A goal programming approach to production planning for flexible manufacturing systems

The increased use of flexible manufacturing systems (FMS) reduces production time variability as the fixed processing time of automated systems replaces the variable processing time of human resources in production systems. This reduction in the variability permits the application of deterministic, constrained optimization models that recognize the conflicting multi-objective nature of resource utilization in an FMS environment. One such multi-objective optimization model is linear goal programming. Unlike most linear programming models, linear goal programming is designed to handle conflicting multi-objective problems. A goal programming model is developed a and presented in this paper as an aid in FMS planning. An application is also presented to illustrate the informational benefits of using the proposed model.     

OPTIMIZING ORGANIZATIONAL GOALS IN ASSIGNING FACULTY TEACHING SCHEDULES

The administration of higher education, as with other administrative environments, has become increasingly complex. This paper presents a mathematical model which addresses one of the administrative tasks concerning administrators in all institutions, the establishment of faculty teaching schedules. The assignment technique utilized by the model is linear programming with two special characteristics: goal programming and mixed-integer programming. The goal programming characteristic refers to the provision of explicit stack variables to take on values representing deviations from assignment criteria that may result in resolving conflicts which arise from interactive administrative priorities. The mixed-integer programming characteristic refers to a requirement of the model that certain variables take on only integer values if they appear in the final solution. The model is demonstrated using two different sets of preference orderings for goal achievement.     

ON FUZZY GOAL PROGRAMMING*

This paper illustrates how the goal programming problem with fuzzy goals having linear membership functions may be formulated as a single goal programming problem. Also, a previously defined method for dealing with fuzzy weights for each of the goals is re-examined.     

ON FUZZY GOAL PROGRAMMING—SOME COMMENTS

This paper pertains to goal programming with fuzzy goals and fuzzy priorities. Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper [2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals.     

QUADRATIC PREFERENCES AND GOAL PROGRAMMING

One of the shortcomings of goal programming lies in its linearity, assumption, specifically in the objective function. This assumption compels one to work with constant marginal utilities and rates of substitution. In this paper a quadratic preference function, which is more consistent with economic theory and reality, is formulated and introduced into goal programming. In an effort to facilitate the understanding of the proposed procedure, two illustrative examples—one with symmetric preferences and the other with asymmetric preferences, both applied to the objective function—are solved and compared with a goal programming solution.     

Governmental planning and budgeting with multiple objective models

This paper presents a goal programming model development and application for a state level public health care agency. Only limited applications of macro planning processes related to health care have been presented in the literature. In that regard, this paper describes briefly the goal programming model, the background of the agency involved and the specific models developed. Three formulations of the model are presented involving (1) the resources required to achieve all goals, (2) the goal attainment status utilizing a likely budget and (3) a proposal for resolving the goal desire-resource limitation dilemma.     

连锁经营物流配送系统的动态分析模型研究

本文提出了研究连锁经营零售商业的物流配送总体计划的大型线性目标规划数学模型,并用动态目标规划的方法来模拟和动态地分析配送系统运行性能和合理性。详细论述了模型结构,并以大型问题的实例进行了测试,证实了方法的正确性和可用性。     

我国外债规模与结构最优控制的非线性目标规划模型及应用

本文讨论了外债规模与结构最优控制的目标及对约束条件的确定,进而建立了优化外债规模与结构的非线性目标规划模型,并以中国的实际情况为实例进行了求解和分析．得到了我国外债最优的规模与结构及一些有意义的建议。     

COMPLIANCE WITH THE OCCUPATIONAL SAFETY AND HEALTH ACT: A MATHEMATICAL PROGRAMMING FRAMEWORK

As a result of the passage of the Occupational Safety and Health Act of 1970, many employers are confronted with a multiple decision situation. The employer would like to comply with federally imposed standards as quickly as possible, but the employer must consider the budgetary limits under which he can profitably operate while achieving compliance. A goal programming model can be formulated to solve the problem. In this paper a review of the OSHA requirements is made, and a case example is presented to demonstrate the applicability of goal programming to resolving the budgetary pressure of OSHA compliance.     

Zero-base budgeting—Dealing with conflicting objectives

While Zero-Base Budgeting (ZBB) has received considerable attention in the public sector, virtually all the studies dealing with the system have neglected or given cursory attention to the problem of multiple conflicting objectives. This paper presents a goal programming approach as a systematic means to develop the ZBB process for multiple objectives. This approach allows the administrators to more realistically portray the decision environment as well as their judgment in their budgetary planning models, thus making the budgeting an effective and pragmatic way to implement the planning and decision making process. This study demonstrates a goal programming based ZBB system in the public sector based on real-world data.     

