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This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Energy consumption in commercial buildings is significantly affected by the performance of heating, ventilation, and air-conditioning (HVAC) systems, which are traditionally operated using centralized controllers. HVAC control requires adjusting multiple setpoints such as chilled water temperatures and supply air temperature (SAT). Supervisory control framework in a distributed setting enables optimal HVAC operation and provides scalable solutions for optimizing energy across several scales from homes to regional areas. This paper proposes a distributed optimization framework for achieving energy efficiency in large-scale building energy systems. It is highly desirable to have building management systems that are scalable, robust, flexible, and are low cost. For addressing the scalability and flexibility, a modular problem formulation is established that decouples the distributed optimization level from local thermal zone modeling level. We leverage a recently developed generalized gossip algorithm for robust distributed optimization. The supervisory controller aims at minimizing the energy input considering occupant comfort. For validating the proposed scheme, a numerical case study based on a physical testbed in the Iowa Energy Center is presented. We show that the distributed optimization methodology outperforms the typical baseline strategy, which is a rule-based controller to set a constant supply air temperature. This paper also incorporates a software architecture based on the VOLTTRON platform, developed by the Pacific Northwest National Laboratory (PNNL), for practical implementation of the proposed framework via the BACnet system. The experimental results show that the supervisory control framework proposed in this paper can save energy by approximately 11%. [ABSTRACT FROM AUTHOR]

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions. [ABSTRACT FROM AUTHOR]

This paper extends the recently developed quasi-spectral model predictive static programming (QS-MPSP) to include state and control path-constraints and yet retain its computational efficiency. This is achieved by (i) formulating the entire problem in the control variables alone by (a) converting the system dynamics to an equivalent algebraic constraint and (b) converting the state constraints to equivalent control constraints, both of which is done by manipulating the system dynamics, (ii) representing the control variables in Quasi-spectral form, which makes the number of free-variables independent of time-grids and (iii) using a computationally efficient optimization algorithm to solve this low-dimensional problem. This generic computationally efficient technique is utilized next as an effective lead angle, and lateral acceleration constrained optimal missile guidance to intercept incoming high-speed ballistic targets with high precision successfully. Both of these constraints, as well as near-zero miss-distance, are of high practical significance for this challenging problem. Extensive three-dimensional simulation studies show the effectiveness of the newly proposed constrained QS-MPSP guidance algorithm. Six degrees-of-freedom simulation studies have also been carried out using autopilot in the loop to validate the results more realistically. [ABSTRACT FROM AUTHOR]

When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using determinantal point processes (DPP) and additive submodular functions. We show that DPPs are more suitable for items expressed as text and that a greedy algorithm diversifies rankings with both theoretical guarantees and empirical performance on what is otherwise an NP-Hard problem. To produce such rankings, this paper contributes a novel way to extend quality and diversity metrics from sets to permutations of ranked lists. These rank metrics open up the use of multi-objective optimization to describe trade-offs between diversity and quality in ranked lists. We use such trade-off fronts to help designers select rankings using indifference curves. However, we also show that rankings on trade-off front share a number of top-ranked items; this means reviewing items (for a given depth like the top ten) from across the entire diversity-to-quality front incurs only a marginal increase in the number of designs considered. While the proposed techniques are general purpose enough to be used across domains, we demonstrate concrete performance on selecting items in an online design community (OpenIDEO), where our approach reduces the time required to review diverse, high-quality ideas from around 25 h to 90 min. This makes evaluation of crowd-generated ideas tractable for a single designer. Our code is publicly accessible for further research. [ABSTRACT FROM AUTHOR]

