Showing papers on "Goal programming published in 2007"

Operations Research an Introduction

Abstract: 1. Overview of Operations Research. I. DETERMINISTIC MODELS. 2. Introduction to Linear Programming. 3. The Simplex Method. 4. Duality and Sensitivity Analysis. 5. Transportation Model and Its Variants. 6. Network Models. 7. Advanced Linear Programming. 8. Goal Programming. 9. Integer Linear Programming. 10. Deterministic Dynamic Programming. 11. Deterministic Inventory Models. II. PROBABILISTIC MODELS. 12. Review of Basic Probability. 13. Forecasting Models. 14. Decision Analysis and Games. 15. Probabilistic Dynamic Programming. 16. Probabilistic Inventory Models. 17. Queueing Systems. 18. Simulation Modeling. 19. Markovian Decision Process. III. NONLINEAR MODELS. 20. Classical Optimization Theory. 21. Nonlinear Programming Algorithms. Appendix A: Review of Matrix Algebra. Appendix B: Introduction to Simnet II. Appendix C: Tora and Simnet II Installation and Execution. Appendix D: Statistical Tables. Appendix E: Answers to Odd-Numbered Problems. Index.

Multi-choice goal programming

Abstract: The situation of multi-choice aspiration levels (MCAL) may exist in many decision/management problems. However, the problem cannot be solved by current goal programming (GP) techniques. In order to improve the utility of GP and solve the MCAL problem, this paper proposes a new idea for programming the MCAL problem. The proposed method allows decision-makers (DMs) to set multiple aspiration levels for their problems in which “the more/higher is better” and “the less/lower is better” in the aspiration levels are addressed. In addition, illustrative examples are given to demonstrate the correctness of the proposed model.

Performance evaluation of bus routes: A provider and passenger perspective

Abstract: In this paper, the provision of bus services along different routes that comprise a public transit network is assessed taking into consideration, the service providers, the users and the societal perspectives. This model is based on Data Envelopment Analysis (DEA) [Charnes, A., Cooper W.W., Rhodes, E., 1978. Measuring the efficiency of decision-making units. European Journal of Operational Research 2, 429–444] and derives from the Network Model in DEA [Fare, R., Grosskopf, S., 2000. Network DEA. Socio-Economic Planning Sciences 34, 35–49] and Goal Programming in DEA [Athanassopoulos, A., 1995. Goal programming and data envelopment analysis (GoDEA) for target-based multi-level planning: allocating central grants to the Greek local authorities. European Journal of Operational Research 87, 535–550]. This proposed approach enables the decision maker not only to optimally allocate resources across the transit network but to achieve targets for societal variables that represent the environment in which the bus services are provided.

Multiple-Attribute Group Decision Making With Different Formats of Preference Information on Attributes

Abstract: Interval utility values, interval fuzzy preference relations, and interval multiplicative preference relations are three common uncertain-preference formats used by decision-makers to provide their preference information in the process of decision making under fuzziness. This paper is devoted in investigating multiple-attribute group-decision-making problems where the attribute values are not precisely known but the value ranges can be obtained, and the decision-makers provide their preference information over attributes by three different uncertain-preference formats i.e., 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first utilize some functions to normalize the uncertain decision matrix and then transform it into an expected decision matrix. We establish a goal-programming model to integrate the expected decision matrix and all three different uncertain-preference formats from which the attribute weights and the overall attribute values of alternatives can be obtained. Then, we use the derived overall attribute values to get the ranking of the given alternatives and to select the best one(s). The model not only can reflect both the subjective considerations of all decision-makers and the objective information but also can avoid losing and distorting the given objective and subjective decision information in the process of information integration. Furthermore, we establish some models to solve the multiple-attribute group-decision-making problems with three different preference formats: 1) utility values; 2) fuzzy preference relations; and 3) multiplicative preference relations. Finally, we illustrate the applicability and effectiveness of the developed models with two practical examples.

