Multi-objective stochastic programming for portfolio selection

Generally, in the portfolio selection problem the Decision Maker (DM) considers simultaneously conflicting objectives such as rate of return, liquidity and risk. Multi-objective programming techniques such as goal programming (GP) and compromise programming (CP) are used to choose the portfolio best satisfying the DM’s aspirations and preferences. In this article, we assume that the parameters associated with the objectives are random and normally distributed. We propose a chance constrained compromise programming model (CCCP) as a deterministic transformation to multi-objective stochastic programming portfolio model. CCCP is based on CP and chance constrained programming (CCP) models. The proposed program is illustrated by means of a portfolio selection problem from the Tunisian stock exchange market.     

Multi-objective decision analysis for competence-oriented project portfolio selection

This paper develops a multi-objective optimization model for project portfolio selection taking employee competencies and their evolution into account. The objectives can include economic gains as well as gains expressed in terms of aggregated competence increments according to pre-defined profiles. In order to determine Pareto-optimal solutions, the overall problem is decomposed into a master problem addressing the portfolio selection itself, and a slave problem dealing with a suitable assignment of personnel to the work packages of the selected projects over time. We provide an asymptotic approximation of the problem by a linearized formulation, which allows an efficient and exact solution of the slave problem. For the solution of the master problem, we compare the multi-objective metaheuristics NSGA-II and P-ACO. Experimental results both for synthetically generated test instances and for real-world test instances, based on an application case from the E-Commerce Competence Center Austria, are presented.     

Multi-objective possibilistic model for portfolio selection with transaction cost

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.     

Aircraft selection modeling: a multi-step heuristic to enumerate airlift alternatives

Annals of Operations Research - We consider the use of the new C-130J-30 aircraft for long distance (strategic) cargo movement. Currently, only large aircraft, the C-5 and the C-17, are identified...     

Categorical data clustering with automatic selection of cluster number

In this paper, we investigate the problem of determining the number of clusters in the k-modes based categorical data clustering process. We propose a new categorical data clustering algorithm with automatic selection of k. The new algorithm extends the k-modes clustering algorithm by introducing a penalty term to the objective function to make more clusters compete for objects. In the new objective function, we employ a regularization parameter to control the number of clusters in a clustering process. Instead of finding k directly, we choose a suitable value of regularization parameter such that the corresponding clustering result is the most stable one among all the generated clustering results. Experimental results on synthetic data sets and the real data sets are used to demonstrate the effectiveness of the proposed algorithm.     

Estimation and model selection in a class of semiparametric models for cluster data

Stimulated by a study in Bangladesh about the first birth interval, we propose a semivarying-coefficient model for cluster data analysis. We consider the estimation procedure for the proposed model and establish the asymptotic results of the proposed estimators. Furthermore, we employ the cross-validation (CV) to identify the constant coefficients. The associated asymptotic properties are rigorously examined. Simulation studies are conducted to investigate the performance of the proposed estimation and the CV-based model selection procedure for finite sample size. Finally, our methods are used to analyse the aforementioned data set to explore how several factors affect the first birth interval in Bangladesh.     

Splitting for Multi-objective Optimization

We introduce a new multi-objective optimization (MOO) methodology based the splitting technique for rare-event simulation. The method generalizes the elite set selection of the traditional splitting framework, and uses both local and global sampling to sample in the decision space. In addition, an ??-dominance method is employed to maintain good solutions. The algorithm was compared with state-of-the art MOO algorithms using a prevailing set of benchmark problems. Numerical experiments demonstrate that the new algorithm is competitive with the well-established MOO algorithms and that it can outperform the best of them in various cases.     

Two alternatives for the cubic algorithm

Two alternatives are proved that allow application of the cubic algorithm for finding approximate solutions of the general non-convex and non-robust mathematical programming problem.     

Coalitional values for cooperative games withr alternatives

In this paper we consider games withn players andr alternatives. In these games the worth of a coalition depends not only on that coalition, but also on the organization of the other players in the game. We propose two coalitional values that are extensions of the Owen value (1977). We give some relations with the Owen value and an axiomatic characterization of each value introduced in this work. Finally, we compare both values. This research has been supported partially by U.P.V./E.H.U. research project 035.321-HB048/97, and the DGES of MEC project PB96-0247.     

