Optimal control approach to stability criteria on Hill's equations

This paper gives a tutorial on how to prove Lyapunov type criteria by optimal control methods. Firstly, we consider stability criteria on Hill’s equations with nonnegative potential. By optimal control methods developed in 1990s, we obtain several stability criteria including Lyapunov’s criterion, Neǐgauz and Lidskiǐ’s criterion. Secondly, we present stability criteria on Hill’s equations with sign-changing potential in which Brog’s criterion and Krein’s criterion are included.

Combined Life Cycle Assessment and Costing Analysis Optimization Model Using Multiple Criteria Decision Making in Earth-Resource Systems

Life Cycle Assessment (LCA) is a comprehensive method to evaluate all attributes or aspects of potential environmental impact throughout a product’s lifecycle. Financial impacts are often added to the systemic evaluation process to reflect both environmental and economic assessment. For the specific application of LCA informing design of new technologies, when numerous variables are undecided or under defined, the process of forming an inventory of complete dataset is very difficult. Accumulating the early data consumes time, and limits application of LCA to new technologies and projects. As such, LCA may not normally be associated with forecasting or guiding a design/production process with an incomplete data set. Here, a life cycle assessment optimization model (LCAO) is described for incomplete data sets, based on the life cycle inventory (LCI) hybrid method and a modified multiple criteria decision making (MCDM) approach. The approach requires data, but can proceed in the given an incomplete or uncertain data set. The model of the algorithm also shows promising results to reveal previously unknown key variables within the dataset, which can then facilitate the minimization of environmental impact while maximizing economic benefits in product design.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

An Application of the Maximum Theorem in Multi-Criteria Optimization, Properties of Pareto-Retract Mappings, and the Structure of Pareto Sets

In this paper we consider three problems in continuous multi-criteria optimization: An application of the Berge Maximum Theorem, properties of Pareto-retract mappings, and the structure of Pareto sets. The key goal of this work is to present the relationship between the three problems mentioned above. First, applying the Maximum Theorem we construct the Pareto-retract mappings from the feasible domain onto the Pareto-optimal solutions set if the feasible domain is compact. Next, using these mappings we analyze the structure of the Pareto sets. Some basic topological properties of the Pareto solutions sets in the general case and in the convex case are also discussed.

Multi-Criteria Decision Analysis for Usage Based Optimization of Powertrains

The electrification of powertrains leads to an increasing diversification of powertrain configurations. Each single configuration has its specific advantages which appear depending on the usage profile. To find the usage based optimal powertrain in consideration of a variety of evaluation criteria, the powertrains have to be optimized for the usage profile and characteristics have to be extracted from the usage profile. The carbon dioxide emissions of the optimized powertrains and usage based criteria are used in a multi-criteria decision analysis to determine the optimal powertrain for a specific usage profile. The description of characteristic maps forms the objective function of a minimization problem. The determined carbon dioxide emissions are one criterion in a multi-criteria decision process. All considered criteria are at least partly objective so that subjective ratings are eliminated as far as possible. The result is an optimized powertrain for a desired usage under the consideration of objective criteria that are extracted from the usage profile.

考虑碳排放的铁路路基施工机群配置优化

铁路路基施工机群配置关系施工工期,会直接产生施工成本,对生态环境造成重要影响,进而产生较高碳排放量。首先,考虑铁路路基施工工期、施工成本、施工绿色指数及碳排放等目标,建立铁路路基施工机群配置优化模型。其中,将施工机群配置优化模型中各优化目标作为一级指标建立机群配置多目标决策偏好评价指标体系,并将组合数有序加权算子(Combination Ordered Weighted Averaging,C-OWA)法与基于指标间相关性分析的权重确定(Criteria Importance Though Intercriteria Correlation,CRITIC)法结合对指标进行组合赋权。其次,采用基于莱维飞行机制的量子粒子群优化(Quantum Particle Swarm Optimization,QPSO)算法求解该施工机群配置优化模型。最后,以某铁路路基工程某标段为例进行实证分析。结果显示,多目标优化方案较原方案工期提前75 d,成本降低203.257万元,绿色指数提升5.250%,碳排放量降低1.305 t。研究结果可为铁路路基施工机群配置优化提供新思路。