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3dcs simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in matlab. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on matlab. (3) Since matlab macrocommands of 3dcs have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, a new computational algorithm for the numerical solution of the adjoint equations for the nonlinear optimal control problem is introduced. To this end, the main features of the optimal control theory are briefly reviewed and effectively employed to derive the adjoint equations for the active control of a mechanical system forced by external excitations. A general nonlinear formulation of the cost functional is assumed, and a feedforward (open-loop) control scheme is considered in the analytical structure of the control architecture. By doing so, the adjoint equations resulting from the optimal control theory enter into the formulation of a nonlinear differential-algebraic two-point boundary value problem, which mathematically describes the solution of the motion control problem under consideration. For the numerical solution of the problem at hand, an adjoint-based control optimization computational procedure is developed in this work to effectively and efficiently compute a nonlinear optimal control policy. A numerical example is provided in the paper to show the principal analytical aspects of the adjoint method. In particular, the feasibility and the effectiveness of the proposed adjoint-based numerical procedure are demonstrated for the reduction of the mechanical vibrations of a nonlinear two degrees-of-freedom dynamical system. [ABSTRACT FROM AUTHOR]

Redundancy is a useful feature in dynamic systems which can be exploited to enhance performance in various tasks. In this work, redundancy will be utilized to minimize the energy consumption of a linear manipulator, while in some cases an additional task of end-effector tracking will also be required and achieved. Optimal control theory has been extensively used for the optimization of dynamic systems; however, complex tasks and redundancy make these problems computationally expensive, numerically difficult to solve, and in many cases, ill-defined. In this paper, evolutionary bilevel optimization for the problem is presented. This is done by setting up an upper level optimization problem for a set of decision variables and a lower level one that actually calculates the optimal inputs and trajectories. The upper level problem is solved by a genetic algorithm (GA), whereas the lower level problem uses classical optimal control. As a result, the proposed algorithm allows the optimization of complex tasks that usually cannot be solved in practice using standard optimal control tools. In addition, despite the use of penalty functions to enforce saturation constraints, the algorithm leads to global energy minimization. Illustrative examples of a redundant x-y robotic manipulator with complex overall tasks will be presented, solved, and discussed. [ABSTRACT FROM AUTHOR]

Automation is becoming more and more important to achieve high efficiency and productivities in manufacturing facilities, and there has been a large increase in the use of autonomous mobile robots (AMRs) for factory automation. With the number of AMRs increasing, how to optimally schedule them in a timely manner such that a large school of AMRs can finish all the assigned tasks within the shortest time presents a significant challenge for control engineers. Exhaustive search can provide an optimal solution. However, its associated computational time is too long to render it feasible for real-time control. This paper introduces a novel two-step algorithm for fast scheduling of AMRs that perform prioritized tasks involving transportation of tools/materials from a pick-up location to a drop-off point on the factory floor. The proposed two-step algorithm first clusters these tasks such that one cluster of tasks is assigned to one single AMR, followed by scheduling of the tasks within a cluster using a model-based learning technique. For the purpose of clustering and scheduling, a task space is defined. The results from the clustering and scheduling algorithms are compared with other widely used heuristic techniques. Both the clustering and the scheduling algorithms are shown to perform better on task sets of relevant sizes and generate real-time solutions for the scheduling of multiple AMRs under task space constraints with priorities. [ABSTRACT FROM AUTHOR]

This paper introduces a robust visual tracking of objects in complex environments with blocking obstacles and light reflection noises. This visual tracking method utilizes a transfer matrix to project image pixels back to real-world coordinates. During the image process, a color and shape test is used to recognize the object and a vector is used to represent the object, which contains the information of orientation and body length of the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector predicts the position of the object. During the real-time tracking, a Kalman filter is used to optimize the result. To validate the method, the visual tracking algorithm was tested by tracking a submarine and a fish on the water surface of a water tank, above which three pieces of blur glass were blocking obstacles between the camera and the object. By using this method, the interference from the reflection of the side glass and the fluctuation of the water surface can be also avoided. [ABSTRACT FROM AUTHOR]