Some alternatives for conditional principal component analysis

Classically principal component analysis is one of the most used techniques for exploring the multivariate association pattern of variables. On the other hand, conditioning is one of the most promising ideas for controlling the variability of observed data. Here we present a review of some conditioning methods from the analysis of residuals of a parametric model to the analysis of the local variation defined by means of a non‐oriented graph of individuals, this variation being defined from the deviation from a local mean or alternatively from the differences among contiguous vertices. We will compare these approaches and will show that under some conditions they give comparable results. Finally, we will present an example of application to illustrate the results previously stated. Copyright © 2000 John Wiley & Sons, Ltd.     

An approach for preference ranking of alternatives

An approach for determining the ranking of a set of alternatives is developed. It is based on concepts of conditional probability. The approach evaluates the set of alternatives and arrives at a preference ranking of either the entire set or a subset of available alternatives. Results of preliminary tests involving generated problems are favorable.     

多目标线性规划的一种交互式单纯形算法

本文基于分析有效极点解的有效变量的特点以及在有效点处各个目标函数的数值来得到改进的搜索方向的研究思想,提出了求解目标函数和约束均为线性的多目标线性规划问题的一种交互式算法。该方法可以保证每一步得到的解均为有效极点解,且根据决策者的偏好不断得到改进,直至最终得到满意的最终解。     

Polynomial alternatives for the group of affine motions

It is known that every polycyclic-by-finite group – even if it admits no affine structure – allows a polynomial structure of bounded degree. A major obstacle to a further development of the theory of these polynomial structures is that the group of the polynomial diffeomorphisms of , in contrast to the group of affine motions, is no longer a finite dimensional Lie group. In this paper we construct a family of (finite dimensional) Lie groups, even linear algebraic groups, of polynomial diffeomorphisms, which we call weighted groups of polynomial diffeomorphisms. It turns out that every polycyclic-by-finite group admits a polynomial structure via these weighted groups; in the nilpotent (and other) case(s), we can sharpen, by specifying a nice set of weights, the existence results obtained in earlier work. We introduce unipotent polynomial structures of nilpotent groups and show how the existence of such polynomial structures is closely related to the existence of simply transitive actions of the corresponding Mal`cev completion. This, and other properties, provide a strong analogy with the situation of affine structures and simply transitive affine actions considered e.g. in the work of Fried, Goldman and Hirsch. Received November 30, 1998; in final form March 10, 1999     

Stochastic dominance tests for ranking alternatives under ambiguity

Ambiguity in decision making is a condition associated with the existence of one-to-many, or more generally, many-to-many relations between outcomes and probabilities of outcomes. The current use of stochastic dominance tests to rank decision alternatives assumes that outcome probability distributions are unambiguous. This paper first establishes procedures to identify an envelope of the ambiguously-described probability-outcome mapping, then extends stochastic dominance theorems to provide tests that partition alternatives into dominated and non-dominated sets.     

Likelihood ratio tests for symmetry against one-sided alternatives

A random variableX is said to have a symmetric distribution (about 0) if and only ifX and –X are, identically distributed. By considering various types of partial orderings between the distributions ofX and –X, one obtains various notions of skewness or one-sided bias. In this paper we study likelihood ratio tests for testing the symmetry of a discrete distribution about zero against the alternatives, (i)X is stochastically greater than –X; and (ii) pr(X=j)pr(X=–j) for allj>0. In the process, we obtain maximum likelihood estimators of the distribution function under the above alternatives. The asymptotic null distributions of the test statistics have been obtained and are of the chi-bar square type. A simulation study was performed to compare the powers of these tests with other tests.     

A value for games withn players andr alternatives

We study value theory for a class of games called games withn players andr alternatives. In these games, each of then players must choose one and only one of ther alternatives. A linear, efficient value is obtained using three characterizations, two of which are axiomatic. This value yields an a priori evaluation for each player relative to each alternative.     

A generalized Hollander-Proschan type test for NBUE alternatives

In this note we develop a family of test statistics for testing exponentiality against NBUE alternatives. The asymptotic distribution of the test statistics is derived. The test statistics are shown to be asymptotically normal and consistent. This family of test statistics includes the test proposed by Hollander and Proschan (1975) as a special case. Efficiency studies have also been done.     

An Interactive Algorithm for Multi-objective Route Planning

We address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem, where the UAV has to visit several targets and return to the base. We model this problem as a combination of two combinatorial problems. First, the path to be followed between each pair of targets should be determined. We model this as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. We model this as a multi-objective traveling salesperson problem (MOTSP). The overall problem is a combination of these two problems, which we define as a generalized MOTSP. We develop an exact interactive approach to identify the best paths and the best tour of a decision maker under a linear utility function.