Three-dimensional physical simulation and optimization of water injection of a multi-well fractured-vuggy unit

With complex fractured-vuggy heterogeneous structures, water has to be injected to facilitate oil pro- duction. However, the effect of different water injection modes on oil recovery varies. The limitation of existing numerical simulation methods in representing fractured- vuggy carbonate reservoirs makes numerical simulation difficult to characterize the fluid flow in these reservoirs. In this paper, based on a geological example unit in the Tahe Oilfield, a three-dimensional physical model was designed and constructed to simulate fluid flow in a fractured-vuggy reservoir according to similarity criteria. The model was validated by simulating a bottom water drive reservoir, and then subsequent water injection modes were optimized. These were continuous （constant rate）, intermittent, and pulsed injection of water. Experimental results reveal that due to the unbalanced formation pressure caused by pulsed water injection, the swept volume was expanded and consequently the highest oil recovery increment was achieved. Similar to continuous water injection, intermit- tent injection was influenced by factors including the connectivity of the fractured-vuggy reservoir, well depth, and the injection-production relationship, which led to a relative low oil recovery. This study may provide a constructive guide to field production and for the devel- opment of the commercial numerical models specialized for fractured-vuggy carbonate reservoirs.

Identification welding parameters using complex criteria of quality

In the present paper,the graphical method determination of optimal welding parameters of GMAW is described.Some quality complex criteria that are the ratio of weld sizes have been proposed. These criteria allow optimizing the shape of the weld metal zone,reducing the likelihood of hot cracks formation,and reducing the stress concentration of the weld at the toe. In the proposed methodology,the earlier described equations connecting the welding parameters and the weld dimensions of mild and low-alloy steels are explored. Determination of the optimal welding parameters is carried out graphically taking into account the variance of the welding parameters. An example of using the proposed method destined to determine the optimum welding parameters is provided. It is shown that its accuracy is sufficiently good for a practical application.

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to （-1, 1）. Based on the deduced principle of acceleration factors, a new adaptive PSO algorithm- harmonious PSO （HPSO） is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.

Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.

Multiple Criteria Games-Theory and Applications

After a reivew of basic concepts in multiple criteria optimization, the paper presents a characterization of noncooperative equilibria in multiple criteria games in normal form either by weighted sums or by. order-consistent achievement scalarizing functions, for convex and nonconvex cases. Possible applications of multiple criteria games and such characterizations of their equilibria are indicated. The analysis of multiple criteria games might be especially useful when studying reasons of possible conflict escalation processes and ways of preventing them.

Reactive Search Optimization;Application to Multiobjective Optimization Problems

During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.

Towards Multi-Faceted Test Cases Optimization

The target of software engineering is to produce high quality software product at low cost. Software testing is labour-intensive, ambiguous and error prone activity of software development. How to provide cost-effective strategies for software test cases optimization problem such as classification, minimization, selection, and prioritization has been one of the research focuses in software testing for a long time. Many researchers and academicians have addressed the effectiveness/fitness and optimization of test cases, and obtained many interesting results. However, one issue of paramount importance in software testing i.e. the intrinsic imprecise and uncertainty of test cases fitness, fitness parameters, multi-objective optimization, is left unaddressed. Test cases fitness depends on several parameters. Vagueness of fitness of test cases and their fitness parameters have created the uncertainty in test cases optimization. Cost and adequacy values are incorporated into multi-faceted optimization of test cases. This paper argues test cases optimization requires multi-faceted optimization in order to adequately cater realistic software testing. In this paper, authors have identified several parameters for test cases fitness and multiple objectives for test cases optimization. In addition above, authors have formulated the test cases optimization problem in three different ways using multi-faceted concept. These formulations can be used in future by authors and researchers.