A NOTE ON DECISION-MAKING CRITERIA FOR ALGORITHM SELECTION: REDUCING GOAL PROGRAMMING COMPUTATIONAL EFFORT

Most linear goal programming (LPG) algorithms are based on a simplex-type solution method that begins with an initial simplex tableau with solution-set variables (basic variables) consisting of all-negative deviational variables or all-positive deviational variables. This note (1) demonstrates how computational solution effort can be reduced by selecting the appropriate all-negative or all-positive deviational variable algorithm and (2) describes a procedure that can be used to aid decision makers in selecting the appropriate algorithm for all types of applied goal programming (GP) models. Results of this study reveal the procedure as accurate and providing computational time savings when applied to a large sample of GP problems.     

An accounting control system structured on multiple objective planning models

An important problem confronting decision makers in modern organizations is how to plan and control in a multiple goal decision setting. The usual approach for attacking this problem is to assume one dominant goal and treat others as constraints for the budget planning purpose. The traditional accounting control system is a variance analysis which makes a comparison between an ex ante planning budget, a budget adjusted to the actual activity level, and actual results. The present paper describes how to set up multiple goal planning models by using goal programming and multiple objective linear programming techniques. And an opportunity cost concept of ex post accounting variance analysis (which a comparison is made between an ex ante budget, ex post optimum budget, and actual results) is used as a control device. This ex post analysis will signal a deviation in any data input parameter in the planning models.     

A survey of goal programming

In this paper the applications of goal programming to industrial and economic planning are reviewed, and it is shown how the linear programming framework can be extended to include a series of goals (or objectives). A planning method is suggested in which the decision maker is presented with a list of all the possible attainable goals from which he picks the most appropriate set.     

MANAGEMENT OF COLLEGE STUDENT RECRUITING ACTIVITIES USING GOAL PROGRAMMING*

A shrinking pool of potential students, due to a declining birthrate as well as uncertain economic times, is creating the need for more effective recruiting of college students. One approach using a goal programming model has been developed and is currently being used to manage recruitment activities in a small four-year college in Nebraska. The model identifies both the type and number of activities that must be completed each quarter in order to reach an enrollment goal for a given year. Factors such as budget, time, manpower, and marketing strategies are highlighted in the model. The results of the goal programming model encouraged more field activities in the first two quarters with emphasis on new-candidate identification. The third-quarter recruiting strategy is more balanced while the fourth-quarter emphasis is placed on follow-up activities that occur chiefly in the office. Use of this model will enable recruiters to meet enrollments while managing recruiting resources and activities in order to remain within the recruiting budget.     

The falling tide algorithm: A new multi-objective approach for complex workforce scheduling

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.     

我国按地区和行业利用外资规模控制的非线性目标规划模型及应用

本文讨论了利用外资按地区和行业规模控制的目标及约束条件的确定,进而建立了按地区和行业利用外资的最优控制的非线性目标规划模型,并以中国为实际案例进行了求解和分析,得到了我国按地区和行业利用外资的最优规模及一些有意义的对策建议。     

INTEGER GOAL PROGRAMMING IN ADVERTISING MEDIA SELECTION

The media selection decision allocates advertising dollars among competing media so as to optimize promotional and corporate objectives. Linear programming attempts to model this process have been complicated by multiple and often conflicting management goals, the need for integer solutions, and nonlinearities. This study offers a technique that is sufficiently robust to simultaneously handle these problems. An alternative media selection framework is presented and the results of an illustrative application of integer goal programming are discussed. The proposed model improves on linear programming by success fully providing for optimal, integer solutions in settings that more realistically reflect the complexity of the media decision environment.     

Goal programming and constrained regression—A comment

Goal programming was originated to obtain “constrained regression” estimates for an executive compensation formula which would conform to an organization hierarchy and other company policies prescribed by management. A subsequent history in that context is traced through other uses in personnel planning and in organization designs. Applications to planning advertising strategies are also examined which extend to uses of goal programming that involve fitting frequency functions under a variety of constraints. Finally this is all related to the initial and continuing research on the statistical properties of these estimates.     

基于多目标规划的中国可持续发展模式优化研究

本文以人口(Population)、资源(Resource)、经济(Economy)、环境(Environment)和科技(Science & Technolo gy)为核心,构造了PREEST系统模型。以PREEST系统为对象,运用主成分分析法对1987-2001年度中国的可持续发展水平进行了综合评价;借助多目标规划理论,构建了中国可持续发展多目标集成模型,并以"十五"计划纲要为基准,开展了中国可持续发展规划研究的实证分析。在此基础上,通过对"十五"期间中国可持续发展传统模式和规划模式的对比分析,就中国人口、资源、经济、环境和科技等领域的协调发展提出了政策建议。     

PORTFOLIO MODELING IN MULTIPLE-CRITERIA SITUATIONS UNDER UNCERTAINTY: COMMENT

In a recent issue of Decision Sciences, Muhlemann, Lockett, and Gear [8] developed a multiple-objective, stochastic linear programming formulation of the multiperiod portfolio selection problem under uncertainty. The purpose of this note is to offer some extensions to their multicriteria approach which is otherwise viewed as an excellent attempt at modeling realistic aspects of the portfolio selection problem. Further, integer goal programming combined with simulation is suggested as an alternate approach for solving the dynamic multiple-objective problem.