Dynamic Optimization Techniques Research Articles

Dynamic Optimization Technique for Distribution of Goods with Stochastic Shortages

This work considers the distribution of goods with stochastic shortages from factories to stores. It is assumed that in the process of shipping the goods to various stores, some proportion of the goods will be damaged (which will lead to shortage of goods in transit). The cost of the damaged goods is added to the cost of the shipment. A proportion of the total expected cost of the shortage goods is assumed to be recovered and should be deducted from the total cost of the shipment. In order to determine the minimum transportation costs for the operation, we adopt dynamic optimization principles. The optimal transportation cost and optimal control policies of shipping the goods from factories to stores were obtained. We find that the optimal costs of the goods recovered could be determined. It was further found that the optimum costs of distributing the goods with minimum and maximum error bounds coincide only at infinity.

Fast intelligent watermarking of heterogeneous image streams through mixture modeling of PSO populations

In intelligent watermarking (IW), evolutionary computing (EC) is employed in order to automatically set the embedding parameters of digital watermarking systems for each image. However, the computational complexity of EC techniques makes IW unfeasible for large scale applications involving heterogeneous images. In this paper, we propose a dynamic particle swarm optimization (DPSO) technique which relies on a memory of Gaussian mixture models (GMMs) of solutions in the optimization space. This technique is employed in the optimization of embedding parameters of a multi-level (robust/fragile) bi-tonal watermarking system in high data rate applications. A compact density representation of previously-found DPSO solutions is created through GMM in the optimization space, and stored in memory. Solutions are re-sampled from this memory, re-evaluated for new images and have their distribution of fitness values compared with that stored in the memory. When the distributions are similar, memory solutions are employed in a straightforward manner, avoiding costly re-optimization operations. A specialized memory management mechanism allows to maintain and adapt GMM distributions over time, as the image stream changes. This memory of GMMs allows an accurate representation of the topology of a stream of optimization problems. Consequently, new cases of optimization can be matched against previous cases more precisely (when compared with a memory of static solutions), leading to considerable decrease in computational burden. Simulation results on heterogeneous streams of images indicate that compared to full re-optimization for each document image, the proposed approach allows to decrease the computational requirement linked to EC by up to 97.7% with little impact on the accuracy for detecting watermarks. Comparable results were obtained for homogeneous streams of document images.

Modified PSO algorithm for multi-objective optimization of the cutting parameters

Economic profit of machining is essentially based on the optimal selection of cutting parameters. In this paper, a multi-objective particle swarm optimization approach is introduced to optimize the cutting parameters in turning processes: cutting speed, feed rate and cutting depth. The proposed model presents the problem in form of a multi-objective problem with production rate and used tool life objectives and has a set of constraints that represent the important limitations to be satisfied. To obtain the non dominated solutions and build the Pareto front graph, a modified dynamic neighborhood particle swarm optimization (DNPSO) technique is used. In addition, a fuzzy-based mechanism is employed to extract the best compromise solution. The results on an illustrative sample reveal the capabilities of the proposed DNPSO approach to generate well-distributed Pareto optimal solutions. Comparison with multi-objective deterministic approach (Min–Max) shows the superiority of the proposed approach and confirms its potential for solving multi-objective problems.

An Optimal and Complete Algorithm for Automatic Web Service Composition

The ability of web services to build and integrate loosely-coupled systems has attracted a great deal of attention from researchers in the field of the automatic web service composition. The combination of different web services to build complex systems can be carried out using different control structures to coordinate the execution flow and, therefore, finding the optimal combination of web services represents a non-trivial search effort. Furthermore, the time restrictions together with the growing number of available services complicate further the composition problem. In this paper the authors present an optimal and complete algorithm which finds all valid compositions from the point of view of the semantic input-output message structure matching. Given a request, a service dependency graph which represents a suboptimal solution is dynamically generated. Then, the solution is improved using a backward heuristic search based on the A* algorithm which finds all the possible solutions with different number of services and runpath. Moreover, in order to improve the scalability of our approach, a set of dynamic optimization techniques have been included. The proposal has been validated using eight different repositories from the Web Service Challenge 2008, obtaining all optimal solutions with minimal overhead.

On a Dynamic Optimization Technique for Resource Allocation Problems in a Production Company

This paper examines the allocation of resource to different tasks in a production company. The company produces the same kinds of goods and want to allocate m number of tasks to 50 number of machines. These machines are subject to breakdown. It is expected that the breakdown machines will be repaired and put into operation. From past records, the company estimated the profit the machines will generate from the various tasks at the first stage of the operation. Also, the company estimated the probability of breakdown of the machines for performing each of the tasks. The aim of this paper is to determine the expected maximize profit that will accrue to the company over T horizon. The profit that will accrued to the company was obtained as N4,571,100,000 after 48 weeks of operation. At the infinty horizon, the profit was obtained to be N20,491,000,000 . It was found that adequate planning, prompt and effective maintainance can enhance the profitability of the company.