Municipal Solid Waste Management Under Uncertainty: A Mixed Interval Parameter Fuzzy-Stochastic Robust Programming Approach

Abstract: A mixed interval parameter fuzzy-stochastic robust programming (MIFSRP) model is developed and applied to the planning of solid waste management systems under uncertainty The MIFSRP can explicitly address system uncertainties with multiple presentations It can be used as an extension of the existing interval-parameter fuzzy robust programming, interval-parameter linear programming, and chance constraint programming methods In this MIFSRP model, the hybrid uncertainties can be directly communicated into the optimization process and resulting solution through representing the uncertain parameters as interval numbers and fuzzy membership functions with random characteristics Highly uncertain information arising from simultaneous appearance of fuzziness and randomness for the lower and upper bounds of interval parameters can be effectively addressed through integrating chance constraint programming, interval linear programming, and fuzzy robust programming methods into a general optimization framework Th

Operations Research and Management Science Handbook

Abstract: I OR/MS Models and Methods Linear Programming, K. G. Murty Brief History of Algorithms for Solving Linear Equations, Linear Inequalities, and LPs Applicability of the LP Model: Classical Examples of Direct Applications LP Models Involving Transformations of Variables Intelligent Modeling Essential to Get Good Results, an Example from Container Shipping Planning Uses of LP Models Brief Introduction to Algorithms for Solving LP Models Software Systems Available for Solving LP Models Multiobjective LP Models Nonlinear Programming, T.B. Trafalis and R.C. Gilbert Introduction Unconstrained Optimization Constrained Optimization Conclusion Integer Programming, M. Weng Introduction Formulation of IP Models Branch and Bound Method Cutting Plane Method Other Solution Methods and Computer Solution Network Optimization, M.B. Yildirim Introduction Notation Minimum Cost Flow Problem Shortest Path Problem Maximum Flow Problem Assignment Problem Minimum Spanning Tree Problem Minimum Cost Multicommodity Flow Problem Conclusions Multiple Criteria Decision Making, A.S. M. Masud and A. R. Ravindran Some Definitions The Concept of "Best Solution" Criteria Normalization Computing Criteria Weights Multiple Criteria Methods for Finite Alternatives Multiple Criteria Mathematical Programming Problems Goal Programming Method of Global Criterion and Compromise Programming Interactive Methods MCDM Applications MCDM Software Further Readings Decision Analysis, C. M. Klein Introduction Terminology for Decision Analysis Decision Making under Risk Decision Making under Uncertainty Practical Decision Analysis Conclusions Resources Dynamic Programming, J. A. Ventura Introduction Deterministic Dynamic Programming Models Stochastic Dynamic Programming Models Conclusions Stochastic Processes, S. H. Xu Introduction Poisson Processes Discrete-Time Markov Chains Continuous-Time Markov Chains Renewal Theory Software Products Available for Solving Stochastic Models Queueing Theory, N. Gautam Introduction Queueing Theory Basics Single-Station and Single-Class Queues Single-Station and Multiclass Queues Multistation and Single-Class Queues Multistation and Multiclass Queues Concluding Remarks Inventory Control, F. Azadivar and A. Rangarajan Introduction Design of Inventory Systems Deterministic Inventory Systems Stochastic Inventory Systems Inventory Control at Multiple Locations Inventory Management in Practice Conclusions Current and Future Research Complexity and Large-Scale Networks, H. P. Thadakamalla, S. R.T. Kumara, and R. Albert Introduction Statistical Properties of Complex Networks Modeling of Complex Networks Why "Complex" Networks Optimization in Complex Networks Conclusions Simulation, C. M. Harmonosky Introduction Basics of Simulation Simulation Languages and Software Simulation Projects-The Bigger Picture Summary Metaheuristics for Discrete Optimization Problems, R.K. Kincaid Mathematical Framework for Single Solution Metaheuristics Network Location Problems Multistart Local Search Simulated Annealing Plain Vanilla Tabu Search Active Structural Acoustic Control (ASAC) Nature Reserve Site Selection Damper Placement in Flexible Truss Structures Reactive Tabu Search Discussion Robust Optimization, H. J. Greenberg and T. Morrison Introduction Classical Models Robust Optimization Models More Applications Summary II OR/MS Applications Project Management, A. B. Badiru Introduction Critical Path Method PERT Network Analysis Statistical Analysis of Project Duration Precedence Diagramming Method Software Tools for Project Management Conclusion Quality Control, Q. Feng and K. C. Kapur Introduction Quality Control and Product Life Cycle New Trends and Relationship to Six Sigma Statistical Process Control Process Capability Studies Advanced Control Charts 16.7 Limitations of Acceptance Sampling 16.8 Conclusions Reliability, L. M. Leemis Introduction Reliability in System Design Lifetime Distributions Parametric Models Parameter Estimation in Survival Analysis Nonparametric Methods Assessing Model Adequacy Summary Production Systems, B. L. Foote and K. G. Murty Production Planning Problem Demand Forecasting Models for Production Layout Design Scheduling of Production and Service Systems Energy Systems, C. R. Hudson and A. B. Badiru Introduction Definition of Energy Harnessing Natural Energy Mathematical Modeling of Energy Systems Linear Programming Model of Energy Resource Combination Integer Programming Model for Energy Investment Options Simulation and Optimization of Distributed Energy Systems Point-of-Use Energy Generation Modeling of CHP Systems Economic Optimization Methods Design of a Model for Optimization of CHP System Capacities Capacity Optimization Implementation of the Computer Model Other Scenarios Airline Optimization, J. L. Snowdon and G. Paleologo Introduction Schedule Planning Revenue Management Aircraft Load Planning Future Research Directions and Conclusions Financial Engineering, A. R. Heching and A. J. King Introduction Return Estimating an Asset's Mean and Variance Diversification Efficient Frontier Utility Analysis Black-Litterman Asset Allocation Model Risk Management Options Valuing Options Dynamic Programming Pricing American Options Using Dynamic Programming Comparison of Monte Carlo Simulation and Dynamic Programming Multi-Period Asset Liability Management Conclusions Supply Chain Management, D. P. Warsing Introduction Managing Inventories in the Supply Chain Managing Transportation in the Supply Chain Managing Locations in the Supply Chain Managing Dyads in the Supply Chain Discussion and Conclusions E-Commerce, S. Sadagopan Introduction Evolution of E-Commerce OR/MS and E-Commerce OR Applications in E-Commerce Tools-Applications Matrix Way Forward Summary Water Resources, G.V. Loganathan Introduction Optimal Operating Policy for Reservoir Systems Water Distribution Systems Optimization Preferences in Choosing Domestic Plumbing Materials Stormwater Management Groundwater Management Summary Military Applications, J. D. Weir and M. U. Thomas Introduction Background on Military OR Current Military Applications of OR Concluding Remarks Future of OR/MS Applications: A Practitioner's Perspective, P. Balasubramanian Past as a Guide to the Future Impact of the Internet Emerging Opportunities Index

An interactive three-stage model for mutual funds portfolio selection ☆

Abstract: The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.

Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment

Abstract: A monolithic mixed integer linear goal programming (MILGP) model that is developed in this paper produces a time and capacity aggregated production plan, a detailed production plan, a detailed procurement plan and a detailed distribution plan simultaneously to overcome the drawbacks of the hierarchical/sequential planning approaches of not yielding a feasible and/or an optimal plan. The model uses different time-grids and planning horizons for aggregate and detailed planning to reduce the computational burden. The limitations of storage space, raw material availability and production capacity at plants and a requirement of maintaining a minimum level of inventory buffer or forward cover have been modelled. Two heuristics that are proposed to solve the MILGP model gave good quality solutions with an average and the worse case optimality gap of 1.17% and 4.4% respectively when applied to one hundred randomly generated industry size problem instances.

Constrained optimization using multiple objective programming

Abstract: In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically designed to incorporate such complex preference structures, they gain more and more importance in application areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share important properties, and that many classical solution approaches have correspondences in the respective models. The analysis naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence results are proven for convex and nonconvex problems.

Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies

Abstract: Manufacturing organizations often make complex decisions in regards to investment in new manufacturing technologies (NMT) These NMT are assessed based on attributes such as flexibility, quality etc, which are hard to quantify This fact calls upon the need for a structured decision support systems that can adequately represent qualitative and subjective assessments The gist of this paper is to propose an integrated fuzzy analytic hierarchy process (AHP) based approach to facilitate the selection and evaluation of NMT in the presence of intangible attributes and uncertainty Three important issues that are pertinent and critical to fuzzy AHP, namely contradiction in user preferences, deriving priorities from inconsistent fuzzy judgment matrices, and group decision-making are addressed in detail Also, an attempt is made to compare fuzzy preference programming (FPP) and two-stage logarithmic goal programming (TLGP) based fuzzy prioritization methods Based on the comparative study, it is concluded that FPP is preferred over TLGP to select and evaluate NMT Numerical examples are provided to illustrate the aforementioned issues

Enhanced Index Investing Based on Goal Programming

Abstract: Enhanced index investing involves tracking a benchmark index closely and using risk-controlled strategies to add modest value to the index. The typical approaches to construction of such portfolios involve subjective management judgments. A new approach to enhanced indexing instead formulates the problem as a dual-criteria goal programming problem. Unlike the traditional approaches, which require a fund manager to buy and sell stocks actively in order to improve returns, the proposed approach is based on the passive management of a small number of stocks. Empirical results from tests in the Taiwan stock market suggest the new approach incurs lower transaction costs and produces sustainable risk-controlled enhanced returns.