Goal programming

About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.

Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP

Abstract: In recent years, sustainable development strategy for enterprises has become an important issue around the globe. There are four management systems (i.e. ISO 9001, ISO 14001, OHSAS 18001, and SA 8000) that can help small and medium enterprises (SMEs) to create sustainable competitive advantages. In view of the fact that the shortage of resources - time, personnel, as well as money - rules most SMEs, this paper proposes a novel hybrid model for selecting optimal management systems under resource constraints, and illustrates the practical application of such a model through an example. This model first applies the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach to construct interrelations among criteria that organizations require. The second step is to obtain the criterion weights through ANP. Lastly, ANP is integrated with a zero-one goal programming (ZOGP) model to obtain optimal alternatives with desired organizational benefits by fully utilizing limited resources. The purpose of this study is to present an integrated approach that could cope with the interdependencies among various criteria and deal with the constraints on resources, and to demonstrate how to select management systems for phased implementation. Therefore, the main contribution of this paper is to enhance the capacity of SMEs to effectively address the challenge of sustainable development through a novel model of prioritizing available management systems.

A goal programming model for paper recycling system

Abstract: 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.

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

Multiple Criteria Decision Support in Engineering Design

Abstract: 1. Introduction.- 1.1 What is Multiple Criteria Decision Making.- 1.2 Relevance of MCDM to Engineering Design.- 1.2.1 The Structure of a Design Problem.- 1.2.2 The Principal Issues in Multiple Criteria Decision Making.- 1.2.3 Issues of Complexity, Subjectivity and Uncertainty.- 1.3 Design Selection vs Design Synthesis.- 1.4 Outline of the Book.- 2. MCDM and The Nature of Decision Making in Design.- 2.1 Introduction.- 2.2 Pareto Optimality: What are the Options?.- 2.3 MCDM Methods and Some Key Terminology.- 2.4 Concluding Comments.- 3. Multiple Attribute Decision Making.- 3.1 Problem Formulations and Method Classification.- 3.1.1 MADM Problems.- 3.1.2 Classification of MADM Methods.- 3.2 Techniques for Weight Assignment.- 3.2.1 Direct Assignment.- 3.2.2 Eigenvector Method.- 3.2.3 Entropy Method.- 3.2.4 Minimal Information Method.- 3.2.4.1 General Pairwise Comparisons and Minimal Information.- 3.2.4.2 Linear Programming Models for Weight Assignment.- 3.2.4.3 An Example.- 3.3 Typical MADM Methods and Applications.- 3.3.1 AHP Method and Application.- 3.3.2 UTA Method and Application.- 3.3.3 TOPSIS Method and Application.- 3.3.4 CODASID Method and Applications.- 3.3.4.1 Information Requirement and Normalization.- 3.3.4.2 New Concordance and Discordance Analyses.- 3.3.4.3 Preference Matrix and CODASID Algorithm.- 3.3.4.4 Applications.- 3.3.5 Comments.- 3.4 A Hierarchical Evaluation Process.- 3.4.1 Design Decision Problems with Subjective Factors.- 3.4.2 A Hierarchical Evaluation Process.- 3.4.3 The Ship Choice Problem.- 3.5 Concluding Comments.- 4. Multiple Objective Decision Making.- 4.1 Multiobjective Optimisation and Method Classification.- 4.1.1 Multiobjective Optimisation and Utility Functions.- 4.1.2 Classification of MODM Methods.- 4.2 Techniques for Single-Objective Optimisation.- 4.2.1 Optimality Conditions.- 4.2.2 Sequential Linear Programming.- 4.2.3 Penalty Methods.- 4.3 Typical MODM Methods.- 4.3.1 Goal Programming.- 4.3.2 Geoffrion's Method.- 4.3.3 Minimax Method.- 4.3.4 ISTM Method.- 4.3.5 Local Utility Function Method.- 4.4 Multiobjective Ship Design.- 4.4.1 A Nonlinear Preliminary Ship Design Model.- 4.4.2 Generation of Subsets of Efficient Ship Designs.- 4.4.3 Progressive Design.- 4.4.4 Design by Setting Target Values.- 4.4.5 Adaptive and Compromise Design.- 4.5 Concluding Comments.- 5. Multiple Criteria Decision Making and Genetic Algorithms.- 5.1 Introduction.- 5.2 The Mechanics of the Simple Genetic Algorithm.- 5.2.1 Selection, Crossover and Mutation.- 5.2.2 A Bi-Modal Optimisation Problem.- 5.2.3 The Need for a Multiple Criteria Approach.- 5.3 Multiple Criteria Genetic Algorithms.- 5.3.1 Some Comparative Multiple Criteria G A Approaches.- 5.3.2 Common Issues in Multiple Criteria Genetic Algorithms in Engineering Design.- 5.3.3 Crowding and Niching.- 5.3.4 Estimating Niche Sizes.- 5.4 The Multiple Criteria Genetic Algorithm (MCGA): A Summary.- 5.5 A Numerical Example.- 5.6 An MCGA Schedule for a Generalised Job Shop.- 5.6.1 Problem Data.- 5.6.2 String Configuration.- 5.6.3 The Results from MCGA.- 5.7 Concluding Comments.- 6. An Integrated Multiple Criteria Decision Support System.- 6.1 System Structure and Method Selection.- 6.1.1 General Structure of IMC-DSS.- 6.1.2 The Routine Base for MCDM Techniques.- 6.1.3 Rules for Selection of MADM and MODM Methods.- 6.2 Data Base and Model Base.- 6.2.1 Decision Models and File Systems.- 6.2.2 Semi-Automatic Model Generation.- 6.3 A User Interface and Interactive Decision Making.- 6.3.1 Menu-Driven Interfaces.- 6.3.2 A Unified Approach for Generating and Ranking Design.- 6.4 Application of IMC-DSS.- 6.4.1 A Multiattribute Vessel Choice Problem.- 6.4.2 A Multiobjective Semi-Submersible Design Problem.- 6.4.3 Design Using the Unified Approach.- 6.5 Concluding Comments.- 7. Past, Present and the Future.- 7.1 Introduction.- 7.2 Case Studies.- 7.2.1 Designing product development processes to minimize lead times.- 7.2.2 Multicriteria robust optimisation under uncertainty of catamarans from a seakeeping point of view.- 7.3 Concluding Comments.- References.- Topic Index.