Feasibility of underground NPP and its MCDA-based optimization

The underground nuclear power plant(NPP)makes full use of land resources, reduces costs, makes better use of its passive safety, and avoids radioactivity release into the atmosphere in serious nuclear accidents.In this paper, for obtaining comprehensive and integrated analyses on this new NPP design, we introduce four kinds of underground NPP designs, analyze the feasibility of each design from various aspects, and use the multiple criteria decision analysis method to choose the best option.

Topology Optimization in Damping Structure Based on ESO

The damping material optimal placement for the structure with damping layer is studied based on evolutionary structural optimization (ESO) to maximize modal loss factors. A mathematical model is constructed with the objective function defined as the maximum of modal loss factors of the structure and design constraints function defined as volume fraction of damping material. The optimal placement is found. Several examples are presented for verification. The results demonstrate that the method based on ESO is effective in solving the topology optimization of the structure with unconstrained damping layer and constrained damping layer. This optimization method suits for free and constrained damping structures.

Optimal Route Selection Method Based on Vague Sets

Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

An Integrated Water Resources and Economic Approach for Optimizing Water Allocation Policies

Reservoir/river systems analysis models are generally used in the formulation and evaluation of alternative plans for responding to water related problems and needs. One of the main problems is the water resources allocation and the cost associated with pumping, if needed. Taking the appropriate decision is considered as a techno-economic issue. The case study presented in this paper involves a complex system of three dams, two pumping stations and two diversion structures all serving an agricultural production unit. The objective of this research is to determine a suitable and feasible water allocation/pumping policy as a “trade-off” between minimizing the water deficiency and the cost of pumping. To achieve this objective, a water resources model was developed using HEC-5. A multi-criteria decision approach was implemented to determine the most appropriate water release policy and the capacity of the water diversion facilities. The parameters used were subject to a sensitivity analysis to assess their relative impact on the determined policy. The suggested release policy allows a reduction of half the total of the pumping costs with only 3% reduction in the water allocation reliability, as measured by the failure frequency of demand satisfaction and the average shortage index.

Optimization in Site Selection of Wind Turbine for Energy Using Fuzzy Logic System and GIS—A Case Study for Gujarat

The development of new wind energy project requires studying of many parameters to achieve maximum benefits at the cost of minimum environmental impacts. Using Geographic Information System (GIS), an analytical framework has been developed in this paper with fuzzy logic to evaluate the suitable site for turbines for optimum energy output. The criteria for suitable site for energy optimization are environmental, physical and human factors. The present study helps to assess the appropriate sites for the wind turbines in Gujarat. The result obtained from the study conveys the suitability of the development of wind turbines along the western parts of Gujarat. The suggested model could be used for the future site selection of the wind turbine which in turn could be of orientation for energy planners and decision makers.

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method.

气流分选技术研究进展

双碳背景下,气流分选技术因污染低、成本低、不用水、投资省、工艺简单等优势备受关注。为提升气流分选技术在多领域、多地区、多工况的适应性,解决气流分选物料适用范围窄、细粒分选效率低、设备大型化难等问题迫在眉睫。围绕气流分选理论、技术应用和技术改进3方面系统评述气流分选技术研究进展。首先,介绍了气流分选技术原理,分析气流分选过程颗粒受力及分选理论进展;其次,综述了气流分选在固废、矿业、农业等领域的应用进展,并评析了技术优势和技术短板;再次,系统分析了气流分选供风方式优化和设备结构优化等技术改进进展;最后,给出了通过单体并联和相似放大准则提高设备处理量,通过预干燥、分选干燥一体化、改进入料方式提高潮湿物料适应性与分散性,通过窄粒级分选和保持物料形状一致性的预处理过程提高细粒和薄片物料分选效率等路径。展望了未来气流分选技术在线检测与控制智能化、分选装备大型化、分选效率精细化、分选工艺联合化等方面的发展方向,为气流分选技术进一步研究和推广应用提供重要依据。