Linear, Integer, Separable and Fuzzy Programming Problems: A Unified Approach towards Reformulation

For mathematical programming (MP) to have greater impact as a decision tool, MP software systems must offer suitable support in terms of model communication and modelling techniques. In this paper, modelling techniques that allow logical restrictions to be modelled in integer programming terms are described, and their implications discussed. In addition, it is illustrated that many classes of non-linearities which are not variable separable may be, after suitable algebraic manipulation, put in a variable separable form. The methods of reformulating the fuzzy linear programming problem as a max-min problem is also introduced. It is shown that analysis of bounds plays a key role in the following four important contexts: model reduction, reformulation of logical restrictions as 0-1 mixed integer programmes, reformulation of non-linear programmes as variable separable programmes and reformulation of fuzzy linear programmes. It is observed that, as well as incorporating an interface between the modeller and the optimizer, there is a need to make available to the modeller software facilities which support the model reformulation techniques described here.

[1]  Robert Fourer,et al.  Modeling languages versus matrix generators for linear programming , 1983, TOMS.

[2]  Klaus Schittkowski,et al.  More test examples for nonlinear programming codes , 1981 .

[3]  George B. Dantzig,et al.  Linear programming and extensions , 1965 .

[4]  Gautam Mitra,et al.  Theory and application of mathematical programming , 1976 .

[5]  Harvey J. Greenberg,et al.  Computer-assisted analysis and model simplification , 1981 .

[6]  Thomas L. Magnanti,et al.  Applied Mathematical Programming , 1977 .

[7]  G. McCormick,et al.  The polyadic structure of factorable function tensors with applications to high-order minimization techniques , 1986 .

[8]  Gautam Mitra,et al.  UIMP: User Interface for Mathematical Programming , 1982, TOMS.

[9]  Robert G. Dyson Maximin Programming, Fuzzy Linear Programming and Multi-Criteria Decision Making , 1980 .

[10]  Gautam Mitra,et al.  Computer-Assisted Mathematical Programming (Modelling) System: CAMPS , 1988, Comput. J..

[11]  Cormac Lucas,et al.  COMPUTER ASSISTED MODELLING OF LINEAR, INTEGER AND SEPARABLE PROGRAMMING PROBLEMS , 1984 .

[12]  Ron S. Dembo,et al.  A set of geometric programming test problems and their solutions , 1976, Math. Program..

[13]  H. P. Williams,et al.  Model Building in Mathematical Programming , 1979 .

[14]  H. P. Williams Experiments in the formulation of integer programming problems , 1974 .

[15]  H. Zimmermann Fuzzy programming and linear programming with several objective functions , 1978 .

[16]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[17]  H. J. Hagger,et al.  An introduction to numerical mathematics , 1964 .

[18]  Hans-Jürgen Zimmermann,et al.  Media Selection and Fuzzy Linear Programming , 1978 .

[19]  H. P. Williams A Reduction Procedure for Linear and Integer Programming Models , 1983 .

[20]  David M. Smith,et al.  A model-management framework for mathematical programming , 1984 .

[21]  Michel Balinski,et al.  Computational practice in mathematical programming , 1975 .

[22]  Haruo Satoh,et al.  AN APPLICATION OF FUZZY LINEAR PROGRAMMING TO EXPANSION PLANNING OF ELECTRIC POWER GENERATION , 1984 .

[23]  J. K. Lowe,et al.  Some results and experiments in programming techniques for propositional logic , 1986, Comput. Oper. Res..