Preference modelling by estimating local utility functions for multiobjective optimization

This paper is intended to design goal programming models for capturing the decision maker's (DM's) preference information and for supporting the search for the best compromise solutions in multiobjective optimization. At first, a linear goal programming model is built to estimate piecewise linear local utility functions based on pairwise comparisons of efficient solutions as well as objectives. The interactive step trade-off method (ISTM) is employed to generate a typical subset of efficient solutions of a multiobjective problem. Another general goal programming model is then constructed to embed the estimated utility functions in the original multiobjective problem for utility optimization using ordinary nonlinear programming algorithms. This technique, consisting of the ISTM method and the newly investigated search process, facilitates the identification and elimination of possible inconsistent information which may exist in the DM's preferences. It also provides various ways to carry out post-optimality analysis to test the robustness of the obtained best solutions. A modified nonlinear multiobjective management problem is taken as example to demonstrate the technique.     

Solving the multiobjective possibilistic linear programming problem

In conventional multiobjective decision making problems, the estimation of the parameters of the model is often a problematic task. Normally they are either given by the decision maker (DM), who has imprecise information and/or expresses his considerations subjectively, or by statistical inference from past data and their stability is doubtful. Therefore, it is reasonable to construct a model reflecting imprecise data or ambiguity in terms of fuzzy sets for which a lot of fuzzy approaches to multiobjective programming have been developed. In this paper we propose a method to solve a multiobjective linear programming problem involving fuzzy parameters (FP-MOLP), whose possibility distributions are given by fuzzy numbers, estimated from the information provided by the DM. As the parameters, intervening in the model, are fuzzy the solutions will be also fuzzy. We propose a new Pareto Optimal Solution concept for fuzzy multiobjective programming problems. It is based on the extension principle and the joint possibility distribution of the fuzzy parameters of the problem. The method relies on α-cuts of the fuzzy solution to generate its possibility distributions. These ideas are illustrated with a numerical example.     

The analytic continuation of solutions to elliptic boundary value problems in two independent variables

An explicit representation is derived for the continuation across an analytic boundary of the solution to a boundary value problem for an analytic elliptic equation of second order in two independent variables. The representation is in terms of Cauchy data on the boundary and the complex Riemann function. This is equivalent to a representation for the solution to Cauchy's problem given by Henrici in 1957. It is confirmed that the method of complex characteristics is satisfactory for locating real singularities in the solution provided that the Riemann function is entire in its four arguments. Applications to Laplace's and Helmholtz's equations are discussed. By inserting known, simple solutions to the latter equation into the representation formula, several nontrivial integral relations involving the Bessel function J₀, and a possibly new series expansion for J_μ(x), are found.     

Optimality conditions and duality results for nonsmooth vector optimization problems with the multiple interval-valued objective function

In this paper, both Fritz John and Karush-Kuhn-Tucker necessary optimality conditions are established for a (weakly) LU-efficient solution in the considered nonsmooth multiobjective programming problem with the multiple interval-objective function. Further, the sufficient optimality conditions for a (weakly) LU-efficient solution and several duality results in Mond-Weir sense are proved under assumptions that the functions constituting the considered nondifferentiable multiobjective programming problem with the multiple interval-objective function are convex.     

求群体多目标决策问题的一种可行方向交互算法

对于群体多目标决策问题,决策者可以各自的关于目标之间的权衡比表达其偏爱信息并进行决策.当个体权衡比具有加性性质时可得群体权衡比.本文以此构造一种求解群体非线性规划问题的交互算法.迭代中基于求解决非线性规划的Topkis-Veinott方法构造可行方向.在一定的条件下,算法收敛于所讨论问题的群体满意解.     

A multiobjective based approach for mathematical programs with linear flexible constraints

In this paper a mathematical problem with linear flexible constraints is considered. In order to solve the problem an approach is proposed based on multiobjective linear programming. Indeed, allowing violations for the constraints, and using multiobjective linear programming to minimize these violations, a subset of solution set which has less violations, namely efficiently feasible set, is obtained. Then, the corresponding objective function is optimized over efficiently feasible set in order to obtain an optimal solution. An application of the proposed approach in pattern classification is introduced.     

Application of multiobjective dynamic programming to regional natural resource management

The general multiobjective dynamic programming problem is reformulated as a classical dynamic programming problem that then can be solved by regular dynamic programming methods. It is shown that the method of differential dynamic programming is most applicable for solution of this problem, which has a higher dimension state space. A case study, the management of a large natural resource system, is presented and modeled next. Finally, the model is applied to the case of bauxite mining development in Hungary, and numerical results for this case are presented.     

