Monotone Approximations of Minimum and Maximum Functions and Multi-objective Problems

In this paper the problem of accomplishing multiple objectives by a number of agents represented as dynamic systems is considered. Each agent is assumed to have a goal which is to accomplish one or more objectives where each objective is mathematically formulated using an appropriate objective function. Sufficient conditions for accomplishing objectives are derived using particular convergent approximations of minimum and maximum functions depending on the formulation of the goals and objectives. These approximations are differentiable functions and they monotonically converge to the corresponding minimum or maximum function. Finally, an illustrative pursuit-evasion game example with two evaders and two pursuers is provided.     

Redundant objective functions in linear vector maximum problems and their determination

Suppose that in a multicriteria linear programming problem among the given objective functions there are some which can be deleted without influencing the set E of all efficient solutions. Such objectives are said to be redundant. Introducing systems of objective functions which realize their individual optimum in a single vertex of the polyhedron generated by the restriction set, the notion of relative or absolute redundant objectives is defined. A theory which describes properties of absolute and relative redundant objectives is developed. A method for determining all the relative and absolute redundant objectives, based on this theory, is given. Illustrative examples demonstrate the procedure.     

An extended covering model for flexible discrete and equity location problems

To model flexible objectives for discrete location problems, ordered median functions can be applied. These functions multiply a weight to the cost of fulfilling the demand of a customer which depends on the position of that cost relative to the costs of fulfilling the demand of the other customers. In this paper a reformulated and more compact version of a covering model for the discrete ordered median problem (DOMP) is considered. It is shown that by using this reformulation better solution times can be obtained. This is especially true for some objectives that are often employed in location theory. In addition, the covering model is extended so that ordered median functions with negative weights are feasible as well. This type of modeling weights has not been treated in the literature on the DOMP before. We show that several discrete location problems with equity objectives are particular cases of this model. As a result, a mixed-integer linear model for this type of problems is obtained for the first time.     

Using box indices in supporting comparison in multiobjective optimization

Because of the conflicting nature of criteria or objectives, solving a multiobjective optimization problem typically requires interaction with a decision maker who can specify preference information related to the objectives in the problem in question. Due to the difficulties of dealing with multiple objectives, the way information is presented plays a very important role. Questions posed to the decision maker must be simple enough and information shown must be easy to understand. For this purpose, visualization and graphical representations can be useful and constitute one of the main tools used in the literature. In this paper, we propose to use box indices to represent information related to different solution alternatives of multiobjective optimization problems involving at least three objectives. Box indices are an intelligible and easy to handle way to represent data. They are based on evaluating the solutions in a natural and rough enough scale in order to let the decision maker easily recognize the main characteristics of a solution at a glance and to facilitate comparison of two or more solutions in an easily understandable way.     

An Exact Method for Fractional Goal Programming

Goal Programming with fractional objectives can be reduced to mathematical programming with a linear objective under linear and quadratic constraints, thus optimal solutions can be obtained by using existing Global Optimization techniques. However, only heuristic procedures are suggested in the literature on the field. In this note we explore the practical applicability of a recent algorithm for nonconvex quadratic programming with quadratic constraints for this problem. Encouraging computational experiences for randomly generated instances with up to 14 fractional objectives are presented.     

Tabu Search Based Procedure for Solving the 0-1 MultiObjective Knapsack Problem: The Two Objectives Case

We consider in this paper the solving of 0-1 knapsack problems with multiple linear objectives. We present a tabu search approach to generate a good approximation of the efficient set. The heuristic scheme is included in a redu tion decision space framework. The case of two objectives is developed in this paper. TS principles viewed into the multiobjective context are discussed. According to a prospective way, several variations of the algorithm are investigate. Numerical experiments are reported and compared with available exact efficient solutions. Intuitive justifications for the observed empirical behavior of the procedure and open questions are discussed.     

Nonessential objectives within network approaches for MCDM

In Gal and Hanne [Eur. J. Oper. Res. 119 (1999) 373] the problem of using several methods to solve a multiple criteria decision making (MCDM) problem with linear objective functions after dropping nonessential objectives is analyzed. It turned out that the solution does not need be the same when using various methods for solving the system containing the nonessential objectives or not. In this paper we consider the application of network approaches for multicriteria decision making such as neural networks and an approach for combining MCDM methods (called MCDM networks). We discuss questions of comparing the results obtained with several methods as applied to the problem with or without nonessential objectives. Especially, we argue for considering redundancies such as nonessential objectives as a native feature in complex information processing. In contrast to previous results on nonessential objectives, the current paper focuses on discrete MCDM problems which are also denoted as multiple attribute decision making (MADM).     