The thermal management is an ever increasing challenge in advanced electronic devices. In this paper, simulation-based optimization is applied to improve the design of a plate-fin heat-sink in terms of operational cost and thermal performance. The proposed framework combines a conjugate heat transfer solver, a CAD engine and an adapted Sandwiching algorithm. A key feature is the use of novel immersed boundary (IB) techniques that allows for automated meshing which is perfectly suited for parametric design optimization. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributing uniformly the energy flux intercepted by a thermoplastic sheet surface during the infrared radiation. To do so, we discretized this problem and then formulated it as an integer linear programming problem, for which we applied two meta-heuristic algorithms namely the simulated annealing algorithm (SA) and harmony search algorithm (HSA), in order to minimize the corresponding objective function. The results produced by the numerical study we conducted on the performance of both algorithms are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of a study of citation and co-authorship networks for articles published at the ASME Design Automation Conference (DAC) during the years 2002-2015. Two topic-modeling methods are presented for studying the DAC literature: A frequency-based model was developed to explore DAC topic distribution and evolution, as well as citation analysis for each core topic. Correlation analysis and association-rule mining were used to discover relationships between topics. A new unsupervised learning algorithm, propagation mergence (PM), was created to address identified shortcomings of existing methods and applied to study the existing DAC citation network. Influential articles and important article clusters were identified and effective visualizations created. We also investigated the DAC co-authorship network by identifying key authors and showing that the network structure exhibits small-world-network properties. The resulting insights, obtained by the both the proposed and existing methods, may be beneficial to the engineering design research community, especially with respect to determining future research directions and possible actions for improvement. The data set used here is limited; expanding to include additional relevant conference proceedings and journal articles in the future would offer a more complete understanding of the engineering design research literature. [ABSTRACT FROM AUTHOR]

This paper compares the effectiveness of the proposed hybrid metaheuristic algorithms for a class of unstable systems with time delay to that of the existing ones. The local search and global methods of optimization are combined to yield more effective hybrid metaheuristic algorithms. These algorithms are used to tune the proportional-integral-derivative (PID) controllers, satisfying the robust stabilizing vector gain margin (VGM). Six global heuristic algorithms namely ant colony optimization (ACO), particle swarm optimization (PSO), biogeography-based optimization (BBO), population-based incremental learning (PBIL), evolution strategy (ES), and stud genetic algorithms (StudGA) are combined with the local search property of derivative free optimization methods such as simplex derivative based pattern search (SDPS) and implicit filtering (IMF) to yield hybrid metaheuristic algorithms. The efficacy of the proposed control schemes in terms of various time domain specifications and stabilizing VGM are compared with some existing methods for unstable process with time delay (UPTD) systems. The performance of the proposed control schemes particularly in the context of uncertainty in the plant is demonstrated using a case study. The efficacy of the proposed control scheme is illustrated with a nontransfer function based multibody vehicle autosteer control design problem. [ABSTRACT FROM AUTHOR]

A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation. [ABSTRACT FROM AUTHOR]

This paper presents an improved speed estimator for a permanent magnet synchronous motor (PMSM). It focuses on hybrid electric vehicles (HEVs). The speed estimator is based on reactive power model reference adaptive system (Q-MRAS). The MRAS parameters are tuned using particle swarm optimization (PSO) algorithms. The proposed method has been experimentally verified with a 100 kW, 5000 rpm PMSM, and a good agreement between the measured speed and the estimated speed is found. It is shown that the proposed method is able to handle the transition into the flux weakening mode without any problem. [ABSTRACT FROM AUTHOR]

Engineering design often involves problems with multiple conflicting performance criteria, commonly referred to as multi-objective optimization problems (MOP). MOPs are known to be particularly challenging if the number of objectives is more than three. This has motivated recent attempts to solve MOPs with more than three objectives, which are now more specifically referred to as "many-objective" optimization problems (MaOPs). Evolutionary algorithms (EAs) used to solve such problems require numerous design evaluations prior to convergence. This is not practical for engineering applications involving computationally expensive evaluations such as computational fluid dynamics and finite element analysis. While the use of surrogates has been commonly studied for single-objective optimization, there is scarce literature on its use for MOPs/MaOPs. This paper attempts to bridge this research gap by introducing a surrogate-assisted optimization algorithm for solving MOP/MaOP within a limited computing budget. The algorithm relies on principles of decomposition and adaptation of reference vectors for effective search. The flexibility of function representation is offered through the use of multiple types of surrogate models. Furthermore, to efficiently deal with constrained MaOPs, marginally infeasible solutions are promoted during initial phases of the search. The performance of the proposed algorithm is benchmarked with the state-of-the-art approaches using a range of problems with up to ten objective problems. Thereafter, a case study involving vehicle design is presented to demonstrate the utility of the approach. [ABSTRACT FROM AUTHOR]