Occupant comfort evaluation and wind-induced serviceability design optimization of tall buildings

This paper presents an integrated wind-induced dynamic analysis and computer-based design optimization technique for minimizing the structural cost of general tall buildings subject to static and dynamic serviceability design criteria. Once the wind-induced dynamic response of a tall building structure is accurately determined and the optimal serviceability design problem is explicitly formulated, a rigorously derived Optimality Criteria (OC) method is to be developed to achieve the optimal distribution of element stiffness of the structural system satisfying the wind-induced drift and acceleration design constraints. The effectiveness and practicality of the optimal design technique are illustrated by a full-scale 60-story building with complex 3D mode shapes. Both peak resultant acceleration criteria and frequency dependent modal acceleration criteria are considered and their influences on the optimization results are highlighted. Results have shown that the use of various acceleration criteria has different implications in the habitability evaluations and subsequently different optimal design solutions. The computer based optimization technique provides a powerful tool for the lateral drift and occupant comfort design of tall building structures.

The Dynamic Analysis and Optimization Technique of Non-Stationary Random Vibration

This paper introduces a new non-stationary random vibration technique for the analysis of time-dependent random vibration systems. It is based on the pseudo-excitation method (PEM) and precise integration method (PIM), which is extended herein and can improve significantly the analysis efficiency. Based on the random vibration equations with the right-hand side random replaced by a pseudo excitation, the first and second orders of sensitivity formulae are calculated conveniently by differentiating these equations. By use of sensitivity information, the optimization of complicated random vibration can be performed easily. Some numerical examples of vehicle-bridge time-dependent coupled system show the effectiveness and accuracy of present method.

MTCrossBit: A dynamic binary translation system based on multithreaded optimization

Dynamic optimization has been proposed to overcome many limitations caused by static optimization and is widely applied in dynamic binary translation (DBT) to effectively enhance system performance. However, almost all the existing dynamic optimization techniques or methods employed in DBT systems for a single-threaded executive environment considerably increase the complexity of the hardware or the striking runtime overhead. We propose a multithreaded DBT framework with no associated hardware called the MTCrossBit, where a helper thread for building a hot trace is employed to significantly reduce the overhead. In addition, the main thread and helper thread are each assigned to different cores to use the multi-core resources efficiently to attain better performance, two novel methods yet to be implemented in the MTCrossBit are presented: the dual-special-parallel translation caches and the new lock-free threads communication mechanism—assembly language communication (ASLC). We then apply quantitative analysis to prove that MTCrossBit can speed up the original CrossBit. Simultaneously, we present results from the implementation of the MTCrossBit on the uniprocessor machines with multi-cores utilizing the benchmark-SPECint 2000, and illustrate that we achieved some success with the above concurrent architecture.

Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies

We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.

Thread Warping

We introduce thread warping, a dynamic optimization technique that customizes multicore architectures to a given application by dynamically synthesizing threads into custom accelerator circuits on FPGAs (Field-Programmable Gate Arrays). Thread warping builds upon previous dynamic synthesis techniques for single-threaded applications, enabling dynamic architectural adaptation to different amounts of thread-level parallelism, while also exploiting parallelism within each thread to further improve performance. Furthermore, thread warping maintains the important separation of function from architecture, enabling portability of applications to architectures with different quantities of microprocessors and FPGAs, an advantage not shared by static compilation/synthesis approaches. We introduce an approach consisting of CAD tools and operating system support that enables thread warping on potentially any microprocessor/FPGA architecture. We evaluate thread warping using a simulator for high-performance computing systems with different interconnections in addition to multicore embedded systems having between 4 and 64 ARM11 microprocessors. On average, thread warping achieved approximately 3x speedup compared to a high-performance quad-core Intel Xeon and 109x compared to an embedded system consisting of 4 ARM11 cores, with a size cost approximately equal to 36 ARM11 cores.

Maximum thermodynamic efficiency problem in batch distillation

A dynamic batch distillation study of the non-ideal mixture Ethanol-Water is presented. The objective of the study was to calculate an average thermodynamic efficiency of the process under an optimal constant reflux policy and the objective function includes a given production time in order to obtain the desired product quality (measured as the average mole fraction of the accumulated product). An expression for computing the thermodynamic efficiency is presented. The simulation of the column uses a mathematical model considering the complete dynamics of the operation and the problem of optimal control resulting in a non-linear programming problem. A dynamic optimization technique based on a SQP method was used to solve the problem. The average thermodynamic efficiency for the separation process under the conditions presented was 37.95%.

