基于后悔-欣喜值最大一致性的多属性决策及其在区域绿色经济评价应用

现实中区域绿色经济发展水平评价尚未关注信息的异质性、偏好多维性和决策者有限理性等问题,据此本文提出一种基于后悔理论的多维偏好评价方法.首先,构建区域绿色经济发展水平评价指标体系.其次,通过计算方案的后悔值与欣喜值并得出综合排序值.然后,通过定义综合排序值与专家决策偏好的一致性和不一致性程度,建立决策优化模型,求解获得指标权重,进而进行方案排序和优选.以三明市为例,研究发现,决策者的后悔规避心理对绿色经济评价存在影响,且规避系数越大,投资与消费指标对方案排序影响越大.研究方法兼顾了指标数据分析和专家心理偏好,有较强的有效性、合理性和实用性.     

基于后悔理论的多时点风险灰靶决策方法

针对属性值为区间灰数,时点权重未知的多时点风险决策问题,考虑决策者的心理行为,提出一种基于后悔理论的灰靶决策方法.给出区间灰数的大小比较方法和距离测度,定义正负理想方案,根据决策者相对于负理想方案的欣喜心理与相对于正理想方案的后悔心理定义欣喜后悔值函数,采用时间度和熵值相结合的方法确定时点权重,求出各方案多时点综合欣喜后悔值并最终确定出方案的排序.最后,通过具体算例分析结果验证了该方法的可行性和有效性.     

基于后悔理论及云模型的多属性群决策方法

针对属性值为不确定语言信息的群决策问题,考虑决策者后悔规避的心理行为特征,提出了一种基于后悔理论及云模型的多属性群决策方法.首先,将不确定语言值转化为云模型;其次,给出一种新的考虑决策者期望的方法求解其权重;然后,通过给出的云模型效用函数构建效用值矩阵,进而通过后悔-欣喜函数构建感知效用矩阵确定方案的综合感知效用,并根据其排序;最后,通过实例说明了方法的可行性和科学有效性.     

考虑后悔行为的灰色应急决策方法

针对实施不同方案各情景发生概率不同的应急响应风险决策问题,提出基于后悔理论和区间灰数信息的突发事件应急决策方法.方法考虑到决策者的后悔规避的心理行为,建立了方案两两比较的灰色后悔-欣喜值矩阵,并通过计算每个方案相对于其他所有方案的总体灰色后悔-欣喜值对方案进行排序;最后,通过具体算例验证了方法的可行性和有效性.     

考虑全局投标者后悔心理行为的组合拍卖的投标均衡策略

许多实验研究表明投标者在拍卖过程中往往表现出预期后悔心理行为,并且投标者的预期后悔心理行为将会对投标策略产生影响,但以往大多是针对单物品拍卖研究考虑投标者后悔心理行为的投标均衡策略,而针对多物品拍卖情形的研究较少关注。本文着重研究了考虑投标者后悔心理行为的组合拍卖的投标均衡策略问题,在全局投标者存在预期后悔心理行为的假设下,依据Engelbrecht-Wiggans和Katok提出的后悔函数刻画了投标者的后悔心理行为,在此基础上,构建了组合拍卖模型,通过分析给出了全局投标者投标均衡策略需要满足的充分和必要条件。进一步地,依据构建的模型,通过数值实验分析了局部投标者人数、组合效应系数和全局投标者后悔参数对全局投标者投标策略的影响。最后,通过一个关于无线电频谱组合拍卖的算例说明了本文给出的模型及投标均衡策略确定方法的潜在应用和优越性。     

台风灾害预警—应急响应两阶段决策方法

针对具有不确定情景预测信息的台风灾害应急决策问题,提出一种同时考虑事前预警措施和事中应急响应措施的两阶段决策方法。该方法中,首先考虑决策者的风险规避行为,通过计算各应急响应措施的实施效用来确定针对不同预警措施和灾害情景下的最优应急响应措施;其次考虑决策者的后悔规避行为,计算任意两个预警措施相比较的后悔—欣喜值,并通过最大熵法将不确定情景的区间概率转化为点概率;在此基础上,计算出各预警措施的综合后悔—欣喜值及排序值,并最终确定第一阶段的最优预警措施和第二阶段针对各灾害情景的最优应急响应措施。最后,通过算例研究和对比分析说明该方法的可行性和有效性。     

在线时间序列搜索的风险补偿模型

对于在线时间序列搜索问题,在假设对未来信息有一定的预期下,提出了在线时间序列搜索的风险补偿模型,进一步研究了模型的求解,给出了模型的一个最优策略,并通过数值计算讨论了最优策略的补偿函数随参数变化规律.数值实验结果表明,随着风险容忍度的增大与预期区间下限的增大,补偿函数均增大且趋于收敛;随着预期概率的增大与预期区间上限的减少,补偿函数分别增大.研究结果丰富了在线时间序列搜索的理论且具有实际应用价值.     