Logic optimality for multi-objective optimization

Pareto dominance is one of the most basic concepts in multi-objective optimization. However, it is inefficient when the number of objectives is large because in this case it leads to an unmanageable number of Pareto solutions. In order to solve this problem, a new concept of logic dominance is defined by considering the number of improved objectives and the quantity of improvement simultaneously, where probabilistic logic is applied to measure the quantity of improvement. Based on logic dominance, the corresponding logic nondominated solution is defined as a feasible solution which is not dominated by other ones based on this new relationship, and it is proved that each logic nondominated solution is also a Pareto solution. Essentially, logic dominance is an extension of Pareto dominance. Since there are already several extensions for Pareto dominance, some comparisons are given in terms of numerical examples, which indicates that logic dominance is more efficient. As an application of logic dominance, a house choice problem with five objectives is considered.     

Resource allocation in the North Sea demersal fisheries: A goal programming approach

The management of a fishery is a complex task generally involving multiple, often conflicting, objectives. These objectives typically include economic, biological and social goals such as improving the income of fishers, reducing the catch of depleted species and maintaining employment.Multicriteria decision making (MCDM) techniques appear wellsuited to such a management problem, allowing compromises between conflicting objectives to be analysed in a structured framework. In comparison to other fields, such as water resource planning, forestry and agriculture, there have been few applications of MCDM to fisheries.In this paper, a goal programming model of the North Sea demersal fishery is presented. The model is used to demonstrate the potential applicability of this type of approach to the analysis and development of fisheries management plans with multiple objectives. Alternative scenarios are considered for the problem, and tradeoffs between given objectives are also highlighted and discussed.     

Enhanced interactive satisficing method via alternative tolerance for fuzzy goal programming with progressive preference

This paper proposes an enhanced interactive satisficing method via alternative tolerance for fuzzy goal programming with progressive preference. The alternative tolerances of the fuzzy objectives with three types of fuzzy relations are used to model progressive preference of decision maker. In order to improve the dissatisficing objectives, the relaxed satisficing objectives are sacrificed by modifying their tolerant limits. By means of attainable reference point, the auxiliary programming is designed to generate the tolerances of the dissatisficing objectives for ensuring feasibility. Correspondingly, the membership functions are updated or the objective constraints are added. The Max–Min goal programming model (or the revised one) and the test model of the M-Pareto optimality are solved lexicographically. By our method, the dissatisficing objectives are improved iteratively till the preferred result is acquired. Illustrative examples show its power.     

Analysing Policy Effectiveness Using Cone Restricted Data Envelopment Analysis

Reducing weight flexibility has been suggested as a method for ensuring that the solution to data envelopment analyses do not give unreasonably low weightings to certain inputs or outputs. In this paper we extend the use of reducing weight flexibility and use it to model the effects of the decision-making unit's objectives on its efficiency relative to other DMUs with possibly different objectives. We show how such an approach can identify situations in which the weights imputed by a data envelopment analysis can be inconsistent with the decision-making weights used by the firm, and how this approach can be used to provide efficiency measures that are consistent with the DMU's own objectives. The method allows the analyst to distinguish between a decision-making unit's technological inefficiency and its inability to implement its own policies.     

工业互联网环境下预防性维修管理多目标协同优化

工期、成本、质量通常作为传统项目管理过程中三大目标,而工业互联网环境下预防性维修管理除了以上三大目标之外还更多的考虑了安全因素.根据设备机群内不同设备分析了其四大目标不同构成和内涵,四大目标决策权重采用经改进的带置信度的专家确定方法,根据工期约束等因素导出资源使用均衡条件.在此基础上构建了非线性条件下四方关系多属性效用函数模型.采用线性递减收敛因子改进下的多点交叉遗传算法进行优化求解,促使种群快速寻找最优解,算法表现出较好的收敛性和多样性使得寻优过程更加合理.     

Equity Models in Planar Location

In this paper we investigate planar location models with equity objectives. Two objectives are analyzed: (1) Minimizing the variance of the distances to the facility, and (2) minimizing the range of the distances. The problems are solved using the global optimization technique “Big Triangle Small Triangle”. Computational experiments provided excellent results. Solving a problem with 10,000 demand points required less than 5 s of computer time for finding the minimum variance, and less than half that time for finding the minimum range.     

