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• Determining the urban critical infrastructures in the event of an earthquake. • Designing Location-Routing-Inventory decisions in humanitarian relief chain. • Simulation of interaction of urban infrastructure in earthquake conditions. • Using a cooperative game to examine the manner of cooperation of disaster relief supply chain players. • Application of the model to a real-world disaster relief logistic case study. This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators' vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance. [ABSTRACT FROM AUTHOR]

Sugarcane is one of the main commodities in the world economy. An important step in the sugarcane supply chain is the harvest stage, requiring the support of mathematical modeling and optimization tools. Integrating the process of choosing the areas for harvesting according to the sugarcane maturation with the formation of the harvesting fronts and associating them with machine relocation costs is challenging. This paper contributes with a novel integer mathematical model that considers these concerns using three conflicting objectives. The first is that the sugarcane should be harvested as close as possible to its maturity, considering the sugarcane should be harvested as close as possible to its maturity by considering a combined measure of temperature and time since planting. The second aims to minimize the number of harvest fronts. The third objective is to minimize the costs involved with the relocation of the harvesters that cut the cane. Different strategies for obtaining Pareto-optimal solutions were proposed using exact methods, including MIP start and a decomposition technique. In the worst case, solutions with tolerable integrality gaps were obtained (inferior to 3%) in less than 1.5 h. Computational tests in real case studies were conducted in the sugarcane industry in two countries, indicating strong trade-offs in the determined solutions. In the Brazilian case, harvesting as close to maturation as possible, for example, causes an increase of 158% in the number of harvest fronts and 47% in the transportation costs, considering the minimum values for these objectives. Other trade-offs were seen and discussed when one of the other objectives was prioritized. These different compromise solutions help the decision-makers choose the most appropriate harvesting schedule according to their preferences. • We propose a new MIP optimization model to determine the harvesting of sugarcane. • The model aims to optimize three conflicting objectives. • A new method of computing cane maturation is used: the concept of degree-days. • A procedure for determining Pareto solutions by exact methods is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a production and differential pricing decision model in a two-echelon supply chain that involves a demand from two or more market segments. In this framework, the retailer is allowed to set different prices during the planning horizon. While integrated production-marketing management has been a key research issue in supply chain management for a long time, little attention has been given to set prices and marketing expenditures in integrated multi-site (parallel) manufacturing systems and multiple demand classes. Generally, the presence of multiple demand classes induced by different market segments may impose demand leakage and then change production plan and ordering policies throughout the supply chain system. To tackle this problem, this paper develops a novel approach in order to provide an optimal aggregate production and marketing plan by interconnecting the sales channels of the retailer and demand. A non-linear model is established to determine optimal price differentiation, marketing expenditures and production plans of manufacturing sites in a multi-period, multi-product and multi-sale channels production planning problem by maximizing total profit of the supply chain. To handle the model and obtain solutions, we propose an efficient analytical model based upon convex hulls. Finally, we apply the proposed procedure to a clothing company in order to show usefulness and significance of the model and solution method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for controlling the operational ventilation in complex road tunnels. Basically, the control structure is feed-forward with an adaptive logic. The feed-forward part includes the static optimization which uses the mathematical model of airflow dynamics. The adaptive logic is based on the recursive least squares with exponential forgetting, which compensates deviations between the mathematical model and real measured data, and thus provides a feedback. During the standard operation of road tunnels, requirements to maintain indoor air quality (IAQ) and protect the surrounding area of a tunnel from pollution, are necessary. Moreover, reducing electricity costs is desirable, as the ventilation in road tunnels forms a significant part of electricity costs. The control scheme has been validated through the long-term evaluation of operation in the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe. The experimental validation of the proposed control scheme is the main contribution of the paper. [ABSTRACT FROM AUTHOR]

