the corresponding skill needs for environmental engineers to respond to current and evolving environmental challenges both at regional and global scales.     

Improving transient performance of discrete-time model reference adaptive control architectures

Discrete-time adaptive control algorithms can be executed directly in embedded code unlike their continuous-time counterparts, which require discretization. However, their designs predicated on quadratic Lyapunov-based frameworks are quite intricate due to the resulting complexity in the Lyapunov difference expressions. Therefore, a wide array of available continuous-time results addressing transient performance issues using adaptive control algorithms cannot be applied or readily extended to the discrete-time case. In this article, we present a new model reference adaptive control architecture for discrete-time uncertain dynamical systems. Specifically, the proposed architecture consists of a command governor mechanism that adjusts the trajectory of a given command during the closed-loop transient response. It is shown that this mechanism is effective in improving transient performance of discrete-time model reference adaptive control architectures. Using a logarithmic Lyapunov function, we prove Lyapunov stability of the closed-loop system as well as asymptotic convergence of the system error states involving the difference between the states of the uncertain dynamical system and the states of the reference model, as well as driving the command governor signal to zero.     

Biofouling affinity of membrane surfaces under quiescent conditions

Biofouling affinity of four thin film membranes was evaluated under quiescent conditions by exposing the membrane to the effluent from a membrane bioreactor. Coupons of the thin film membranes were left for 5 days in the bioreactor effluent which was filtered through a 0.2 micron filter. The surface morphologies of the membranes before and after the exposure were evaluated by atomic force microscopy (AFM). All membranes showed significant amounts of deposits after the exposure. The easily disturbed nature of the deposits on the polyamide membrane indicated that these deposits were either entrapped within the surface crevices or loosely attached on the membrane. The extent of surface coverage and uniform appearance of the deposits on the polysulfone membrane were indicative of molecular adhesion by functional groups on the membrane surface. Based on the wetting angle measurements, all clean membranes exhibited hydrophilic characteristics. After exposure to the bioreactor effluent, the membranes exhibited complete wetting characteristics. The use of AFM image analysis technique provided insight for the morphological changes and substrate accumulation patterns on membrane surfaces.     

Development of a component-based machining centre selection model using AHP

Machining centres are widely used in manufacturing companies all over the world. Since investments in machining centres are long-term and expensive, selection of the most appropriate machining centre is an important decision for manufacturing companies. There has been considerable effort spent in developing crisp and fuzzy multi-criteria decision-making (MCDM) models that use technical specifications provided by machining-centre manufacturers such as, axis size, power, spindle speed, tolerance, repeatability, cutting-tool change time, and number of cutting tools along with other economical and commercial factors. However, the technical specifications are directly taken from machining-centre manufacturers’ catalogues without checking their correctness, adequacies, or ability to represent the areas that are used for measurement. In such a case, one cannot be sure whether the outcomes are sound or not without a detailed check of the technical specifications, which can only be performed after actual usage of the machine itself. To overcome all such problems, an analytic hierarchy process (AHP) model that evaluates the machining centre components is developed in this paper. The new component-based AHP model is then compared with two MCDM models that use only technical specification values.     

Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network

Genetically optimized neural network systems (GONNS) was developed to simulate the intelligent decision-making capability of human beings. After they are trained with experimental data or observations, GONNS use one or more artificial neural networks (ANN) to represent complex systems. The optimization is performed by one or more genetic algorithms (GA). In this study, the GONNS was used to estimate the optimal operating condition of the friction stir welding (FSW) process. Five separate ANNs represented the relationship between two identical input parameters and each one of the considered characteristics of the welding zone. GA searched for the optimized parameters to make one of the parameters maximum or minimum, while the other four are kept within the desired range. The GONNS was found as an excellent optimization tool for FSW.