Mehrdad Tamiz

Practical goal programming

Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models, to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields. Suitable as both a text and reference, its nine chapters first provide a brief history, fundamental definitions, and underlying philosophies, and then detail the goal programming variants and define them algebraically. Chapter 3 details the step-by-step formulation of the basic goal programming model, and Chapter 4 explores more advanced modeling issues and highlights some recently proposed extensions. Chapter 5 then details the solution methodologies of goal programming, concentrating on computerized solution by the Excel Solver and LINGO packages for each of the three main variants, and includes a discussion of the viability of the use of specialized goal programming packages. Chapter 6 discusses the linkages between Pareto Efficiency and goal programming. Chapters 3 to 6 are supported by a set of ten exercises, and an Excel spreadsheet giving the basic solution of each example is available at an accompanying website. Chapter 7 details the current state of the art in terms of the integration of goal programming with other techniques, and the text concludes with two case studies which were chosen to demonstrate the application of goal programming in practice and to illustrate the principles developed in Chapters 1 to 7. Chapter 8 details an application in healthcare, and Chapter 9 describes applications in portfolio selection.

New Developments in Multiple Objective and Goal Programming

This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

Goal Programming Variants

This chapter introduces the major goal programming variants. The purpose and underlying philosophy of each variant are given. The three major variants in terms of underlying distance metric (and hence philosophy) used are introduced first. These are lexicographic, weighted, and Chebyshev goal programming. Then the variants in terms of the mathematical nature of the decision variables and/or goals used are introduced. These are fuzzy, integer, binary, and fractional goal programming.

History and Philosophy of Goal Programming

The above quote demonstrates that goal-based behaviour and decision making has a long history. This goal-based philosophy has been formalised in the modern field of operational research and management science by the technique of goal programming. The earliest goal programming formulation was introduced by Charnes et al.

An enhanced approach to the ranked voting system

This paper provides enhancements to a Data Envelopment Analysis (DEA) and Assurance Region (AR) model for a ranked voting system. A transformation of the given data reduces the number of constraints on the weights in the model, thus making it more effective and easier to solve. Another advantage of the improved model is to decrease the effect of inefficient candidates on the efficiency of the efficient candidates. The advantages of the proposed enhancements are illustrated by an example.

Using Taguchi method for post optimality analysis in MCDM

The aim of this research is to use Taguchis loss function to assess the loss associated with a solution provided by a particular Multi-Criteria Decision Making (MCDM) method. Although the idea proposed in this paper, derived from Taguchis philosophy and his idea of social loss, can be invariably applied to any MCDM method, the paper utilises Goal Programming (GP) to illustrate the applicability of the proposed method.

Trend of Integration and Combination of Goal Programming

This chapter is concerned with the place of goal programming within the wider fields of multi-criteria decision making, operational research, and artificial intelligence. Modern operational research methodology no longer regards its constituent techniques as stand-alone modelling and solution tools but looks to combine them. Hence there is a growing body of research in fields such as systems and hyper-heuristics (Ozcan et al., 2008), which seek to intelligently select and mix techniques for symbiotic advantage.

Formulating Goal Programmes

This chapter concerns methods and concepts that need to be considered in order to formulate effective goal programming models. There are many issues unique to goal programming that even those who are experienced in building other types of operational research and/or mathematical programming models need to be aware of. This chapter will address the modelling issues sequentially and show how to employ good goal programming practice and avoid common pitfalls.

Advanced Topics in Goal Programming Formulation

The topic of how to formulate effective goal programmes using the major variants is covered in Chapter 3. This chapter expands on those concepts by introducing techniques developed to expand the flexibility of goal programming and enhance its use.

Solving and Analysing Goal Programming Models

The purpose of this chapter is to explain how to ‘effectively’ solve and analyse goal programming models. The majority of the chapter will consider the solution and analysis of the three major goal programming variants using computerised software. The theory and methods of goal programming solution will also be detailed.