Determination of the creep model parameters is a challenging task particularly when a non-linear elastic visco-plastic (EVP) model is adopted, mainly due to the limited test duration as well as the assumption of the reference time. Therefore, this paper presents an innovative numerical solution to find the EVP model parameters applying the trustregion reflective least square optimization algorithm. The developed approach involves several available laboratory consolidation test results in the optimization procedure with the adopted commencing time to creep as a unit of time. In this paper, the laboratory results of Ottawa clay were employed to demonstrate the limitation of the recent method to obtain model parameters. Furthermore, the developed method is verified against Skå-Edeby clay in the laboratory conditions. The EVP model parameters are obtained by applying the developed method to the available laboratory consolidation results of clay samples. The analysis results of vertical strains and excess pore water pressures demonstrate that the developed method can be a feasible tool to estimate the settlement properties of clays. [ABSTRACT FROM AUTHOR]

This paper explores the use of data-driven approximation algorithms, often called surrogate modeling, in the early-stage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more in-depth exploration of a design space and reduce computational time of optimization algorithms. While this approach has been widely developed and used in related disciplines such as aerospace engineering, there are few examples of its application in civil engineering. This paper focuses on the general use of surrogate modeling in the design of civil structures and examines six model types that span a wide range of characteristics. Original contributions include novel metrics and visualization techniques for understanding model error and a new robustness framework that accounts for variability in model comparison. These concepts are applied to a multi-objective case study of an airport terminal design that considers both structural material volume and operational energy consumption. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Highlights • A time-space Kriging-based sequential metamodeling approach is proposed for multi-objective crashworthiness optimization. • The structural mechanical responses of sensitive time-space blocks are employed to reformulate crashworthiness criteria. • Adaptive addition of new samples is used to improve the Kriging metamodel accuracy sequentially. • The proposed method can generate designs with better crashworthiness performances efficiently and accurately. Abstract A time-space Kriging-based sequential metamodeling approach is proposed for multi-objective crashworthiness optimization (MOCO) in this paper. By defining the novel time-space design criteria, the constructed metamodels for the optimization objectives include the characteristic mechanical responses with respect to both the structural space domain and crash time domain, compared to standard metrics with the extremum of the time history of the entire structure. The adaptive addition of new samples is performed to gradually improve the approximation accuracy during the optimization with the guidance of an adaptive weighted sum method. The effectiveness of the proposed method is demonstrated by investigating a multi-cell thin-walled crashworthiness design problem. Finally, its effectiveness in practical engineering is validated by the crashworthiness design for a vehicle under full-overlap frontal crash loadcase. [ABSTRACT FROM AUTHOR]

Highlight • This paper proposes an improved grasshopper optimization strategy (GOA) for continuous optimization. • We have applied the improved GOA successfully to the financial stress prediction problem. • Levy flight, Gaussian mutation and opposition-based learning are embedded in GOA. • The experimental results reveal the improved performance of the proposed algorithm. Abstract This study proposed an improved grasshopper optimization algorithm (GOA) for continuous optimization and applied it successfully to the financial stress prediction problem. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behavior of grasshoppers. This algorithm is proved to be efficient in solving global unconstrained and constrained optimization problems. However, the original GOA has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these shortcomings, an improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established. Firstly, Gaussian mutation is employed to increase population diversity, which can make GOA has stronger local search ability. Then, Levy-flight strategy was adopted to enhance the randomness of the search agent's movement, which can make GOA have a stronger global exploration capability. Furthermore, opposition-based learning was introduced into GOA for more efficient search solution space. Based on the improved GOA, an effective kernel extreme learning machine model was developed for financial stress prediction. As the experimental results show, the three strategies can significantly boost the performance of GOA and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others. [ABSTRACT FROM AUTHOR]

This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance; (ii) model parameters identification by supervised machine learning; (iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error. [ABSTRACT FROM AUTHOR]

