Concrete Overstrength: Assessment of Field Strength Seeking Insights for Overdesign Optimization

This study investigates the high contents of cementitious materials in Portland cement concrete and assesses the required (f’cr) and actual (σ) compressive strength of concrete specimens. A linear optimization technique identifies the required binder content to reach f’cr. Standard specifications have required concrete overdesign (OD) for decades, but few studies have evaluated the actual magnitude of OD from field data. The compressive strength of 958 cylinders prepared in the field represented 8200 m3 of ready-mixed concrete with 300 and 450 kg/m3 of cementitious are analyzed. The actual OD appears to be 7 to 21% higher than required. The required 28-day compressive strength of concrete was achieved in less than seven days. Therefore, the content of the cementitious materials could be reduced by 6 and 17% so that concrete could reach f’cr without cementitious overconsumption. Reducing cementitious content is recommended to improve construction quality and optimize resource utilization. Among the main reasons for this recommendation are the estimated substantial long-term savings, increased concrete durability and more rational use of natural resources required to build the structures.

New Algorithm to Solve Mixed Integer Quadratically Constrained Quadratic Programming Problems Using Piecewise Linear Approximation

Techniques and methods of linear optimization underwent a significant improvement in the 20th century which led to the development of reliable mixed integer linear programming (MILP) solvers. It would be useful if these solvers could handle mixed integer nonlinear programming (MINLP) problems. Piecewise linear approximation (PLA) is one of most popular methods used to transform nonlinear problems into linear ones. This paper will introduce PLA with brief a background and literature review, followed by describing our contribution before presenting the results of computational experiments and our findings. The goals of this paper are (a) improving PLA models by using nonuniform domain partitioning, and (b) proposing an idea of applying PLA partially on MINLP problems, making them easier to handle. The computational experiments were done using quadratically constrained quadratic programming (QCQP) and MIQCQP and they showed that problems under PLA with nonuniform partition resulted in more accurate solutions and required less time compared to PLA with uniform partition.

Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods

Inverse optimization—determining parameters of an optimization problem that render a given solution optimal—has received increasing attention in recent years. Although significant inverse optimization literature exists for convex optimization problems, there have been few advances for discrete problems, despite the ubiquity of applications that fundamentally rely on discrete decision making. In this paper, we present a new set of theoretical insights and algorithms for the general class of inverse mixed integer linear optimization problems. Specifically, a general characterization of optimality conditions is established and leveraged to design new cutting plane solution algorithms. Through an extensive set of computational experiments, we show that our methods provide substantial improvements over existing methods in solving the largest and most difficult instances to date.

Multiple Dependent State Sampling Inspection Plan for Lindley Distributed Quality Characteristic

This article develops multiple dependent state (MDS) sampling inspection plans based on the mean of lifetime quality characteristic that follows non-normal distributions viz., exponential and Lindley distribution. In this plan, the lot quality is measured by the lot mean (𝜇). We have estimated the optimal plan parameters of the proposed technique by non-linear optimization approaches considering acceptable quality level and rejection quality level. We have compared the sample size between the MDS sampling inspection plan and the single sampling inspection plan for the variable. Finally, we have taken two examples to illustrate the proposed technique.

Combined optimisation and MCDA based solution of the tram depot location problem

This paper deals with an issue of technical facilities location in a public transport system. The decision problem is formulated as a selection of the most advantageous alternative, i.e. the location of a new tram depot among the already existing facilities of this type. The selection is preceded by the evaluation of the alternatives. The assessment is not a trivial task, because there are many groups of interest with usually contradictory points of view. Therefore, the evaluation of the new tram depot locations should represent different aspects, e.g., economical, technical, environmental, and organizational. To handle such a complex decision problem the authors propose a methodology, which is a composition of the optimisation and multiple criteria evaluation techniques. The developed methodology is experimentally applied to the selection of one out of five tram depot locations in the public transport system of the city of Poznan, Poland. All the computational experiments are performed by means of optimization and multiple criteria decision aiding (MCDA) methods and tools, i.e. a linear optimization engine Solver Premium Platform and AHP method with its application AHORNsimple. The calculations are the basis for recommending the location of a new depot in the central part of the transport system network, which is a reasonable solution taking into account, e.g. the proximity of the main railway line, the possibility of triple distribution of the transport means from depot. The proposed methodology of the decision problem solution gives also an opportunity to create the hierarchy of considered tram depot locations as well as to compare the position in the ranking of the best solution with the existing one. Since the proposed methodology assumes the selection of the most suitable MCDA method to the problem under consideration and the decision maker’s preferen¬ces, it guarantees that the result of analysis becomes reliable and the decision aiding process is credible.

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters

We present an algorithm for simultaneous retrieval of aerosol and marine parameters in coastal waters. The algorithm is based on a radiative transfer forward model for a coupled atmosphere-ocean system, which is used to train a radial basis function neural network (RBF-NN) to obtain a fast and accurate method to compute radiances at the top of the atmosphere (TOA) for given aerosol and marine input parameters. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve three marine parameters (concentrations of chlorophyll and mineral particles, as well as absorption by coloured dissolved organic matter (CDOM)), and two aerosol parameters (aerosol fine-mode fraction and aerosol volume fraction). We validated the retrieval algorithm using synthetic data and found it, for both low and high sun, to predict each of the five parameters accurately, both with and without white noise added to the top of the atmosphere (TOA) radiances. When varying the solar zenith angle (SZA) and retraining the RBF-NN without noise added to the TOA radiance, we found the algorithm to predict the CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction with correlation coefficients greater than 0.72, 0.73, 0.93, 0.67, and 0.87, respectively, for 45∘≤ SZA ≤ 75∘. By adding white Gaussian noise to the TOA radiances with varying values of the signal-to-noise-ratio (SNR), we found the retrieval algorithm to predict CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction well with correlation coefficients greater than 0.77, 0.75, 0.91, 0.81, and 0.86, respectively, for high sun and SNR ≥ 95.

Gramian-constrained optimization process for the stiffness model identification of industrial manipulators

This work deals with the elastostatic identification of industrial manipulators. By reviewing the basics of the physical elastic properties of both links and joints in the framework of the lumped stiffness modeling techniques, the Gramian nature of the stiffness matrices has been found out adequate to do so. Then, a novel optimization method has been developed, which incorporates the Gramian matrix formulation along a non-linear optimization process, acting as an intrinsic constraint for the conservativeness of the elastostatic modeling. Numerical and experimental analyses evince the effectiveness of the proposed method, as the elastostatic models obtained by means of the proposed technique predict more than 93.7% of the compliance deviations of a real industrial robot. The proposed method is simple enough to be jointly applicable to the most recent elastostatic model reduction techniques.

A Global Optimization Algorithm for Unconstrained Non-linear Optimization Problems Using Chaotic Neuron Map and Golden Number

This paper presents aninnovative global multi-variable optimization algorithm using one of the best chaotic sequences, the neuron map, a description of which is also provided in the paper. The algorithm uses neuron map in the first stage to move near the global minimum point, as well as in each iteration of the second stage of local search that is done using the N-dimensional golden section search algorithm. The generation and mapping of the neuron variables to the optimization variables along with the stagewise search for the global minimum is explained conscientiously in the work. Numerical results on some benchmark functions and the comparison with a latest state-of-the-art algorithm ispresented in order to demonstrate the efficiency of the proposed algorithm.

TDCA: Improved Optimization Algorithm with Degree Distribution and Communication Traffic for the Deployment of Software Components Base on AUTOSAR Architecture

AUTOSAR (Automotive Open System Architecture), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and balancing ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture.