Goal programming

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

Optimization Modelling: A Practical Approach

Abstract: INTRODUCTION Introduction General Introduction History of Optimization Optimization Problems Mathematical Model Concept of Optimization Classification of Optimization Problems Organization of the Book References Exercises The Process of Optimization Introduction Decision Process Problem Identification and Clarification Problem Definition Development of a Mathematical Model Deriving a Solution Sensitivity Analysis Testing the Solution Implementation Chapter Summary Exercises Introduction to Modelling Introduction Components of a Mathematical Model Simple Examples Analysing a Problem Modelling a Simple Problem Linear Programming Model More Mathematical Models Integer Programming Multi-Objective Problem Goal Programming Nonlinear Programming Chapter Summary Exercises MODELLING TECHNIQUES Simple Modelling Techniques I Introduction The Use of Subscripts in Variables Simple Modelling Techniques Special Types of LP Chapter Summary References Exercises Simple Modelling Techniques II Introduction Precedence Constraints Either-or Constraints K out of N Constraints must Hold Yes-or-No Decisions Functions with N Possible Values Mutually Exclusive Alternatives and Contingent Decisions Linking Constraints with the Objective Function Piecewise Linear Functions Nonlinear to Approximate Functions Deterministic Models with Probability Terms Alternate Objective Functions Constrained to Unconstrained Problem Simplifying Cross Product of Binary Variables Fractional Programming Unrestricted Variables Changing Constraint and Objective Type Conditional Constraints Dual Formulation Regression Model Stochastic Programming Constraint Programming Chapter Summary References Bibliography Exercises Modelling Large-Scale and Well-Known Problems I Introduction Use of the Summation Sign Use of the Subset Sign Network Flow Problems The Knapsack Problem Facility Location and Layout Production Planning and Scheduling Logistics and Transportation Chapter Summary References Exercises Modelling Well-Known Problems II Introduction Job and Machine Scheduling Assignment and Routing Staff Rostering and Scheduling Scheduling and Timetabling Problem Chapter Summary References Exercises Alternative Modelling Introduction Modelling under Different Assumptions Hierarchical Modelling: An Introduction Chapter Summary References MODEL SOLVING Solution Approaches: An Overview Introduction Complexity and Complexity Classes Classical Optimization Techniques Heuristic Techniques Optimization Software Chapter Summary References Appendix-9A: LINDO /LINGO Appendix -9B: MPL Appendix -9C: GAMS Appendix -9D: Solver Appendix -9E: Win QSB Input Preparation and Model Solving Introduction Data and Data Collection Data Type Data Preparation Data Preprocessing Model Driven Data vs. Data Driven Model Model Solving Chapter Summary References Exercises Appendix-10A: Additional Problem Solving using LINGO Output Analysis and Practical Issues Introduction Solutions and Reports Sensitivity Analysis Practical Issues and Tips Risk Analysis Chapter Summary Exercises Basic Optimization Techniques Introduction Graphical Method Simplex Method Branch and Bound Method Chapter Summary References Exercises PRACTICAL PROBLEMS Models For Practical Problems I Introduction A Crop Planning Problem Power Generation Planning A Water Supply Problem A Supply Chain Problem Coal Production and Marketing Plan General Blending Problem Chapter Summary References Models for Practical Problems II Introduction A Combat Logistics Problem A Lot Sizing Problem A Joint Lot-Sizing and Transportation Decision Problem Coal Bank Scheduling A Scaffolding System A Gas-Lift Optimization Problem Multiple Shifts Planning Chapter Summary References Solving Practical Problems Introduction A Product-Mix Problem A Two-Stage Transportation Problem A Crop Planning Problem Power Generation Planning Problem Gas Lift Optimization Chapter Summary References Appendix-A: Crop Planning LP Model

A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates

Abstract: This paper presents a new mixed-integer goal programming (MIGP) model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates. Two objectives are considered in the model to minimize the total weighted flow time and the total weighted tardiness simultaneously. Due to the complexity of the above model and uncertainty involved in real-world scheduling problems, it is sometimes unrealistic or even impossible to acquire exact input data. Hence, we consider the parallel-machine scheduling problem with sequence-dependent set-up times under the hypothesis of fuzzy processing time's knowledge and two fuzzy objectives as the MIGP model. In addition, a quite effective and applicable methodology for solving the above fuzzy model are presented. At the end, the effectiveness of the proposed model and the denoted methodology is demonstrated through some test problems.

Goal directed benchmarking for organizational efficiency

Abstract: In this paper, we extend the standard data envelopment analysis (DEA) model to include longer term top management goals. This extension is in recognition of the fact that benchmarking for decision making units (DMUs) is more than a purely monitoring process, and includes a component of future planning. The new model uses a goal programming structure to find points on the efficient frontier which are realistically achievable by DMUs, but at the same time achieving a closer approach to long term organizational goals (as distinct from the local performance of individual DMUs). Consideration is given to the possibility of adjusting constraints on the DMU by investment in extended inputs or new technologies, in which case minimization of associated investment costs becomes an additional management objective.