Finite-time control problem of linear time-varying systems with input constraints is considered in this paper. Successive ellipsoidal approximations are used to estimate the state evolution of linear time-varying systems during a certain finite-time interval. An algorithm to design a controller based on approximations of state evolution is proposed. According to the proposed algorithm, the speed of state approaching equilibrium is optimized piecewisely using admissible control. The controller gain can be obtained by solving several quasi-convex optimization problems, which makes the design process computationally tractable. Simulation results show that the proposed controller can quickly reduce state deviation without violating input constraints. [ABSTRACT FROM AUTHOR]

Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees-of-freedom in modular product design, considering disassembly time, cost, and energy consumption. An automatic self-decomposed disassembly precedence matrix (DPM) is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search-based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits (EBs). The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving EBs and disassembly profit (DP). [ABSTRACT FROM AUTHOR]

Thermoacoustic instabilities pose a major threat to modern gas turbines. The use of acoustic dampers, like Helmholtz resonators, has proven useful for the mitigation of such instabilities. However, assessing the effect of acoustic dampers on thermoacoustic modes in annular combustion chambers remains an intricate task. This results from the implicit nature of the thermoacoustic Helmholtz equation associated with the high number of possible parameter values for the positioning of the dampers and their impedance design. In the present work, the principal challenges of the effective placement and the design of the impedance of acoustic dampers in annular chambers are discussed. This includes the choice of an appropriate objective function for the optimization, the combinatorial challenges when dealing with different possible damper arrangements, and the numerical complexities when using the thermoacoustic Helmholtz equation to approach this issue. As a key aspect, the paper proposes a new adjoint-based approach to tackle these problems. The new algorithm establishes algebraic models that predict the effect of acoustic dampers on the growth rates of the thermoacoustic modes. The theory is exemplified on the basis of a generic annular combustor model with 12 burners. [ABSTRACT FROM AUTHOR]

The problem of parameter estimation of permanent-magnet synchronous machines (PMSMs) can be formulated as a nonlinear optimization problem. To obtain accurate machine parameters, it is necessary to develop easily applicable but efficient optimization algorithms to solve the parameter estimation models. This paper proposes a novel dynamic differential evolution with adaptive mutation operator (AMDDE) algorithm for the multiparameter simultaneous estimation of a nonsalient pole PMSM. The dynamic updating of population enables AMDDE to responds to any improved changes of the population immediately and thus generates better optimization solutions compared with the static mechanism used in original differential evolution. Two mutation strategies, namely DE/rand/1 and DE/best/1, are adaptively employed to balance the global exploration and local exploitation. The effectiveness of the proposed AMDDE is demonstrated on the multiparameter estimation for a nonsalient pole PMSM. Experimental results indicate that the proposed method significantly outperforms the existing peer algorithms in efficiency, accuracy, and robustness. Furthermore, the new algorithm can be potentially realized in digital microcontroller due to its simple structure and lower memory requirement. The proposed algorithm can also be applied to other parameter estimation and optimization problems. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes are used to fabricate complex geometries using a layer-by-layer material deposition technique. These processes are recognized for creating complex shapes which are difficult to manufacture otherwise and enable designers to be more creative with their designs. However, as AM is still in its developing stages, relevant literature with respect to design guidelines for AM is not readily available. This paper proposes a novel design methodology which can assist designers in creating parts that are friendly to additive manufacturing. The research includes formulation of design guidelines by studying the relationship between input part geometry and AM process parameters. Two cases are considered for application of the developed design guidelines. The first case presents a feature graph-based design improvement method in which a producibility index (PI) concept is introduced to compare AM friendly designs. This method is useful for performing manufacturing validation of pre-existing designs and modifying it for better manufacturability through AM processes. The second approach presents a topology optimization-based design methodology which can help designers in creating entirely new lightweight designs which can be manufactured using AM processes with ease. Application of both these methods is presented in the form of case studies depicting design evolution for increasing manufacturability and associated producibility index of the part. [ABSTRACT FROM AUTHOR]

