Financial planning using goal programming

Financial planners within industrial organizations are often given the impossible task of formulating a multi-year financial plan which is severely over-constrained. As the planner attempts to meet one objective another variable (or ratio of variables) becomes unacceptable and he is faced with the familiar ‘balloon squeezing’ effect. The problem is one of multiple conflicting objectives (goals) hence lends itself very well to a goal programming method of solution. This approach provides a powerful ‘what-if’ device for the financial planner and allows him to arrive at a satisfactory solution by examining the various trade-offs among the conflicting goals. To be an effective and usable tool, the individual goals are not assigned a priority coefficient as is typical of most goal programming applications. Rather, the planner can reflect his priorities in the manner in which he performs the subsequent what-if analyses. Also, a more flexible penalty function is introduced allowing the planner to assign a more realistic set of penalities which vary in severity over a specified range. Methods of implementing this concept are discussed which overcome the problems created by the immense storage requirements and the necessity of assigning the various penalties.     

Interactive fuzzy goal programming for multi-objective transportation problems

This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.     

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.     

A goal programming model for paper recycling system

The conflict between economic optimization and environmental protection has received wide attention in recent research programs for waste management system planning. This has also resulted in a set of new waste management goals in reverse logistics system planning. The purpose of this analysis is to formulate a mixed integer goal programming (MIGP) model to assist in proper management of the paper recycling logistics system. The model studies the inter-relationship between multiple objectives (with changing priorities) of a recycled paper distribution network. The objectives considered are reduction in reverse logistics cost; product quality improvement through increased segregation at the source; and environmental benefits through increased wastepaper recovery. The proposed model also assists in determining the facility location, route and flow of different varieties of recyclable wastepaper in the multi-item, multi-echelon and multi-facility decision making framework. The use of the model has been illustrated through a problem of paper recycling in India.     

Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.     

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.     

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.     

A production planning system based on linear programming

This paper describes the production planning system developed for the chemicals division of the Boliden Company Ltd., Sweden. The major elements of the production planning system are a linear programming model, a cost model, a scheduling program and a program to forecast the economic outcome. A special organization was established to implement the plans.     

A categorized bibliographic survey of goal programming

A total of 240 goal programming articles that have so far appeared in over 60 English journal publications are compiled and classified according to technique and application areas. Analysis of these data provides interesting insights regarding goal programming article characteristics, literature trends and future needs.     

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.     

An approach to stochastic programming for medium term planning

A number of methods of obtaining the distribution of the optimum of the ‘wait and see’ stochastic programming model have been proposed, but computational experience for these is currently limited to the solution of small problems. The purpose of this paper is to discuss the role of the ‘wait and see’ model in planning, and to propose a method of analysis based on the minimax and maximax decision criteria. The approach requires the solution of a special class of non-linear programming problems. Computational results to date suggest that it will be possible to analyse practically sized problems in this way.     

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.     

How managers decide to use planning models

Recent published reports and surveys have shown that a growing number of corporations and government agencies are using decision models not only for lower level scheduling and resource allocation but also for short-range and long-range planning. Although the literature has described in some detail the types of models used and the ways in which they are used, limited attention has been paid to the ways in which managers decide whether or not to use these models. This paper, which is based on a series of case studies, suggests that the decision to use planning models is made not by performing a comprehensive cost benefit analysis, but by the use of a reference model—that is, an existing model, sometimes in a competitive organization, similar to the one being considered.     

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.