Showing papers on "Goal programming published in 1982"

Quantitative Analysis for Management

Abstract: CHAPTER 1 Introduction to Quantitative Analysis 1 CHAPTER 2 Probability Concepts and Applications 23 CHAPTER 3 Decision Analysis 69 CHAPTER 4 Regression Models 117 CHAPTER 5 Forecasting 157 CHAPTER 6 Inventory Control Models 199 CHAPTER 7 Linear Programming Models: Graphical and Computer Methods 255 CHAPTER 8 Linear Programming Modeling Applications:With Computer Analyses in Excel and QM for Windows 311 CHAPTER 9 Linear Programming: The Simplex Method 351 CHAPTER 10 Transportation and Assignment Models 409 CHAPTER 11 Integer Programming, Goal Programming, and Nonlinear Programming 469 CHAPTER 12 Network Models 515 CHAPTER 13 Project Management 543 CHAPTER 14 Waiting Lines and Queuing Theory Models 585 CHAPTER 15 Simulation Modeling 625 CHAPTER 16 Markov Analysis 669 CHAPTER 17 Statistical Quality Control 699 CD-ROM MODULES 1 Analytic Hierarchy Process M1-1 2 Dynamic Programming M2-1 3 Decision Theory and the Normal Distribution M3-1 4 Game Theory M4-1 5 Mathematical Tools: Determinants and Matrices M5-1 6 Calculus-Based Optimization M6-1

Introduction to Management Science

Abstract: 1. Management Science The Management Science Approach to Problem Solving Model Building: Break-Even Analysis Computer Solution Management Science Modeling Techniques Business Usage of Management Science Techniques Management Science Models in Descision Support Systems 2. Linear Programming: Model Formulation and Graphical Solution Model Formulation A Maximization Model Example Graphical Solutions of Linear Programming Methods A Minimization Model Example Irregular Types of Linear Programming Problems Characteristics of Linear Programming Problems 3. Linear Programming: Computer Solution and Sensitivity Analysis Computer Solution Sensitivity Analysis 4. Linear Programming: Modeling Examples A Product Mix Example A Diet Example An Investment Example A Marketing Example A Transportation Example A Blend Example A Multiperiod Scheduling Example A Data Envelopment Analysis Example 5. Integer Programming Integer Programming Models Integer Programming Graphical Solution Computer Solution of Integer Programming Problems with Excel and QM for Windows 0-1 Integer Programming Modeling Examples 6. Transportation, Transshipment, and Assignment Problems The Transportation Model Computer Solution of a Transportation Problem The Transshipment Model The Assignment Model Computer Solution of the Assignment Problem 7. Network Flow Models Network Components The Shortest Route Problem The Minimal Spanning Tree Problem The Maximal Flow Problem 8. Project Management The Elements of Project Management CPM/PERT Probabilistic Activity Times Microsoft Project Project Crashing and Time-Cost Trade-Off Formulating the CPM/PERT Network as a Linear Programming Model 9. Multicriteria Decision Making Goal Programming Graphical Interpretation of Goal Programming Computer Solution of Goal Programming Problems with QM for Windows and Excel The Analytical Hierarchy Process Scoring Model 10. Nonlinear Programming Nonlinear Profit Analysis Constrained Optimization Solution of Nonlinear Programming Problems with Excel A Nonlinear Programming Model with Multiple Constraints Nonlinear Model Examples 11. Probability and Statistics Types of Probability Fundamentals of Probability Statistical Independence and Dependence Expected Value The Normal Distribution 12. Decision Analysis Components of Decision Making Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility 13. Queuing Analysis Elements of Waiting Line Analysis The Single-Server Waiting Line System Undefined and Constant Service times Finite Queue Length Finite Calling Population The Multiple-Server Waiting Line Additional Types of Queuing Systems 14. Simulation The Monte Carlo Process Computer Simulation with Excel Spreadsheets Simulation of a Queuing System Continuous Probability Distributions Statistical Analysis of Simulation Results Crystal Ball Verification of the Simulation Model Areas of Simulation Application 15. Forecasting Forecasting Components Time Series Methods Forecast Accuracy Time Series Forecasting Using Excel Time Series Forecasting Using QM for Windows Regression Methods 16. Inventory Management Elements of Inventory Management Inventory Control Systems Economic Order Quantity Models The Basic EOQ Model The EOQ Model with Noninstantaneous Receipt The EOQ Model with Shortages EOQ Analysis with QM for Windows EOQ Analysis with Excel and Excel QM Quantity Discounts Reorder Point Determining Safety Stocks Using Service Levels Order Quantity for a Periodic Inventory System Appendix A Normal Table Chi-Square Table Appendix B Setting Up and Editing a Spreadsheet Appendix C The Poisson and Exponential Distributions Solutions to Selected Odd-Numbered Problems Glossary Index Photo Credits CD-ROM Modules

Linear Programming in Single and Multiple Objective Systems

Abstract: (1982). Linear Programming in Single and Multiple Objective Systems. Journal of the Operational Research Society: Vol. 33, No. 6, pp. 591-591.