Energy consumption in five-axis machining of freeform surfaces can be considerably large for large-size parts. This paper presents a study on how to setup the workpiece in order to minimize the energy consumption without modifying the toolpath itself. For an arbitrary freeform workpiece, the way how it is setup on the working table highly affects the machine's kinematic behavior, which dominates the overall processing time and energy consumption. Taking into account the speed and acceleration limit of each axis of the machine, we first establish the energy consumption model as a function of the workpiece setup. However, this original model involves certain critical physically pertinent coefficients (such as the moment of inertial of a rotary table) which are usually unavailable in practice. Instead, by exploring insightful geometric characteristics of the five-axis machine, an alternative energy consumption model is established which is independent of those hard-to-obtain coefficients. A simple algorithm is then designed to optimize this model. Both computer simulations and physical cutting experiments demonstrate that, when compared with an arbitrary setup, the optimized workpiece setup is able to achieve a significant saving (as much as 50%) in both energy consumption and total machining time, both using a same tool path. [ABSTRACT FROM AUTHOR]

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary algorithms based on the concept of "decomposition" have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems. [ABSTRACT FROM AUTHOR]

The design of manufacturing systems is typically decoupled from ordering policy decisions. Traditionally, the system design decision is made to minimize the total investment cost given some system capacity requirements. Then, after the system is implemented, the ordering policy decisions are made. In this paper, a coupled approach is presented whereby the manufacturing system design is jointly developed with the ordering policy to minimize the total cost of inventory' holding, setup, and equipment investment in a multiproduct system. The methodology is presented in the context of scalable-reconfigurable manufacturing systems (scalable-RMSs). First, a linear integer mathematical formulation to minimize investment cost in a single-product, multistage scalable-RMS is presented. The mathematical formulation is then extended to consider multiple products. Due to the nonlinear nature of the multiproduct formulation, an iterative algorithm is developed. Lastly, a mathematical formulation to simultaneously minimize the system investment and operating costs (i.e., the coupled approach) is presented. Given the complexity of the formulation, a genetic algorithm (GA)-based heuristic is proposed. Twenty, four instances of the problem were generated to test the proposed methodologies. Experimental results indicate that the proposed GA-based heuristic is efficient in terms of solution quality, and runtime. Further experimental results indicate that the proposed coupled approach reduces the total costs by an average of 25% over the decoupled approach. It is concluded that the coupled approach (solved with the proposed GA-based heuristic) outperforms the decoupled approach (solved to optimality) in all instances considered. [ABSTRACT FROM AUTHOR]

This paper presents an energy dispatch algorithm for real-time topping cycle cooling, heating, and power (CHP) operation for buildings with the objective of minimizing the operational cost, primary energy consumption (PEC), or carbon dioxide emission (CDE). The algorithm features a supervisory feed-forward control for real-time CHP operation using short-term weather forecasting. The advantages of the proposed control scheme for CHP operation are (a) relatively simple and efficient implementation allowing realistic real-time operation, (b) optimized CHP operation with respect to operational cost, PEC, or CDE, and (c) increased site-energy consumption resulting in less dependence on the electric grid. In the feed-forward portion of the control scheme, short-term electric, cooling, and heating loads are predicted using the U.S. Department of Energy benchmark small office building model. The results are encouraging regarding the potential saving of operational cost, PEC, and CDE from using the control system for a CHP system with electric and thermal energy storages. [ABSTRACT FROM AUTHOR]

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques. [ABSTRACT FROM AUTHOR]