Search results

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

The purpose of this paper is to create a flight route optimization for all flights that aims to minimize the total cost consists of fuel cost, ground delay cost and air delay cost over the fixed route and free route airspaces.

Efficient usage of current available airspace capacity becomes more and more important with the increasing flight demands. The efficient capacity usage of an airspace is generally in contradiction to optimum flight efficiency of a single flight. It can only be achieved with the holistic approach that focusing all flights over mixed airspaces and their routes instead of single flight route optimization for a single airspace. In the scope of this paper, optimization methods were developed to find the best route planning for all flights considering the benefits of all flights not only a single flight. This paper is searching for an optimization to reduce the total cost for all flights in mixed airspaces. With the developed optimization models, the determination of conflict-free optimum routes and delay amounts was achieved with airway capacity and separation minimum constraints in mixed airspaces. The mathematical model and the simulated annealing method were developed for these purposes.

The total cost values for flights were minimized by both developed mathematical model and simulated annealing algorithm. With the mathematical model, a reduction in total route length of 4.13% and a reduction in fuel consumption of 3.95% was achieved in a mixed airspace. The optimization algorithm with simulated annealing has also 3.11% flight distance saving and 3.03% fuel consumption enhancement.

Although the wind condition can change the fuel consumption and flight durations, the paper does not include the wind condition effects. If the wind condition effect is considered, the shortest route may not always cause the least fuel consumption especially under the head wind condition.

The results of this paper show that a flight route optimization as a holistic approach considering the all flight demand information enhances the fuel consumption and flight duration. Because of this reason, the developed optimization model can be effectively used to minimize the fuel consumption and reduce the exhaust emissions of aircraft.

This paper develops the mathematical model and simulated annealing algorithm for the optimization of flight route over the mixed airspaces that compose of fixed and free route airspaces. Each model offers the best available and conflict-free route plan and if necessary required delay amounts for each demanded flight under the airspace capacity, airspace route structure and used separation minimum for each airspace.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this paper is to apply some surrogate-assisted optimization techniques in order to improve the performances of a five-phase permanent magnet machine in the context of a complex model requiring computation time.

An optimal control of four independent currents is proposed in order to minimize the total losses with the respect of functioning constraints. Moreover, some geometrical parameters are added to the optimization process allowing a co-design between control and dimensioning.

The optimization results prove the remarkable effect of using the freedom degree offered by a five-phase structure on iron and magnets losses. The performances of the five-phase machine with concentrated windings are notably improved at high speed (16,000 rpm).

The effectiveness of the method allows solving the challenge which consists in taking into account inside the control strategy the eddy-current losses in magnets and iron. In fact, magnet losses are a critical point to protect the machine from demagnetization in flux-weakening region.

Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization.

In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center.

This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method.

The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights.

The paper shows how SD simulation and multi-parameter optimization can provide insights into the field of customer support. However, the existing literature lacks a holistic view of these kinds of simulation systems, as well as a multi-parameter optimization method for SD methodology.

This paper aims to summarize design rules based on the process characteristics of selective laser melting (SLM) and structural optimization and apply the design rules in the lightweight design of an aluminum alloy antenna bracket. The design goal is to reduce 30 per cent of the weight while maintaining the stress levels in the original part.

To reduce weight as much as possible, the titanium alloy with higher specific strength was selected during the process of optimization. The material distribution of the bracket was improved by the topology optimization design. The redesign for SLM was used to obtain an optimization model, which was more suitable for SLM. The component performance was improved by shape optimization. The modal analysis data of the structural optimization model were compared with those of the stochastic lightweight model to verify the structural optimization model. The scanning data were compared with those of the original model to verify whether the model was suitable for SLM.

Structural optimization design for antenna bracket realized the mass decrease of 30.43 per cent and the fundamental frequency increase of 50.18 per cent. The modal analysis data of the stochastic lightweight model and the structural optimization model indicated that the optimization performance of structural optimization method was better than that of the stochastic lightweight method. The comparison results between the scanning data of the forming part and the original data confirmed that the structural optimization design for SLM lightweight component could achieve the desired forming accuracy.

This paper summarizes geometric constraints in SLM and derives design rules of structural optimization based on the process characteristics of SLM. SLM design rules make structural optimization design more reasonable. The combination of structural optimization design and SLM can improve the performance of lightweight antenna bracket significantly.

The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made.

The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps.

The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view.

A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization.

The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization realized by means of the Multiobjective Particle Swarm Optimization (MOPSO).

In fuzzy modeling, complexity, interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. Since the performance of the IG‐RBFNN model is directly affected by some parameters, such as the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials in the consequent parts of the rules, the authors carry out both structural as well as parametric optimization of the network. A multi‐objective Particle Swarm Optimization using Crowding Distance (MOPSO‐CD) as well as O/WLS learning‐based optimization are exploited to carry out the structural and parametric optimization of the model, respectively, while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.

The performance of the proposed model is illustrated with the aid of three examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

A MOPSO‐CD as well as O/WLS learning‐based optimization are exploited, respectively, to carry out the structural and parametric optimization of the model. As a result, the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.