In this paper, the premature failure of the integral droppers in high speed railway catenary in China was investigated. Series of microscopic characterization and analysis were conducted to identify the root causes of the unexpected failure. By numerically simulating the existing crimping behavior, distribution of the contact stress in the dropper was obtained. It was found that there was stress concentration at the dropper sliced end, which became the primary reason for the permanent distortion and damage on the droppers, and consequently resulted in the untimely final fracture with bending fretting fatigue acting. Through the failure analysis and numerical simulation, a scheme of the improvements in measuring the existing crimping was brought up. Meanwhile, a plan was also put forward to change the current crimping model and reduce the depth of indentation of clamp tube. Static tensile tests and bending fretting fatigue tests were carried out to evaluate the optional plans, which would help extend the service life of integral dropper in practice. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid algorithm of Nelder–Mead and scatter search algorithms called SSNM. Numerical results for some known test problems with varying dimensions from 2 to 100 variables prove its great performance for a bounded or constrained problem. This paper also has a considerable contribution to the joint economic lot sizing (JELS) problem literature. This problem is considered for a two-stage supply chain with price-sensitive demand for geometric, geometric-then-equal size and optimal shipment policies. Solution procedures are also developed which use the SSNM algorithm in their steps. These models and solution procedures are novel in the JELS literature. [ABSTRACT FROM AUTHOR]

The paper illustrates the redefinition of the concept of biorefinery and its application onto the biogas field. The proposed integrated framework merges in a synergistic way the second and third generation biorefineries with the aim of reducing the CO 2 emissions and maximizing the utilization of biomass potential. The specific focus of the paper is on the mathematical modelling of the main conversion steps of the lignocellulosic residuals into biosyngas by means of thermal treatment (pyrolysis, combustion, gasification) and, next, into biomethanol (heterogeneously catalyzed synthesis). The models are implemented in user-friendly programmes and adopted to estimate biomass conversion and process yield. A final study of sensitivity analysis is provided to open the way for the next process optimization based on detail models. [ABSTRACT FROM AUTHOR]

We construct and analyze robust strong stability preserving IMplicit-EXplicit Runge-Kutta (IMEX RK) methods for models of flow with diffusion as they appear in astrophysics, and in many other fields where equations with similar structure arise. It turns out that besides the optimization of the region of absolute monotonicity, some other properties of the methods are crucial for the success of such simulations. In particular, the models in our focus dictate to also take into account the step size limits associated with dissipativity, positivity of the stiff parabolic terms which represent transport by diffusion, the uniform convergence with respect to different stiffness properties of those same terms, etc. Furthermore, in the literature, some other properties, like the inclusion of a part of the imaginary axis in the stability region, have been argued to be relevant. In this paper, we construct several new IMEX RK methods which differ from each other by taking various or even all of these constraints simultaneously into account. It is demonstrated for some simple examples as well as for the problem of double-diffusive convection, that the newly constructed schemes provide a significant computational advantage over other methods from the literature. Due to their accumulation of different stability properties, the optimized IMEX RK methods obtained in this paper are robust schemes that may also be useful for general models which involve the solution of advection-diffusion equations, or other transport equations with similar stability requirements. [ABSTRACT FROM AUTHOR]

Surface blending is a useful operation in geometric design for rounding sharp edges or corners. Meanwhile, NURBS has already become the de facto industrial standard in existing CAD/CAM systems. Therefore, it is required to study how to blend two B -spline surfaces. However, two arbitrary B -spline surfaces (called base surfaces ) are hard to be blended with a B -spline surface (called blending surface ) because the knot vectors of the two base surfaces are usually mismatched. In this paper, we proposed a curve-based spline representation, i.e., the semi-structured B-spline surface , which is generated by skinning a series of B -spline curves with different knot vectors. By assigning suitable knot vectors to the head and tail skinned curves, the semi-structured B -spline surface can blend two B -spline surfaces smoothly without disturbing them at all. We formulated the B -spline surface blending problem as an optimization problem with continuity constraints, and the continuity between the base and blending surfaces can reach G 2 or C 2 . Examples illustrated in this paper validate the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