Utility driven optimization of real time data broadcast schedules

AbstractData dissemination in wireless environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many environments where the utility of the received data is an equally important criterion as its timeliness. Missing the deadline may reduce the utility of the data but does not necessarily make it zero. In this work, we address the problem of scheduling real time data broadcasts with such soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within an acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby eliminating the requirement of a heuristic in the process. An evolution strategy based stochastic hill-climber is investigated in this context.

Dynamic Reliability-Based Seismic Optimal Design of Base-Isolated Structures

This paper presents a dynamic reliability-based optimization technique for the seismic design of base-isolated structures. Firstly, the governing equation of multi-degree-of-freedom base-isolated structures is established. Then, the superstructure is unfolded by the first mode. Considering that the damping is non-classical and the total base-isolation system is un-symmetric, the complex modal analysis is adopted to uncouple the governing equation and the analytical solutions of stochastic seismic response under the Kanai-Tajimi spectrum loading are obtained. Taking the ratio between the first-order modal displacement standard deviation of the superstructure with base- isolated system and the fixed-base structure as the optimal objective function, the dynamic reliability of the isolated system displacement as the constraint, the optimal design parameters of the isolated system are obtained through the penalty function method. A 3-story building with isolated system illustrates the proposed dynamic reliability-based optimization method. It is believed that such an optimization technique provides an effective tool for the seismic design of base-isolated structures.

Dynamic Reliability-Based Seismic Optimal Design of Structures with Tuned Mass Damper

This paper presents a dynamic reliability-based optimization technique for the seismic design of structures with tuned mass damper (TMD). Firstly, the governing equation of multi-degree-of-freedom structure with TMD is established. Then, the main structure is unfolded by the first mode. Considering that the damping is non-classical and the total main structure and TMD system is un-symmetric, the complex modal analysis is adopted to uncouple the governing equation and the analytical solutions of stochastic seismic response under the Kanai-Tajimi spectrum loading are obtained. Taking the ratio between the first-order modal displacement standard deviation of the structure with TMD and the one without TMD as the objective function, the dynamic reliability of the TMD system displacement as the constraint, the optimal design parameters of the TMD system are obtained through the penalty function method. A 10-story building with TMD system illustrates the proposed dynamic reliability-based optimization method. It is believed that such an optimization technique provides an effective tool for the seismic design of structures with TMD.

Minimizing dose during fluoroscopic tracking through geometric performance feedback

There is a growing concern regarding the dose delivered during x-ray fluoroscopy guided procedures, particularly in interventional cardiology and neuroradiology, and in real-time tumor tracking radiotherapy and radiosurgery. Many of these procedures involve long treatment times, and as such, there is cause for concern regarding the dose delivered and the associated radiation related risks. An insufficient dose, however, may convey less geometric information, which may lead to inaccuracy and imprecision in intervention placement. The purpose of this study is to investigate a method for achieving the required tracking uncertainty for a given interventional procedure using minimal dose. A simple model is used to demonstrate that a relationship exists between imaging dose and tracking uncertainty. A feedback framework is introduced that exploits this relationship to modulate the tube current (and hence the dose) in order to maintain the required uncertainty for a given interventional procedure. This framework is evaluated in the context of a fiducial tracking problem associated with image-guided radiotherapy in the lung. A particle filter algorithm is used to robustly track the fiducial as it traverses through regions of high and low quantum noise. Published motion models are incorporated in a tracking test suite to evaluate the dose-localization performance trade-offs. It is shown that using this framework, the entrance surface exposure can be reduced by up to 28.6% when feedback is employed to operate at a geometric tracking uncertainty of 0.3 mm. The analysis reveals a potentially powerful technique for dynamic optimization of fluoroscopic imaging parameters to control the applied dose by exploiting the trade-off between tracking uncertainty and x-ray exposure per frame.

STOCHASTIC OPTIMIZATION OF HEURISTIC METHOD RULE TO DETERMINE ASSET ALLOCATION TO RETIREMENT PORTFOLIO / STOCHASTINIS EURISTINIO METODO TAISYKLĖS PENSIJOS PORTFELIO SUDĖČIAI NUSTATYTI OPTIMIZAVIMAS

The article examines the problem of determining asset allocation to sustainable retirement portfolio. The article attempts to apply heuristic method – 100 minus age in stocks rule – to determine asset allocation to sustainable retirement portfolio. Using dynamic stochastic simulation and stochastic optimization techniques the optimization of heuristic method rule is presented and the optimal alternative to „100“ is found. Seeking to reflect the stochastic nature of stock and bond returns and the human lifespan, the dynamic stochastic simulation models incorporate both the stochastic returns and the probability of living another year based on Lithuania‘s population mortality tables. The article presents the new method – adjusted heuristic method – to be used to determine asset allocation to retirement portfolio and highlights its advantages.