多重因素约束下的网格搜索对策问题研究

首次基于搜索成本及搜索资源等限制因素,构造局中人面向多重约束条件的可行策略集合,建立相应的搜索空间;在给定搜索点权值的基础上,考虑搜索成本与搜索成功概率等因素,构造相应的支付函数,建立多重因素约束下的网格搜索对策模型.为简化模型求解,将对策论问题转化为约束最优化问题,求解约束问题获得最优值,转化为模型的对策值,并给出双方最优混合策略.最后,给出军事想定实例,说明上述模型的实用性及方法的有效性.     

犹豫模糊环境下考虑后悔行为的双边匹配决策方法

针对犹豫模糊环境下的双边匹配问题,考虑匹配主体存在决策后将所选方案与其他方案进行对比的心理行为,即决策者是有限理性的,提出一种考虑后悔规避行为的双边匹配方法.首先,对犹豫模糊环境下的双边匹配问题进行了描述;然后,将犹豫模糊信息转化为对主体的满意度,在此基础上,依据后悔理论用满意度构建效用函数和后悔-欣喜函数,转化为相对于匹配主体的感知效用,构造感知效用计算规则,建立双边匹配模型,求解模型获取感知效用最大化.最后,采用IT服务企业的实例来验证该方法的可行性与有效性.     

基于遗传算法的对规避目标搜索模型

分析了在应召条件下对规避目标搜索行动的特点,然后采用遗传算法建立了可用于辅助搜索决策者制定协同搜索方案的模型,为分析应召搜索提供了新的方法,该方法克服了传统的运筹学搜索论在协同行动等复杂条件下寻求最优搜索方案的不足     

Efficient Genetic Algorithms Using Simple Genes Exchange Local Search Policy for the Quadratic Assignment Problem

In this paper, we describe an approach for solving the quadratic assignment problem (QAP) that is based on genetic algorithms (GA). It will be shown that a standard canonical GA (SGA), which involves genetic operators of selection, reproduction, crossover, and mutation, tends to fall short of the desired performance expected of a search algorithm. The performance deteriorates significantly as the size of the problem increases. To address this syndrome, it is common for GA-based techniques to be embedded with deterministic local search procedures. It is proposed that the local search should involve simple procedure of genome reordering that should not be too complex. More importantly, from a computational point of view, the local search should not carry with it the full cost of evaluating a chromosome after each move in the localized landscape. Results of simulation on several difficult QAP benchmarks showed the effectiveness of our approaches.     

Generalised graph colouring by a hybrid of local search and constraint programming

The IMPASSE class of local search algorithms have given good results on many vertex colouring benchmarks. Previous work enhanced IMPASSE by adding the constraint programming technique of forward checking, in order to prune colouration neighbourhoods during search. On several large graphs the algorithm found the best known colourings. This paper extends the work by improving the heuristics and generalising the approach to bandwidth multicolouring. It is shown to give better results than a related search algorithm on an integer programming model, and to be competitive with published results. Experiments indicate that stronger constraint propagation further improves search performance, but that a symmetry breaking technique has unpredictable effects.     

Continuous functions minimization by dynamic random search technique

Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to 1 by using the basic random search technique, but it takes too much time to reach the global minimum. Improving the basic random search technique may decrease the solution time. In this study, in order to obtain the global minimum fastly, a new random search algorithm is suggested. This algorithm is called as the Dynamic Random Search Technique (DRASET). DRASET consists of two phases, which are general search and local search based on general solution. Knowledge related to the best solution found in the process of general search is kept and then that knowledge is used as initial value of local search. DRASET’s performance was experimented with 15 test problems and satisfactory results were obtained.     

Exploring relaxation induced neighborhoods to improve MIP solutions

Given a feasible solution to a Mixed Integer Programming (MIP) model, a natural question is whether that solution can be improved using local search techniques. Local search has been applied very successfully in a variety of other combinatorial optimization domains. Unfortunately, local search relies extensively on the notion of a solution neighborhood, and this neighborhood is almost always tailored to the structure of the particular problem being solved. A MIP model typically conveys little information about the underlying problem structure. This paper considers two new approaches to exploring interesting, domain-independent neighborhoods in MIP. The more effective of the two, which we call Relaxation Induced Neighborhood Search (RINS), constructs a promising neighborhood using information contained in the continuous relaxation of the MIP model. Neighborhood exploration is then formulated as a MIP model itself and solved recursively. The second, which we call guided dives, is a simple modification of the MIP tree traversal order. Loosely speaking, it guides the search towards nodes that are close neighbors of the best known feasible solution. Extensive computational experiments on very difficult MIP models show that both approaches outperform default CPLEX MIP and a previously described approach for exploring MIP neighborhoods (local branching) with respect to several different metrics. The metrics we consider are quality of the best integer solution produced within a time limit, ability to improve a given integer solution (of both good and poor quality), and time required to diversify the search in order to find a new solution.Mathematics Subject Classification (2000):20E28, 20G40, 20C20Acknowledgement We wish to thank the two anonymous referees for their helpful comments.     