We consider a novel multi-server queueing system that is potentially useful for optimizing real-world systems, in which the objectives of high performance and low power consumption are conflicting. The queueing model is formulated and investigated under the assumption that an arrival flow is defined by a batch Markovian arrival process and random values characterizing customer processing have the phase-type distribution. If the service time of some customer by a server exceeds a certain random bound, this server receives help from a so-called backup server from a finite pool of backup servers. The behavior of the system is described by a quite complicated multi-dimensional continuous-time Markov chain that is successfully analyzed in this paper. Examples of the potential use of the obtained results in managerial decisions are presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original double flexible job-shop scheduling problem (DFJSP), in which both workers and machines are flexible. Because of the characteristics of double flexibility, DFJSP conforms to practical production better than the flexible job-shop scheduling problem (FJSP). In addition, a multi-objective optimization mathematic model according to the DFJSP is proposed, which is concerned with the processing time indicator that is usually optimized by most existing studies; green production indicators, namely, factors regarding environmental protection; and human factor indicators, which are actual indispensable elements that exist in the production system. Furthermore, ten benchmarks of DFJSP are presented and solved using a newly proposed hybrid genetic algorithm (NHGA). With the proposed NHGA, a new well-designed three-layer chromosome encoding method and some effective crossover and mutation operators are developed. To obtain the best combination of key parameters in NHGA, the Taguchi design of experiment method is used for their evaluation. Finally, comparisons between NHGA and NSGA-II show that the proposed NHGA has advantages in terms of the solving accuracy and efficiency of the DFJSP, particularly at a large scale. It would be beneficial to apply our proposed model to the multi-objective optimization of scheduling problems, especially those considering human factor and green production-related indicators. [ABSTRACT FROM AUTHOR]

Oil and gas companies play an important role in the global economy since they supply a large portion of the necessary energy to the world. The optimal production of oil and gas should be performed in an integrated fashion for the whole supply chain. The downstream oil and gas supply chain (OGSC) has attracted the interest of many researchers due to its central role in the world economy. This paper develops an integrated multi-objective OGSC model for medium-term tactical decision making for the OGSC downstream segment. The selected objectives related to downstream activities are the following: minimize the total cost, maximize the total revenue, and maximize the service level. The model includes multi-period and multi-product inputs. The model is verified and solved using an improved augmented ε-constraint algorithm to generate Pareto optimal solutions. The model assists in assessing various trade-offs among different objectives and guides decision makers for the effective management of the downstream OGSC. The utility of the proposed model is demonstrated using a real case from a Saudi Arabian downstream OGSC. Sensitivity analysis is conducted to investigate the effects of input parameters on the set of Pareto optimal solutions. The model is expected to have a positive impact on the future management of this important component of the energy sector. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study. [ABSTRACT FROM AUTHOR]

In the evidential reasoning approach of decision theory, different evidence weights can generate different combined results. Consequently, evidence weights can significantly influence solutions. In terms of the “psychology of economic man,” decision-makers may tend to seek similar pieces of evidence to support their own evidence and thereby form alliances. In this paper, we extend the concept of evidential reasoning (ER) to evidential reasoning based on alliances (ERBA) to obtain the weights of evidence. In the main concept of ERBA, pieces of evidence that are easy for decision-makers to negotiate are classified in the same group or “alliance.” On the other hand, if the pieces of evidence are not easy to negotiate, they are classified in different alliances. In this study, two negotiation optimization models were developed to provide relative importance weights based on intra- and inter-alliance evidence features. The proposed models enable weighted evidence to be combined using the ER rule. Experimental results showed that the proposed approach is rational and effective. [ABSTRACT FROM AUTHOR]

• Establishing the mathematical models for the optimization problem of the CBM well maintenance tasks scheduling. • The Q-learning based algorithm for the CBM well maintenance tasks scheduling is proposed. • The proposed Q-learning based algorithm can solve the optimization problem with relatively large number of maintenance tasks. • The proposed Q-learning based algorithm can meet the real time requirements for the CBM well maintenance tasks scheduling. The Coalbed Methane (CBM) well maintenance tasks scheduling optimization is of importance for improving the CBM production efficiency. Traditionally, this problem is addressed using mathematical model based classical optimization method or some meta-heuristic algorithms. However, due to the large-scale nature of this problem, these trials often fail in practical use. Therefore, the Q-learning algorithm based solving method is proposed in this paper. An interactive environment for reinforcement learning is constructed. To validate the effectiveness of proposed method, scenarios with different scales are provided. For the cases with 10, 14, 20, 30, 47, 60, 80 and 100 maintenance tasks respectively, the required solution time is 3.66 s, 4.94 s, 7.66 s, 12.48 s, 21.82 s, 30.82 s, 48.33 s, 73.17 s respectively. The proposed Q-learning algorithm is insensitive with the problem scale, which is promising. Moreover, the Q-learning based algorithm is more efficient than the traditional algorithm along with the increase of the number of tasks. [ABSTRACT FROM AUTHOR]