On computational methods for solutions of multiobjective linear production programming games

In this paper we consider a production model in which multiple decision makers pool resources to produce finished goods. Such a production model, which is assumed to be linear, can be formulated as a multiobjective linear programming problem. It is shown that a multi-commodity game arises from the multiobjective linear production programming problem with multiple decision makers and such a game is referred to as a multiobjective linear production programming game. The characteristic sets in the game can be obtained by finding the set of all the Pareto extreme points of the multiobjective programming problem. It is proven that the core of the game is not empty, and points in the core are computed by using the duality theory of multiobjective linear programming problems. Moreover, the least core and the nucleolus of the game are examined. Finally, we consider a situation that decision makers first optimize their multiobjective linear production programming problem and then they examine allocation of profits and/or costs. Computational methods are developed and illustrative numerical examples are given.     

An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters

Real engineering design problems are generally characterized by the presence of many often conflicting and incommensurable objectives. Naturally, these objectives involve many parameters whose possible values may be assigned by the experts. The aim of this paper is to introduce a hybrid approach combining three optimization techniques, dynamic programming (DP), genetic algorithms and particle swarm optimization (PSO). Our approach integrates the merits of both DP and artificial optimization techniques and it has two characteristic features. Firstly, the proposed algorithm converts fuzzy multiobjective optimization problem to a sequence of a crisp nonlinear programming problems. Secondly, the proposed algorithm uses H-SOA for solving nonlinear programming problem. In which, any complex problem under certain structure can be solved and there is no need for the existence of some properties rather than traditional methods that need some features of the problem such as differentiability and continuity. Finally, with different degree of α we get different α-Pareto optimal solution of the problem. A numerical example is given to illustrate the results developed in this paper.     

Explicit solutions of Maxwell's equations on a space of constant curvature

An explicit solution is given to the Cauchy problem for the source-free Maxwell's equations in a vacuum on a space-time of the form $\text{R}$¹ X M₃, where M₃ is a 3-manifold of constant curvature. This solution satisfies Huyghens' Principle, that all electromagnetic radiation propagates at exactly the speed of light. The solution is obtained by harmonic analysis on M₃, and in the process a generating class of plane wave solutions is found. These solutions approximate the flat-space plane wave solutions in a neighbourhood of a point, but their global properties are somewhat different. The solutions obtained are easily transplanted to the Robertson-Walker models of General Relativity by re-scaling the time variable.     

Partial difference equations in m1⩾m2⩾⋯⩾mn⩾0 and their applications to combinatorics

Various discrete functions encountered in Combinatorics are solutions of Partial Difference Equations in the subset of Nⁿ given by m₁?m₂???m_n?0. Given a partial difference equation, it is described how to pass from the standard “easy” solution of an equation in Nⁿ to a solution of the same equation subject to certain “Dirichlet” or “Neumann” boundary conditions in the domain m₁?m₂???m_n?0 and related domains. Applications include a rather quick derivation of MacMahon's generating function for plane partitions, a generalization and q-analog of the Ballot problem, and a joint analog of the Ballot problem and Simon Newcomb's problem.     

Multiobjective programming under d-invexity

In this paper, a generalization of convexity is considered in the case of nonlinear multiobjective programming problem where the functions involved are nondifferentiable. By considering the concept of Pareto optimal solution and substituting d-invexity for convexity, the Fritz John type and Karush–Kuhn–Tucker type necessary optimality conditions and duality in the sense of Mond–Weir and Wolfe for nondifferentiable multiobjective programming are given.     

Solving a multiobjective possibilistic problem through compromise programming

Real decision problems usually consider several objectives that have parameters which are often given by the decision maker in an imprecise way. It is possible to handle these kinds of problems through multiple criteria models in terms of possibility theory.Here we propose a method for solving these kinds of models through a fuzzy compromise programming approach.To formulate a fuzzy compromise programming problem from a possibilistic multiobjective linear programming problem the fuzzy ideal solution concept is introduced. This concept is based on soft preference and indifference relationships and on canonical representation of fuzzy numbers by means of their α-cuts. The accuracy between the ideal solution and the objective values is evaluated handling the fuzzy parameters through their expected intervals and a definition of discrepancy between intervals is introduced in our analysis.     