Goal Programming via Multidimensional Scaling Applied to Senegalese Subsistence Farms

Abstract: A methodology is developed to estimate empirically the weights for a multiple-goal objective function of Senegalese subsistence farmers. The methodology includes a farmer-oriented goal preference survey and an application of a multidimensional scaling technique to the survey data. A comparison of model performance under the multiple-goal objective function with a profit-maximization objective function does not indicate that there are distinct advantages to using either function.

R&D Project Selection and Manpower Allocation with Integer Nonlinear Goal Programming

Abstract: A number of recent research efforts in the area of research and development planning have indicated the necessity that the R&D project selection process be viewed as a multi-criteria decision-making problem. As a result, linear 0-1 goal programming, because of its ability to encompass multiple objectives, has been employed on several occasions as a project selection model. However, in these goal programming models the relationships between resource utilization and project outcomes or between various resource utilizations have been expressed linearly when, in reality, they are often non-linear. For example, as the resources allocated to a project are increased the probability of project success will also increase but at a decreasing rate. In this paper, a non-linear integer goal programming model is described via a case example. The case example encompasses a pool of thirty researchers available for allocation to seven possible R&D projects. As such, the model consists of integer decision variables for both the number of researchers allocated, and, project selection. Researcher allocation and project selection are subject to several linear and nonlinear goal constraints. Nonlinear goal constraints are constructed that relate the probability of project success to the number of researchers assigned to a project and to expected monetary return, and, that relate the number of researchers allocated to project completion time. Linear goal constraints are developed for budget limitations, computer capacity utilization and various strict conditions placed on the model. The model selects projects and allocates researchers to projects such that a prioritized goal structure is most satisfactorily achieved. The model solution of the case example indicated the selection of five of the seven projects and the number of researchers assigned to each project. Of the nine prioritized goals, six were achieved while three were only partially achieved.

On the (re)discovery of fuzzy goal programming

Abstract: An earlier paper and subsequent commentaries in Decision Sciences described purportedly new methods for formulating and solving goal programming problems with fuzzy goals. This note suggests that much of what these articles propose is but a rediscovery of well-known results.

A multiple criteria approach for evaluating information systems

Abstract: An information system can be viewed as a symbiotic relationship between the users of the system and the system itself. Ideally, an information system should be evaluated with equal consideration given to both user constraints and to system constraints. The approach described in this article provides the analyst with a framework for gaining insight into information system performance from both user and system viewpoints by establishing a causal relationship between user goal attainment and system activity. This approach produces not only measures of current performance, but also predictive measures of future performance. The approach is based on a multiple goal programming formulation of the information system design evaluation problem. This article presents an overview of the formulation and its interpretation. The focus is on the analysis of an example system facilitated by this approach. A discussion of the applicability of the approach concludes this article.

A Chance-Constrained Goal Programming Model for Capital Budgeting

Abstract: A chance-constrained formulation is presented for a zero-one goal programming problem whose coefficients in the technological matrix are stochastic. The model is presented with a numerical example. A capital budgeting problem is taken for illustration.

A Goal Programming Approach to the Optimization of Multi response Simulation Models

Abstract: This paper presents a direct search approach to the optimization of multiresponse simulation models. The paper develops an approach within the framework of goal programming and uses a modified pattern search routine developed for this purpose. The algorithm and a graphical example are presented. The advantages and disadvantages of the approach determined from computational experiences with the solution procedure are discussed.

GODDESS: A Goal-Directed Decision Structuring System

Abstract: This paper describes an operational version of a computerized, domain-independent, decision support system which is based on a novel, goal-directed structure for representing decision problems. The structure allows the user to state relations among aspects, effects, conditions, and goals, in addition to actions and states which are the basic components of the traditional decision tree approach. The program interacts with the user in a stylized English-like dialogue, starting with the stated objectives and proceeding to unravel the more detailed means by which these objectives can be realized. At any point in time, the program focuses the user's attention on the issues which are most crucial to the problem at hand. The structure used is more compatible with the way people encode knowledge about problems and actions, and therefore promises to offer the following advantages: 1) judgments and beliefs issued by the user constitute a more valid representation of the user's experience; and 2) the user may be guided toward the discovery of action alternatives he otherwise would not have identified.

Slack, natural slack, and location covering☆

Abstract: This paper presents a goal programming approach to location covering and contrasts the resultant models with other covering formulations One of the primary differences between the models forwarded in this paper and those of previous studies in the notion of slack and natural slack in the definition of coverage relations Our approach takes advantage of the latter concept, natural slack, and produces a location covering form which is more flexible in the determination of objective functions than that of other formulations Furthermore, we show how the goal programming framework adopted herein allows consideration of a wider range of location covering policy issues

An Alternative Solution Method for Goal Programming Problems: A Tutorial

Abstract: A simplified description of a new computing procedure for goal programming problems is provided, together with a step-by-step solution of an illustrative example. The procedure is based on Baumol's simplex method for solving linear programming problems with minor modifications. The proposed computational method appears to be more efficient than goal programming methods which are in common use.