The aircraft scheduling problem (ASP) is a salient problem in airport runway scheduling system. This paper originally proposes an Ant Colony (AC) algorithm based on the wake-vortex modeling (WVM) method for ASP. Numerical results validate that this new method has better performance than CPLEX, general AC algorithm, and approximation algorithm in Ma et al. (2014). It is a promising method to improve the efficiency of the aircraft scheduling system from a theoretical standpoint. [ABSTRACT FROM AUTHOR]

Porous-core sandwich structures with CFRP face sheets have similar micro structures to biological tissues, for example, grass leaves. The sandwich structures are composed of two thin, stiff face sheets, which provide the primary in-plane structural stiffness, and a thick low-density porous core, which provides the functionalities and the majority of out-plane stiffness. However, the mechanical properties of these sandwich structures are often limited by local failures such as face-core debonding or core collapse. Inspired by the tree leaves with fractal distributed veins, which are larger and stiffer than grass leaves, in this paper, a topological optimization on the micro structures of core is conducted to achieve exceptional mechanical properties. By taking adhesive-area constraint, local-buckling constraint and embedding-volume constraint into consideration, a biomimetic hybrid core, which is similar to the fractal distribution of tree veins, is found to be an optimal core design for sandwich structures with CFRP face sheets. [ABSTRACT FROM AUTHOR]

This paper presents isogeometric topology optimization (ITO) for periodic lattice materials, where non-uniform rational B-spline (NURBS) basis functions of CAD models are directly used in the finite element analysis to improve computational accuracy and efficiency. Two TO schemes that use asymptotic homogenization (AH) for the calculation of the mechanical properties are proposed for lattice materials with uniform and graded relative density respectively. To accelerate ITO for graded lattice materials, the mechanical properties are expressed as a function of the relative density of the unit cell, a step that avoids their iterative calculations during ITO. Three benchmark examples are presented to validate the proposed scheme with results that show tangible advantages, such as reduced computational time and faster convergence, of ITO over conventional TO. [ABSTRACT FROM AUTHOR]

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of test problems including CEC2005 to test and verify its performance qualitatively and quantitatively. It is then employed to find the optimal shape for a 52-bar truss, 3-bar truss, and cantilever beam to demonstrate its applicability. The results show that the proposed algorithm is able to provide superior results compared to well-known and recent algorithms in the literature. The results of the real applications also prove the merits of GOA in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

Speech signals and glottal signals convey speakers’ emotional state along with linguistic information. To recognize speakers’ emotions and respond to it expressively is very much important for human-machine interaction. To develop a subject independent speech emotion/stress recognition system, by identifying speaker's emotion from their voices, features from OpenSmile toolbox, higher order spectral features and feature selection algorithm, is proposed in this work. Feature selection plays an important role in overcoming the challenge of dimensionality in several applications. This paper proposes a new particle swarm optimization assisted Biogeography-based algorithm for feature selection. The simulations were conducted using Berlin Emotional Speech Database (BES), Surrey Audio-Visual Expressed Emotion Database (SAVEE), Speech under Simulated and Actual Stress (SUSAS) and also validated using eight benchmark datasets. These datasets are of different dimensions and classes. Totally eight different experiments were conducted and obtained the recognition rates in range of 90.31%–99.47% (BES database), 62.50%–78.44% (SAVEE database) and 85.83%–98.70% (SUSAS database). The obtained results convincingly prove the effectiveness of the proposed feature selection algorithm when compared to the previous works and other metaheuristic algorithms (BBO and PSO). [ABSTRACT FROM AUTHOR]