一类不定性多人两层多目标协调决策模型的初探

考虑到组织决策中分权的普遍存在和高低管理层间依靠信息沟通所发生的控制和协调行为以及组织环境和内部条件的真实特征-不定性，本文将一类特殊的多人两层多目标协调决策模型置于组织不定性环境中予以研究，提出了不定性多人两层多目标协调决策模型．并通过模型的不断转化和K—T条件的应用，最终转化为确定的一般目标规划模型．同时，考虑到上层决策单元对下层决策行为的信息反馈进行处理时的及时性和交互性要求，一个具有快速反应能力的双层人机交互决策模式在问题求解中被设计出来以适应组织对适时目标管理的信息处理需要．     

Compromise allocation in multivariate stratified sample surveys under two stage randomized response model

A general model for the randomized response (RR) method was introduced by Warner (J. Am. Stat. Assoc. 60:63–69, 1965) when a single-sensitive question is under study. However, since social surveys are often based on questionnaires containing more than one sensitive question, the analysis of multiple RR data is of considerable interest. In multivariate stratified surveys with multiple RR data the choice of optimum sample sizes from various strata may be viewed as a multiobjective nonlinear programming problem. The allocation thus obtained may be called a “compromise allocation” in sampling literature. This paper deals with the two-stage stratified Warner’s RR model applied to multiple sensitive questions. The problems of obtaining compromise allocations are formulated as multi-objective integer non linear programming problems with linear and quadratic cost functions as two separate problems. The solution to the formulated problems are achieved through goal programming technique. Numerical examples are presented to illustrate the computational details.     

A modified objective function method for solving nonlinear multiobjective fractional programming problems

A new method is used for solving nonlinear multiobjective fractional programming problems having V-invex objective and constraint functions with respect to the same function η. In this approach, an equivalent vector programming problem is constructed by a modification of the objective fractional function in the original nonlinear multiobjective fractional problem. Furthermore, a modified Lagrange function is introduced for a constructed vector optimization problem. By the help of the modified Lagrange function, saddle point results are presented for the original nonlinear fractional programming problem with several ratios. Finally, a Mond-Weir type dual is associated, and weak, strong and converse duality results are established by using the introduced method with a modified function. To obtain these duality results between the original multiobjective fractional programming problem and its original Mond-Weir duals, a modified Mond-Weir vector dual problem with a modified objective function is constructed.     

A Solution Concept for Fuzzy Multiobjective Programming Problems Based on Convex Cones

A solution concept for fuzzy multiobjective programming problems based on ordering cones (convex cones) is proposed in this paper. The notions of ordering cones and partial orderings on a vector space are essentially equivalent. Therefore, the optimality notions in a real vector space can be elicited naturally by invoking a concept similar to that of the Pareto-optimal solution in vector optimization problems. We introduce a corresponding multiobjective programming problem and a weighting problem of the original fuzzy multiobjective programming problem using linear functionals so that the optimal solution of its corresponding weighting problem is also the Pareto-optimal solution of the original fuzzy multiobjective programming problem.     

Opportunity Losses Due to Uncertainty in Multiobjective Decision Problems

A multiobjective maximization problem is considered in which at least one objective function f_i(x, C) depends on a random parameter C. If a single-valued measure, such as weighting or an l_p distance, is used to determine the preferred solution among the nondominated solutions, then standard decision-theoretic methods can be used to determine the expected opportunity loss (EOL). By an example hydrologic problem, it is shown that EOL is highly dependent on the single-valued measure selected to solve the multiobjective problem. The expected multiobjective opportunity loss (EMOL) is developed as a vector-valued measure of the effect of uncertainty on the problem which is independent of the technique. Finding the decision point with minimum EOL or EMOL is a possible way of selecting the preferred point. Problems pertaining to a multiobjective formulation of the EOL concept are examined.     

Characterization of vector strict global minimizers of order 2 in differentiable vector optimization problems under a new approximation method

In this paper, a new approximation method is introduced to characterize a so-called vector strict global minimizer of order 2 for a class of nonlinear differentiable multiobjective programming problems with (F,ρ)-convex functions of order 2. In this method, an equivalent vector optimization problem is constructed by a modification of both the objectives and the constraint functions in the original multiobjective programming problem at the given feasible point. In order to prove the equivalence between the original multiobjective programming problem and its associated F-approximated vector optimization problem, the suitable (F,ρ)-convexity of order 2 assumption is imposed on the functions constituting the considered vector optimization problem.     

Another approach to multiobjective programming problems withF-convex functions

In this paper, optimality conditions for multiobjective programming problems havingF-convex objective and constraint functions are considered. An equivalent multiobjective programming problem is constructed by a modification of the objective function. Furthermore, anF—Lagrange function is introduced for a constructed multiobjective programming problem, and a new type of saddle point is introduced. Some results for the new type of a saddle point are given.