Planning with Goal Programming: A Case Study for Multiple-Use of Forested Land

Abstract: A case study is provided to develop and demonstrate a general goal programming procedure for hierarchical multiple land-use planning of forested lands with variable planning horizons. Four land-use policies containing timber harvesting, dispersed recreation, developed recreation, hunting and wildlife management are considered for a parcel of land incorporating 11,070 ha. The goals for each type of land-use are analyzed in terms of land-use capability coefficients, various priority settings, and planning horizons spanning from 2 to 36 years. It is shown that multiple-use conflicts can be resolved by either changing the priorities associated with conflicting uses, and (or) by extending planning horizons from short- to medium-term or long-term. Key Words: Land-use planning, multiple-use, goal programming, timber, developed and dispersed recreation, hunting, wildlife.

An Application of Goal Programming to Resolve a Site Location Problem

Abstract: This paper illustrates the use of a goal programming model to resolve a trucking terminal site location problem This is accomplished by allowing consideration of quantifiable personal preferences of the individuals who provide and use the truck terminal's services

On minimizing the duration of transportation

Abstract: The problem of minimizing the duration of transportation has been studied. The problem has been reduced to a goal programming-type problem which readily lends itself to solution by the standard transportation method. This approach to the solution of the problem is very much different from all other existing ones.

Planning and Problem Solving

Abstract: : Problem solving is the process of developing a sequence of actions to achieve a goal This broad definition admits all goal-directed artificial intelligence programs to the ranks of problem solvers; for example, MYCIN solves the problem of determining a bacteremia infection HARPY solves the problem of understanding speech signals, and solves the problem of filling in slots in its representations of concepts It follows that this chapter is not about problem solvers-the entire Handbook is about problem solvers This, is about problem- solving techniques In particular, it is about planning In everyday terms, planning means deciding on a course of action before acting This definition accurately describes the planning systems of this chapter, so we will adopt it A plan is, thus, a representation of a course of action It can be an unordered list of goals, such as a grocery list, but usually a plan has an implicit ordering of its goals; for example, most people plan to get dressed to go to the theater, not the other way around Many plans include steps that are vague and require further specification These serve as placeholders in a plan; for example, a daily plan includes the goal eat-lunch, although the details-where to eat, what to eat, when to leave- are not specified Reprints

A management science approach to contingency models of organizational structure.

Abstract: This study demonstrates the applicability of a quantitative modeling approach, specifically goal programming, in operationalizing the relationship between environmental variables and specific organ...

A heuristic weight determination procedure for goal programs used for harvest scheduling models

Abstract: A heuristic, "unstructured" weight determination procedure was developed for harvest scheduling models in which goal programming algorithms are employed. Six noninferior solution sets that included...

A Multicriteria Approach to Supersystem Architecture Definition

Abstract: The concept of large-scale, distributed computing or supersystems is still relatively new and, as experience is gained in the actual design of such systems, it has become increasingly obvious that: 1) the planning and design of a "good" supersystem is inherently far more complex than in the case of conventional systems; and 2) the truly optimal design of such systems can proceed only when all the conflicting goals and design specifications are systematically considered.

Parallelism in Planning and Problem Solving: Reasoning About Resources.

Abstract: : The implications of allowing parallel actions in a plan or problem solution are discussed. The planning system should take advantage of helpful interactions between parallel branches, must detect harmful interactions, and, if possibly, remedy them. This paper describes what is involved in this and presents some new techniques that are implemented in an actual planning system and are useful in seeking solutions to these problems. The most important of these techniques, reasoning about resources, is emphasized and explained. (Author)

GRAPES User's Manual.

Abstract: : GRAPES is a goal-restricted production system designed for modeling human cognitive processes. The system's declarative knowledge resides in a dynamic working memory and its procedural knowledge is embodied in condition-action rules known as productions. Each production can apply only in reference to the current goal. The goals are organized in an and/or tree with the root mode being the top goal, which is specified by the user. The tree is explored in a left-to-right, depth-first manner. Features of the language include goal parameter specification, flexible goal matching, LISP functions calls within production rules, and a host of user-accessible functions designed for their powerful matching ability. The interpreter includes an interrupt capability, a photo package, a tracing mechanism, and various debugging facilities. One peripheral module contains proceduralization and composition, two learning mechanisms used to acquire new productions. Another module contains useful functions for modelling LISP programmers. GRAPES is best-suited for highly goal-directed tasks involving planning or problem solving.

Multi‐Criteria Warehouse Location

Abstract: The problem of multi‐criteria optimisation has attracted a great deal of attention in the literature (for a brief discussion of various approaches see), one of the favourite methods of solution being that of goal programming. An example of how this method can be applied in the multi‐criteria warehouse location problem is described in a case study by Green et al, and because both the methodology and the solution raise several important issues, it has been chosen here for further discussion.

A goal programming model for allocating human resources for the good laboratory practice regulations

Abstract: The Food and Drug Administration's final Good Laboratory Practice regulation will have a far-reaching effect on testing laboratories that are conducting nonclinical laboratory safety tests. This paper develops a goal programming decision model suitable for setting objectives, meeting budgetary and operational constraints, planning personnel utilization, and evaluating different proposals for allocating laboratory personnel to implement a program.