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.     

A new model for aggregate output planning

Aggregate production planning has been aided in the recent past by the advent of mathematical programming models and the improvement in computer solution techniques. Such models have only considered single objectives; this paper formulates a multi-objective production planning model as a goal program and discusses the problems involved in its implementation.     

基于遗传算法的虚拟企业协同资源优化问题研究

面向复杂零件的协同制造,以工艺流程为核心将协同制造任务进行分解,并有效利用"逻辑制造单元"和"逻辑加工路线"等概念描述复杂零件的协同制造任务,最终目标是形成基于复杂零件工艺流程的、可支撑异地协同生产的加工路线。对复杂零件协同制造的制造资源优化配置问题进行了数学分析和描述,建立了问题的目标函数与约束条件。本文以加工时间、运输费用和加工质量作为目标,约束条件包括顺序约束、释放期约束、时间约束、交货期约束、成本约束和质量约束,最终将资源优化配置问题归结为多目标优化问题,并利用遗传算法进行求解,得到了较为满意的结果。通过实例分析,将模型应用于某型号发动机叶片协同制造,说明采用本论文的模型可以有效解决复杂零件协同制造的资源优化配置问题。     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

Optimizing lot size and setup time in a JIT environment: a multiple objective application

The just-in-time (JIT) system has been studied extensively and implemented by a number of US firms as an ell'ective production system. The core of JIT involves determination of lot size and setup time reduction so as to increase manufacturing flexibility while minimizing the inventory level. This decision problem usually involves multiple conflicting objectives and mixed-model production. In this paper, goal programming (GP) is applied to a real-world JIT problem involving fabrication of different automotive and industrial rubber composite belts. The model results provide new insights concerning the conflicting nature of several goals, especially between meeting demand and reducing setup or idle time. Also, the GP solution is superior to the current JIT practice of the company.     

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.     

DECISION ANALYSIS THROUGH GOAL PROGRAMMING

In recent years, decision making based on systematic analysis has been greatly emphasized. Yet, decision analysis is often carried out without analyzing the limitations of certain quantitative techniques. This paper presents the concept, solution method, and application potential of goal programming which eliminates many limitations of conventional linear optimization models.     

A chance-constraints linear programming model on the economic evaluation of flexible manufacturing systems

The implementation of flexible manufacturing systems (FMSs) normally entails a large initial investment under a long-term, uncertain environment. Many effects of installing a FMS will be due to improvement in throughput efficiency, quality, flexibility and the opportunity costs. However, most economic evaluations of FMSs assume the problem is deterministic, such that they fail to model accurately and capture the nature of FMSs. This paper uses stochastic variables to capture the.nature of a FMS under given resource limitations and leads to a multistage chance-constraints linear programming (LP) formulation. Finally, in order to incorporate the uncertainty of capital investment, the interest rate as a function of time is considered over the whole planning horizon and the decision model is extended under continuous and variable discounting.     

基于模糊规划法的企业价值链优化应用研究

本文通过阐述企业价值链的结构和优化目标,分析了模糊规划方法对于价值链优化研究的必要性,建立了企业价值链优化的模糊线性规划模型,并以轿车企业价值链为例给出了数值算例和结果分析,从企业在价值链优化方法的应用研究方面证明了方法的实用性。     

多目标物流网络优化模型的研究

针对物流网络规划中需要考虑多个目标的问题,以配流中心存储容量及使用率为约束,建立了基于总费用及最大单程距离(费用)最小的双目标数学模型,给出了优化模型的求解方法,为决策者提供多种可供选择的优化方案。     

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.     

炼钢连铸生产调度问题的两阶段遗传算法

将炼钢连铸生产过程抽象为混合流水车间,建立了0-1型混合整数线性规划调度模型。模型将严格连续浇注作为等式约束,并通过分段惩罚来平衡炉次的驻留时间。在对模型进行Benders分解的基础上,提出了将GA与LP结合的两阶段遗传算法。在算法设计中,提出了一种新的染色体编码来表示炉次设备指派与排序方案,给出了相应的遗传操作方法。算法的第一阶段通过最小化设备析取冲突来寻找高质量的种群,第二阶段通过求解线性规划模型来指导遗传算法的迭代过程。基于生产实际数据的仿真实验表明,该算法能够有效求解炼钢连铸生产调度问题。     

PRODUCTION-DISTRIBUTION PLANNING IN A LARGE SCALE COMMODITY PROCESSING NETWORK

This paper describes a methodology for production-distribution planning in a large scale commodity processing network. Based on earlier research efforts dealing with single-commodity and multi-commodity distribution system modeling and on production planning for a single-plant commodity processing facility, a mathematical programming methodology is developed for a multiplant soybean processing network. Application of the model leads to the specification of a production plan for a multi-period time horizon, while at the same time indicating the quantities of soybean meal and soybean oil to be supplied to various customers in various locales. Both sets of decisions are made under the general criterion of maximizing the net income produced by the soybean processing complex, subject to various production, inventory, capacity, supply and demand constraints. Test results from application of the model are presented and discussed.     

A zero-one goal programming approach for information system project selection

This paper presents a multi-objective model that can be used to help information systems (IS) managers decide which IS projects should be selected for development and implementation in a constrained resource environment. Developed to overcome the problems observed in currently used IS selection procedures, the model presented in this paper can be used to make decisions when IS selection goals are conflicting and measured in incommensurable units. To illustrate the use of this multi-objective approach to model IS project selection, a corporate resource allocation problem is developed and solved. Comparative results with currently used scoring and ranking methods reveals the superiority of the proposed multi-objective model.     

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。     

Production planning with linear programming: From textbook to factory

A case study explores the application of linear programming (LP) to the medium-term planning of a factory's production. The case appears to be the only non-oil company application of LP for medium-term production planning in the region. Details of the application are presented. Conclusions about implementation and utility of LP models for production planning are reached.     

An application of the integrated AHP-PGP model for performance measurement of supply chain management

The performance measurement problem has gained great attention in business and operations management literature. The first objective of this study is to determine the required performance measures and to develop a model for performance evaluation, based on these selected measures using analytical hierarchy process (AHP) methodology. The second objective is to explain how an integrated AHP-PGP (pre-emptive goal programming) model can be used in performance measurement while optimising the overall performance. An integration of AHP and multi-objective PGP is used to consider both quantitative and qualitative performance measures in optimising the overall performance of the system. The integrated model is presented with a real-world application using source data provided by a survey conducted in India. Findings demonstrate that the integrated AHP-PGP model can be useful to all supply chain industries in their day-to-day performance measurement decisions.     

OPTIMIZING UNIVERSITY ADMISSIONS PLANNING

The crucial issues in the administration of higher education embody the very purpose, function, and concept of each institution. The institution's administrative policies are based on the combined philosophy of many conflicting interest groups such as taxpayers, donors, administrators, faculty, students, staff, and users of university services. A function performed by the university which is receiving increasing attention and outside pressure is the formulation of admissions policy for newly entering students. This paper investigates problems involved in the admissions planning and presents a goal programming model as an optimization tool for the problem.     

Allocation of research and development funds: A zero-one goal programming approach

The research and development project selection process is one of the most difficult and important problems faced by management. It is typically complicated by indivisibility of projects and multiple and conflicting objectives, in addition to limitations on funding, facilities, and qualified researchers. In this paper a case example involving a high technology electrical equipment manufacturer is developed to illustrate this problem using zero—one goal programming to accommodate indivisibility of projects in addition to multiple and conflicting goals. The model presented is an attempt to provide managers with a robust tool for allocating scarce resources among research and development projects.     

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。