Effective coolant application in deep hole gundrilling is crucial to prevent rapid degradation and failure of drills through high performance cooling, lubrication and chip evacuation. This paper presents a novel methodology to increase effectiveness of coolant application through strategic optimization of gun drill designs that leads to appreciable tool life improvement, based on computational fluid dynamics (CFD) analysis. Through the analyses of coolant flow characteristics and rheological properties, conventional designs of critical drill geometries like nose grind contour, coolant hole configuration and shoulder dub-off angle, which are detrimental to coolant application and hence tool life are determined. A new, optimized gun drill design for the drilling of deep holes on Ni-based alloys is proposed. All findings are experimentally substantiated. [ABSTRACT FROM AUTHOR]

Forward and reverse supply chains form a closed-loop supply chain. In this paper, a mathematical model is proposed for a closed-loop supply chain network by considering global factors, including exchange rates and customs duties. The model is a multi-objective mixed-integer linear programming model under uncertain demand. A solution approach based on fuzzy programming is developed for solving the optimization problem. The model is then applied in a network, which is located in Southwestern Ontario, Canada. A sensitivity analysis is provided to validate the model. This model considers global factors, multiobjectives, and uncertainty simultaneously in a closed-loop supply chain network. [ABSTRACT FROM AUTHOR]

Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold’s selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications. [ABSTRACT FROM AUTHOR]

This paper deals with the reduction of top view contour errors in the hot rolling process. Deviations between the actual plate contour and the desired shape may result from asymmetric rolling conditions caused by, e.g., temperature gradients or non-homogeneous input thickness profiles. Under real rolling conditions these disturbances are hard to predict and hence cannot be compensated in a feedforward approach. Therefore, it seems suitable to apply feedback when contour errors occur. This approach essentially requires a measurement of the plate contour and a mathematical model to predict the plate contour after the roll pass. This model is the basis for an optimization-based approach to determine the necessary adjustment of the rolling mill to reduce contour errors. The optimization problem allows to systematically incorporate constraints on the asymmetry of the output plate thickness. Generally, the compliance of the mill stand is not strictly symmetric and may deteriorate the result of the presented approach. The asymmetric compliance results in an asymmetric deflection of the mill stand which is compensated in an empirical approach utilizing the forces applied during the roll pass. Simulation results and measurements of plates rolled during the normal production demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse surface representation for arbitrary surface models (point clouds, mesh models, continuous surface models, etc.). We approximate the input surface model with radial basis functions (RBF) whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is achieved by solving an L 1 optimization problem. Experimental results demonstrate that our method needs much less number of parameters to represent the input surface model with good accuracy. The sparse representation is useful in various applications, such as saving memory space in storing the surface models, and saving time in transmission of the surface models on the internet. [ABSTRACT FROM AUTHOR]

As computing hardware evolves, increasing core counts mean that memory bandwidth is becoming the deciding factor in attaining peak performance of numerical methods. High-order finite element methods, such as those implemented in the spectral/ h p framework Nektar++ , are particularly well-suited to this environment. Unlike low-order methods that typically utilise sparse storage, matrices representing high-order operators have greater density and richer structure. In this paper, we show how these qualities can be exploited to increase runtime performance on nodes that comprise a typical high-performance computing system, by amalgamating the action of key operators on multiple elements into a single, memory-efficient block. We investigate different strategies for achieving optimal performance across a range of polynomial orders and element types. As these strategies all depend on external factors such as BLAS implementation and the geometry of interest, we present a technique for automatically selecting the most efficient strategy at runtime. [ABSTRACT FROM AUTHOR]

Single floor facility layout problem (FLP) is related to finding the arrangement of a given number of departments within a facility; while in multi-floor FLP, the departments should be imbedded in some floors inside the facility. The significant influence of layout design on the effectiveness of any organization has turned FLP into an important issue. This paper presents a three- (two-) stage mathematical programming method to find competitive solutions for multi- (single-) floor problems. At the first stage, the departments are assigned to the floors through a mixed integer programming model (the single floor version does not require this stage). At the second stage, a nonlinear programming model is used to specify the relative position of the departments on each floor; and at the third stage, the final layouts within the floors are determined, through another nonlinear programming model. The multi-floor version is studied in the states in which the locations of the elevators are either formerly specified or not. Computational results show that this framework can find a wide variety of high quality layouts at competitive cost (up to 43% reduction) within a short amount of time for small and especially large size problems, compared to the existing methods in the literature. Also, the proposed method is flexible enough to accommodate the complicated and real-world problems, because of using mathematical programming model and solving it directly. [ABSTRACT FROM AUTHOR]

