Conditional Preference Networks with User's Genuine Decisions

User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP-net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP-net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP-net model that we call the CP-net with Comfort (CPC-net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC-net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC-net. The exact relation can be obtained by performing dominance testing in the corresponding CP-net and comparing the numeric comforts.     

无背索部分斜拉桥初始索力优化

在分析斜拉桥索力影响矩阵的基础上,以结构应变能最小为目标函数,建立了无背索部分斜拉桥索力优化模型,结合某旅游路立交桥进行索力优化分析,得出了一些具有一定参考价值的建议。     

结构双重调谐质量阻尼器(DTMD)控制策略研究

MTMD比TMD具有明显高的鲁棒性。然而,MTMD中各质量块位移不同,总的来说,呈递增或递减趋势,因而各质量块的有效性未得以充分发挥。再者,MTMD比TMD复杂,因而其造价比TMD高。鉴于此,本文提出一种新控制策略——双重调谐质量阻尼器(DTMD)。基于定义的二类最优化目标函数,评价了双重调谐质量阻尼器(DTMD)的控制性能。数值结果表明,利用第二类设计准则(新准则)设计的双重调谐质量阻尼器(DTMD)与由5个TMD组成的MTMD具有近似相同的有效性和鲁棒性。因此,双重调谐质量阻尼器是一种先进的控制策略。     

Comprehensive Pareto Efficiency in robust counterpart optimization

In this paper, an innovative concept named Comprehensive Pareto Efficiency is introduced in the context of robust counterpart optimization, which consists of three sub-concepts: Pareto Robust Optimality (PRO), Global Pareto Robust Optimality (GPRO) and Elite Pareto Robust Optimality (EPRO). Different algorithms are developed for computing robust solutions with respect to these three sub-concepts. As all sub-concepts are based on the Probability of Constraint Violation (PCV), formulations of PCV under different probability distributions are derived and an alternative way to calculate PCV is also presented. Numerical studies are drawn from two applications (production planning problem and orienteering problem), to demonstrate the Comprehensive Pareto Efficiency. The numerical results show that the Comprehensive Pareto Efficiency has important significance for practical applications in terms of the evaluation of the quality of robust solutions and the analysis of the difference between different robust counterparts, which provides a new perspective for robust counterpart optimization.     

Impulse controls and uncertainty in economics: Method and application

We develop a stochastic optimal control framework to address an important class of economic problems where there are discontinuities and a decision maker is able to undertake impulse controls in response to unexpected disturbances. Our contribution is two fold: (1) to develop a linear programming algorithm that produces a consistent approximation of the maximum value and optimal policy functions in the context of stochastic impulse controls; and (2) to illustrate the economic benefits of impulse controls optimized, using our framework, and calibrated to the population dynamics of a marine fishery. We contend that the framework has wide applicability and offers the possibility of higher economic pay-off for a wide-range of policy problems in the presence of discontinuities and adverse shocks.     

Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation

In the last two decades, multiobjective optimization has become main stream and various multiobjective evolutionary algorithms (MOEAs) have been suggested in the field of evolutionary computing (EC) for solving hard combinatorial and continuous multiobjective optimization problems. Most MOEAs employ single evolutionary operators such as crossover, mutation and selection for population evolution. In this paper, we suggest a multiobjective evolutionary algorithm based on multimethods (MMTD) with dynamic resource allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm employs two well known population based stochastic algorithms namely MOEA/D and NSGA-II as constituent algorithms for population evolution with a dynamic resource allocation scheme. We have examined the performance of the proposed MMTD on two different MOPs test suites: the widely used ZDT problems and the recently formulated test instances for the special session on MOEAs competition of the 2009 IEEE congress on evolutionary computation (CEC’09). Experimental results obtained by the suggested MMTD are more promising than those of some state-of-the-art MOEAs in terms of the inverted generational distance (IGD)-metric on most test problems.     

Mathematical modelling and a meta-heuristic for flexible job shop scheduling

This study develops new solution methodologies for the flexible job shop scheduling problem (F-JSSP). As a first step towards dealing with this complex problem, mathematical modellings have been used; two novel effective position- and sequence-based mixed integer linear programming (MILP) models have been developed to fully characterise operations of the shop floor. The developed MILP models are capable of solving both partially and totally F-JSSPs. Size complexities, solution effectiveness and computational efficiencies of the developed MILPs are numerically explored and comprehensively compared vis-à-vis the makespan optimisation criterion. The acquired results demonstrate that the proposed MILPs, by virtue of its structural efficiencies, outperform the state-of-the-art MILPs in literature. The F-JSSP is strongly NP-hard; hence, it renders even the developed enhanced MILPs inefficient in generating schedules with the desired quality for industrial scale problems. Thus, a meta-heuristic that is a hybrid of Artificial Immune and Simulated Annealing (AISA) Algorithms has been proposed and developed for larger instances of the F-JSSP. Optimality gap is measured through comparison of AISA’s suboptimal solutions with its MILP exact optimal counterparts obtained for small- to medium-size benchmarks of F-JSSP. The AISA’s results were examined further by comparing them with seven of the best-performing meta-heuristics applied to the same benchmark. The performed comparative analysis demonstrated the superiority of the developed AISA algorithm. An industrial problem in a mould- and die-making shop was used for verification.     

On optimality and duality for multiple-objective optimization under generalized type I univexity

In this paper, we deal with the multiple-objective optimization problems, considering an improved definition of generalized type I univex function. Some optimality conditions as well as some duality relations are established.     

摩擦接触系统多极边界元法二次建模与数值优化

讨论了摩擦接触系统的建模与求解问题。将系统方程组的多极边界元法求解归结为变分问题并证明其等价性。在系统最优化数学模型的基础上,通过引入严格凸二次函数建立系统的二次规划模型,证明其最优解存在且唯一,并给出模型求解的最优性条件。