Optimal Investment Strategy for a Defined Contributory Pension Plan in Nigeria Using Dynamic Optimization Technique

Abstract: We consider an optimal investment strategy for a defined contributory pension plan in Nigeria using dynamic optimization technique. The Pension Plan Members (PPMs) make contributions continuously into the pension funds. The Pension Fund Administrator (PFA) propose to invest the contributions made by the PPMs into Federal Government of Nigeria (FGN) bond such as construction of roads in Nigeria. They propose that every road constructed must has a tollgate in order to collect toll and make more wealth for the PPMs at time t <= T . We assume that there are Alternative Roads (AR) the drivers may take to their destination without paying toll. The AR may not be good enough for the vehicles to pass smoothly. We assume that Pension Plan Company (PPC) will make more wealth for the PPMs if the Company Roads (CR) are highly motorable. The PPC estimates some percentage of the Gross Returns (GR) at time t to be set aside as the Costs of Roads Construction (CRC). They also estimates some percentage of the Gross-Net Returns (GNR) (i.e. the returns after CRC has been deducted) as Maintenance Costs (MC). They further estimates some percentage of the Gross-Net-Net Returns (GNNR) (i.e. the returns after CRC and MC have been deducted) as Administrative Costs (AC) at time t . Our aim is to find the optimal value of wealth that will accrue to the PPMs over a period of time. We found that the optimal Net Returns (NR) accrued to the PPMs is N6.6434*10 14 ( N denotes Naira). Key words: Optimal Investment; Defined Contributory; Pension Plan; Dynamic Optimization; Net Returns; Pension Plan Members; Pension Reform Act; Gross-Net-Net Returns; Pension Plan Company

Optimal control of multidimensional population balance systems for crystal shape manipulation

AbstractOptimal control of multidimensional particulate processes for crystal shape manipulation based on the minimum principle and the method of characteristics is discussed. The posed optimal control problems consist in achieving a desired morphology for a seed crystal population within a free or a limited time scope, while minimizing the net mass of the nucleated crystal particles. An ODE system is obtained by neglecting the natural feedback of the nucleation mass into the crystallization kinetics. Optimal solutions resulting thereof reduce to boundary value problems in only one or two unknown parameters. Moreover, for the original optimal control problem, involving the full process dynamics, a simple feasible sub-optimal solution and an upper bound for the cost function are proposed. The proposed computational approach is compared with dynamic optimization techniques in terms of the efficiency and accuracy by means of a numerical example.

Autonomous dynamic optimization for digital subscriber line networks

Digital subscriber line (DSL) technology allows copper access network operators to offer high-speed links at a reasonable cost. The optimization of the DSL network is a cumbersome task due to the abundance of physical layer configuration parameters and significant differences between lines with respect to interference and attenuation. This paper discusses concepts for dynamic optimization techniques that trade off rate, latency, stability, and power usage in an automated and transparent fashion. The techniques use an initial configuration selection to swiftly obtain a near-optimal configuration and then proceed to acquire additional statistics on noise and interference in order to be able to fine-tune the configuration and gradually increase performance. Optimization results for a DSL network that is carrying video services are presented by aid of the Dynamic Line Management module in the Alcatel-Lucent 5530 Network Analyzer.

Life-Cycle Optimization of Pavement Overlay Systems

Preservation (maintenance and rehabilitation) strategy is the critical factor controlling pavement performance. A life-cycle optimization (LCO) model was developed to determine an optimal preservation strategy for a pavement overlay system and to minimize the total life-cycle energy consumption, greenhouse gas (GHG) emissions, and costs within an analysis period. Using dynamic programming optimization techniques, the LCO model integrates dynamic life-cycle assessment and life-cycle cost analysis models with an autoregressive pavement overlay deterioration model. To improve sustainability in pavement design, a promising alternative material for pavement overlays, engineered cementitious composites (ECCs), was studied. The LCO model was applied to an ECC overlay system, a concrete overlay system, and a hot mixed asphalt (HMA) overlay system. The LCO results show that the optimal preservation strategies will reduce the total life-cycle energy consumption by 5–30%, GHG emissions by 4–40%, and costs by 0.4–12% for the concrete, ECC, and HMA overlay systems compared to the current Michigan Department of Transportation preservation strategies, respectively. The impact of traffic growth on the optimal preservation strategies was also explored.