A Hybrid Metaheuristic for the Quadratic Assignment Problem

The quadratic assignment problem (QAP) is known to be NP-hard. We propose a hybrid metaheuristic called ANGEL to solve QAP. ANGEL combines the ant colony optimization (ACO), the genetic algorithm (GA) and a local search method (LS). There are two major phases in ANGEL, namely ACO phase and GA phase. Instead of starting from a population that consists of randomly generated chromosomes, GA has an initial population constructed by ACO in order to provide a good start. Pheromone acts as a feedback mechanism from GA phase to ACO phase. When GA phase reaches the termination criterion, control is transferred back to ACO phase. Then ACO utilizes pheromone updated by GA phase to explore solution space and produces a promising population for the next run of GA phase. The local search method is applied to improve the solutions obtained by ACO and GA. We also propose a new concept called the eugenic strategy intended to guide the genetic algorithm to evolve toward a better direction. We report the results of a comprehensive testing of ANGEL in solving QAP. Over a hundred instances of QAP benchmarks were tested and the results show that ANGEL is able to obtain the optimal solution with a high success rate of 90%. This work was supported in part by the National Science Council, R.O.C., under Contract NSC 91-2213-E-005-017.     

Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems

A robust search algorithm should ideally exhibit reasonable performance on a diverse and varied set of problems. In an earlier paper Lim et al. (Computational Optimization and Applications, vol. 15, no. 3, 2000), we outlined a class of hybrid genetic algorithms based on the k-gene exchange local search for solving the quadratic assignment problem (QAP). We follow up on our development of the algorithms by reporting in this paper the results of comprehensive testing of the hybrid genetic algorithms (GA) in solving QAP. Over a hundred instances of QAP benchmarks were tested using a standard set of parameters setting and the results are presented along with the results obtained using simple GA for comparisons. Results of our testing on all the benchmarks show that the hybrid GA can obtain good quality solutions of within 2.5% above the best-known solution for 98% of the instances of QAP benchmarks tested. The computation time is also reasonable. For all the instances tested, all except for one require computation time not exceeding one hour. The results will serve as a useful baseline for performance comparison against other algorithms using the QAP benchmarks as a basis for testing.     

A multi-objective heuristic approach for the casualty collection points location problem

In this paper, we formulate the casualty collection points (CCPs) location problem as a multi-objective model. We propose a minimax regret multi-objective (MRMO) formulation that follows the idea of the minimax regret concept in decision analysis. The proposed multi-objective model is to minimize the maximum per cent deviation of individual objectives from their best possible objective function value. This new multi-objective formulation can be used in other multi-objective models as well. Our specific CCP model consists of five objectives. A descent heuristic and a tabu search procedure are proposed for its solution. The procedure is illustrated on Orange County, California.     

后悔厌恶对商业银行存贷款定价的影响

存贷利差收入是银行主要的收入来源,存贷款定价问题是银行经营管理、风险控制的核心之一.在"代理商"模型的基础上,引入后悔系数和后悔效用函数,建立了基于双重厌恶银行的期望效用函数,并利用极值理论,确定了最优存贷款利差.双重厌恶期望效用函数反映了银行风险厌恶和后悔厌恶的双重特点,解决了现有研究忽略后悔情绪导致模型出现偏差的问题.从理论上分析了双重厌恶型银行最优的存贷款利差的决策问题,给出了其与市场风险、信用风险以及后悔情绪之间的关系.通过与纯风险厌恶型银行最优决策的对比发现,对于双重厌恶型银行来说,信用风险对存贷利差影响的弹性要小于纯风险厌恶型银行,而后悔厌恶程度越大其弹性就越小.由于后悔心理的存在减小了信用风险对存贷款利差影响的弹性.     

支持RDF图上关键字搜索的交互式谓词推荐

结构化查询语言SPARQL支持对RDF数据的准确查询,但它需要用户了解RDF数据模式和查询语法.关键字搜索在可用性方面明显优于结构化查询,但容易因语义模糊性造成搜索空间巨大.利用RDF谓词信息来扩展查询,通过一个RDF图上关键字搜索的互动过程,允许用户通过选择一些谓词来限制查询的语义,以减少关键字的模糊性.     

A composite-neighborhood tabu search approach to the traveling tournament problem

The Traveling Tournament Problem (TTP) is a combinatorial problem that combines features from the traveling salesman problem and the tournament scheduling problem. We propose a family of tabu search solvers for the solution of TTP that make use of complex combination of many neighborhood structures. The different neighborhoods have been thoroughly analyzed and experimentally compared. We evaluate the solvers on three sets of publicly available benchmarks and we show a comparison of their outcomes with previous results presented in the literature. The results show that our algorithm is competitive with those in the literature.