This paper presents a nature-inspired metaheuristic algorithm namely linear adaptive spiral dynamics algorithm (LASDA) and its application to modelling of a flexible system. The performance of spiral dynamics algorithm (SDA) is in general not satisfactory due to the incorporation of a single radius and single angular displacement values during the whole search process. LASDA is proposed as an improved version of SDA where the spiral radius and angular displacement are dynamically varied by employing novel mathematical equation based on linear function, which establishes a relationship between fitness value, spiral radius and angular displacement. The proposed algorithm is tested with various types of multimodal and unimodal benchmark functions and its performance in terms of fitness accuracy is discussed. A linear parametric modelling approach is utilised with an autoregressive model with exogenous inputs (ARX) structure for a flexible system. The proposed algorithm is then used to optimise parameters of the ARX structure. The performances of the LASDA in terms of convergence speed to the optimum value, fitness accuracy, time-domain and frequency-domain responses of acquired model is presented in comparison to SDA, BFA and IBFA. The results show that the proposed algorithm achieves better performance in finding an optimal solution for the benchmark functions as well as for the modelling of the flexible system. [ABSTRACT FROM AUTHOR]

This paper presented a novel mathematical programming model for the simultaneous optimization of heat integrated water allocation networks featuring parallel heat exchanger network (HEN) structure. In the HEN structure, both freshwater and wastewater can be split freely. This model was suitable for both uniform wastewater treatment and separate wastewater treatment cases. The proposed model was formulated as a MINLP (mixed-integer non-linear programming) problem, making it applicable to large scale problems. The main objective was to minimize the total annual cost. Three literature examples, including a large scale example, were illustrated to demonstrate the applicability of the model. It was shown that the proposed method was as accurate as the literature methods for small scale problems, but performed better for large scale problem applications. [ABSTRACT FROM AUTHOR]

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. [ABSTRACT FROM AUTHOR]

Scheduling of power and real-time optimization for three industrial cogeneration plants at one of Dow's Louisiana site is presented in this paper. A first principle mathematical model that includes mass and energy balances for gas turbines, heat recovery units, steam turbines, pressure relief valves and steam headers is used to formulate an optimization problem to recommend the best strategy to trade power. The model has detailed operational information that includes equipment status and control curves for different operating scenarios. The model can also accurately predict the effect of ambient temperature, thereby resulting in an optimal day-ahead schedule. Adjustment of power schedule is done in the real-time market 30 min prior to the hour and implementation of the dispatched power schedule is done using a model predictive controller. [ABSTRACT FROM AUTHOR]

Of the grey models proposed for making predictions based on small sample data, the GM(1,1) model is the most important because of its low demands of data distribution, simple operation, and calculation requirements. However, the classical GM(1,1) model has two disadvantages: it cannot reflect the new information priority principle, and, if it is necessary to obtain the ideal effect of modeling, the original data must meet the class ratio test. This paper presents a new fractional grey model, FGM( q , 1), which is an extension of the GM(1,1) model in that first-order differential equations are transformed into fractional differential equations. Decomposition of the data matrix parameters during the process of solution shows that the new model follows the new information priority principle. For modeling, the mean absolute percentage error (MAPE) is established as the objective function of the optimization model, and a particle swarm algorithm is used to calculate the accumulation number and the order of the differential equation that can minimize the MAPE. Finally, the results from three groups of data modeling show that, compared with other classical grey models, FGM( q , 1) has higher modeling precision, can overcome the GM(1,1) model class ratio test restrictions and has a wider adaptability. [ABSTRACT FROM AUTHOR]

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. Exploiting the equation-based language led to 2200 times faster solution. [ABSTRACT FROM AUTHOR]