Hybrid algorithm for sequencing with bicriteria

This paper presents a solution algorithm for a single machine scheduling problem with two criteria: total tardiness and total flow time. Theoretical results of precedence properties which are respected by all nondominated schedules are first derived. These precedence properties are then incorporated into a multiple-criteria dynamic programming framework to improve the computational efficiency. Results of the computational experiment and the average behavior (computation time and efficiency) of the algorithm are reported.This investigation was supported in part by University of Kansas General Research Allocation No. 3470-20-0038 and the University of Kansas School of Business Research Fund provided by the Fourth National Bank and Trust Company, Wichita. The ideas and opinions expressed herein are solely those of the author. The author especially wants to thank Professor P. L. Yu for valuable comments on an earlier draft of this paper.     

ABC库存分类的多指标体系及其信息熵方法

库存合理分类对企业的库存管理非常重要.在现有ABC库存分类指标体系的基础之上,指出需求波动对库存分类的重要性,设计了考虑需求波动系数的.ABC库存分类多指标体系;进一步,分析了指标的差异性对库存分类的影响,提出了基于信息熵的多指标ABC库存分类方法.与已有文献对比分析表明,所设计的多指标分类体系综合考虑了库存物资的不同属性;对某家电企业的案例分析可以看出,所提出的.ABC库存分类的多指标体系及其信息熵方法有助于企业制定合理的库存决策,可有效提高企业的管理绩效.     

On the structure and properties of a linear multilevel programming problem

Many decision-making situations involve multiple planners with different, and sometimes conflicting, objective functions. One type of model that has been suggested to represent such situations is the linear multilevel programming problem. However, it appears that theoretical and algorithmic results for linear multilevel programming have been limited, to date, to the bounded case or the case of when only two levels exist. In this paper, we investigate the structure and properties of a linear multilevel programming problem that may be unbounded. We study the geometry of the problem and its feasible region. We also give necessary and sufficient conditions for the problem to be unbounded, and we show how the problem is related to a certain parametric concave minimization problem. The algorithmic implications of the results are also discussed.This research was supported by National Science Foundation Grant No. ECS-85-15231.     

Optimization of the Norm of a Vector-Valued DC Function and Applications

In this paper, we show that a DC representation can be obtained explicitly for the composition of a gauge with a DC mapping, so that the optimization of certain functions involving terms of this kind can be made by using standard DC optimization techniques. Applications to facility location theory and multiple-criteria decision making are presented.     

Deriving weights in multiple-criteria decision making with support vector machines

A key problem in Multiple-Criteria Decision Making is how to measure the importance of the different criteria when just a partial preference relation among actions is given. In this note we address the problem of constructing a linear score function (and thus how to associate weights of importance to the criteria) when a binary relation comparing actions and partial information (relative importance) on the criteria are given. It is shown that these tasks can be done viaSupport Vector Machines, an increasingly popular Data Mining technique, which reduces the search of the weights to the resolution of (a series of) nonlinear convex optimization problems with linear constraints. An interactive method is then presented and illustrated by solving a multiple-objective 0–1 knapsack problem. Extensions to the case in which data are imprecise (given by intervals) or intransitivities in strict preferences exist are outlined.     

Book Notices

Finding Pareto-minimum vectors among r given vectors, each of dimension m, is a fundamental problem in multiobjective optimization problems or multiple-criteria decision-making problems. Corley and Moon (Ref. 1) have given an algorithm for finding all the Pareto-minimum paths of a multiobjective network optimization problem from the initial node to any other node. It uses another algorithm by Corley and Moon, which actually computes the Pareto-minimum vectors. We observed that the latter algorithm is incorrect. In this note, we correct the algorithm for computing Pareto-minimum vectors and present a modified algorithm.     

Geometric consideration of duality in vector optimization

Recently, duality in vector optimization has been attracting the interest of many researchers. In order to derive duality in vector optimization, it seems natural to introduce some vector-valued Lagrangian functions with matrix (or linear operator, in some cases) multipliers. This paper gives an insight into the geometry of vector-valued Lagrangian functions and duality in vector optimization. It is observed that supporting cones for convex sets play a key role, as well as supporting hyperplanes, traditionally used in single-objective optimization.The author would like to express his sincere gratitude to Prof. T. Tanino of Tohoku University and to some anonymous referees for their valuable comments.     

Branch-and-Bound Variant of an Outcome-Based Algorithm for Optimizing over the Efficient Set of a Bicriteria Linear Programming Problem

The paper presents a finite branch-and-bound variant of an outcome-based algorithm proposed by Benson and Lee for minimizing a lower-semicontinuous function over the efficient set of a bicriteria linear programming problem. Similarly to the Benson-Lee algorithm, we work primarily in the outcome space. Dissimilarly, instead of constructing a sequence of consecutive efficient edges in the outcome space, we use the idea of generating a refining sequence of partitions covering the at most two-dimensional efficient set in the outcome space. Computational experience is also presented.