Efficient frontier cutoff policies in credit portfolios

Historically, account acquisition in scored retail credit and loan portfolios has focused on risk management in the sense of minimizing default losses. We believe that acquisition policies should focus on a broader set of business measures that explicitly recognize tradeoffs between conflicting objectives of losses, volume and profit. Typical business challenges are: ‘How do I maximize portfolio profit while keeping acceptance rate (volume, size) at acceptable levels?’ ‘How do I maximize profit without incurring default losses above a given level?’ ‘How do I minimize the risk of large loss exposures for a given market share?’ In this paper we are not concerned with which combination of objectives are appropriate, but rather focus on the cutoff policies that allow us to capture a number of different portfolio objectives. When there are conflicting objectives we show that optimal policies yield meaningful tradeoffs and efficient frontiers and that optimal shadow prices allow us to develop risk-adjusted tradeoffs between profit and market share. Some of the graphical solutions that we obtain are simple to derive and easy to understand without explicit mathematical formulations but even simple constraints may require formal use of non-linear programming techniques. We concentrate on models and insights that yield decision strategies and cutoff policies rather than the techniques for developing good predictors.     

A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming approach

In this paper, we deal with a transportation problem in a housing material manufacturer and derive a satisfactory solution to the problem by taking into account not only the degree of satisfaction with respect to objectives of the housing material manufacturer but also those of two forwarding agents to which the housing material manufacturer entrusts transportation of products. The two forwarding agents handle the regular transportation and the small lot transportation, and each of them assigns work force, the transformation tasks properly. There are two objectives in the housing material manufacturer: one is to minimize the transportation cost and the other is to minimize the opportunity loss with respect to transportation time. The objectives of the two forwarding agents are to maximize their profits. We formulate the transportation planning and work force assignment problem as a decentralized two-level integer programming problem, and derive a satisfactory solution by applying an interactive fuzzy programming method.     

The role of weights and scales in the application of multiobjective decision making

The application of multiobjective solution techniques requires, at some point in the analysis, the specification of the preference structure of the decision maker over the set of objectives. In practice, this step must often take the form of determining a set of weights that attempts to quantify the relative importance of the various competing objectives. This paper looks at how some of the well known techniques make use of these weights in attempting to achieve the best compromise solution. The definition of the weights (i.e., what meaning they carry into the analysis) is also explored. It is found that one must be very careful about applying weights when using these techniques. A different set of weights for the same objectives may apply when implementing two different techniques. In addition, a related factor, the specification of scales or ranges over which the objectives are to be evaluated, is explored. Again, it is found that different techniques use these scales in ways that change the manner in which they should be defined. The techniques reviewed are compromise programming, multiattribute utility theory, ELECTRE and cooperative game theory. Concepts are illustrated through the use of a water resources example problem.     

含定性因子的二阶Scheff′e模型多目标最优设计北大核心

在实际的很多情形中,混料试验都具有多个目标,且响应变量不仅受到各分量的影响,还会受到其他定性因子变量的影响.文中基于一类含定性因子的混料模型,通过求解效率和极值得到多目标最优设计,并证明了该设计满足相应组合最优性.实例证明,该方法同样适用于3个或更多目标优化设计问题.     

A multiobjective framework for heavily constrained examination timetabling problems

University examination timetabling is a challenging set partitioning problem that comes in many variations, and real world applications usually carry multiple constraints and require the simultaneous optimization of several (often conflicting) objectives. This paper presents a multiobjective framework capable of solving heavily constrained timetabling problems. In this prototype study, we focus on the two objectives: minimizing timetable length while simultaneously optimizing the spread of examinations for individual students. Candidate solutions are presented to a multiobjective memetic algorithm as orderings of examinations, and a greedy algorithm is used to construct violation free timetables from permutation sequences of exams. The role of the multiobjective algorithm is to iteratively improve a population of orderings, with respect to the given objectives, using various mutation and reordering heuristics.     

A survey on multi-objective evolutionary algorithms for many-objective problems

Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex multi-objective problems with two or three objectives. However, as the number of conflicting objectives increases, the performance of most MOEAs is severely deteriorated. How to improve MOEAs’ performance when solving many-objective problems, i.e. problems with four or more conflicting objectives, is an important issue since a large number of this type of problems exists in science and engineering; thus, several researchers have proposed different alternatives. This paper presents a review of the use of MOEAs in many-objective problems describing the evolution of the field, the methods that were developed, as well as the main findings and open questions that need to be answered in order to continue shaping the field.     

A characterization of weakly efficient points

In this paper, we study a characterization of weakly efficient solutions of Multiobjective Optimization Problems (MOPs). We find that, under some quasiconvex conditions of the objective functions in a convex set of constraints, weakly efficient solutions of an MOP can be characterized as an optimal solution to a scalar constraint problem, in which one of the objectives is optimized and the remaining objectives are set up as constraints. This characterization is much less restrictive than those found in the literature up to now.Corresponding author.