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Michael Jackson defines a Problem Frame as a mean to describe and classify software development problems. The initial description of problem Frames is essentially graphical. A weakness of this proposal is the lack of formal specification allowing efficient reasoning tools. This paper deals with Problem Frames' formal specification with Description Logics. We first propose a formal terminology of Problem Frames leading to the specification of a Problem Frames' TBOX and a specific problem's ABOX. The Description Logics inference tools can then be used to decompose multi frame problems or to fix a particular problem into a Problem Frame.

this paper deals with an automatic document's synthesis system. Our approach is based on the prior formal description of the semantics of the main elements (document, reader and his request) in the synthesis system. In this approach, semantic capture is based on ontology definition that is specified formally using Description Logics (DL). The DL inference techniques associated to production rules are then used to compute a document synthesis. Moreover, DL inference techniques are used to reason about each component.

This study sought to determine the best method to quantify training based on heart rate data. It proposes a modification of Banister’s original performance model to improve the accuracy of predicted performance. The new formulation introduces a variable that accounts for changes in the subject’s initial performance as a result of the quantity of training. The two systems models were applied to a well-trained female monofin swimmer over a 24-week training period. Each model comprised a set of parameters unique to the individual and was estimated by fitting model-predicted performance to measured performance. We used the Alienor method associated to Optimization-Preserving Operators to identify these parameters. The quantification method based on training intensity zones gave a better estimation of predicted performance in both models. Using the new model in sports in which performance is generally predicted (running, swimming) will help us to define its real interest.

This paper deals with the Alienor method to tackle multiobjective nonlinear optimization problems. In this approach, the multiple criteria of the optimization problem are aggregated into a single one using weighted sums. Then, the resulting single objective nonlinear optimization problem is solved using the Alienor method associated with the Optimization Preserving Operators   .. OP O  technique which has proved to be suitable for (nonlinear) optimization problems with a large number of variables (see [1]). The proposed approach is evaluated through test problems. The results show that the approach provides good approximations of the Pareto front while requiring small computational time, even for large instances.

PurposeThe purpose of this paper is to present an efficient algorithm to solve multi‐objective linear programming (MOLP) problem.Design/methodology/approachThis new approach consists to convert the constrained multicriteria problem into an unconstrained global optimization problem. Then, the Alienor method coupled to the optimization preserving operators* (OPO*) technique is used to solve the transformed problem.FindingsA determinist algorithm for solving general MOLP problem contributes to research in the decision‐makers area.Research limitations/implicationsSome improvements could probably be obtained. In future work, other scalarized functions will be used and this algorithm's complexity will be studied.Practical implicationsThe new algorithm can be advantageously compared with other methods To illustrate this new approach, an example is studied.Originality/valueA new algorithm is given which guarantees all efficient solutions are easily obtained in most cases.

PurposeTo use a new method based on α‐dense curved for solving problems of operational research.Design/methodology/approachThe method of global optimization (called Alienor) is used for solving problems involving integer or mixed variables. A reducing transformation using α‐dense curves allows to transforms a n‐variables problem into a problem of a single variable.FindingsExtends the basic method valid for continuous variables to problems involving integer, Boolean or mixed variables. All problems of operational research, linear or nonlinear, may be easily solved by or technique based on α‐dense curves (filling a n‐dimensional space). Industrial problems can be quickly solved by this technique obtaining the best solutions.Originality/valueThis method is deduced from the original works of Y. Cherruault and colleagues about global optimization and α‐dense curves. It proposes new techniques for solving operational research problems.

PurposeTo introduce Optimization‐Preserving‐Operators (O‐P‐Os), which are operators that are defined on classes of real functions that depend on a single variable, and allow us to eliminate local optima and to preserve global optima.Design/methodology/approachOutline a new method to build O‐P‐Os. These are introduced as O‐P‐O* and lead to a new approach for solving global optimization problems.FindingsIt was found that classical discretization methods for obtaining optimum of one variable function was too time‐consuming. The simple method introduced provided solutions to the test functions chosen as examples. The solutions were provided in a short time.Research limitations/implicationsProvides new tools for mathematical programming and in particular the global optimization problems. The O‐P‐O* introduced innovative technique for solving such problems.Practical implicationsO‐P‐O* produces solutions to global optimization problems in a much improved time. The algorithm derived, and the steps for its operation proved on implementation, the efficiency of the new method. This was demonstrated by numerical results for selected functions obtained using microcomputer systems.Originality/valueProvides new way of solving global optimization problems.

Purpose – This paper presents an efficient algorithm for solving general constrained optimization problems that arise in operational research (OR).Design/methodology/approach – An unified approach is accomplished by converting the constrained optimization problem into an unconstrained one and by using Alienor method coupled to the new optimization preserving operator* (OPO*) technique for the resolution.Findings – A new algorithm for solving general constrained optimization problems with continuous objective function contributes to research in this area and in particular, to applications to OR.Research limitations/implications – Some improvements could probably be obtained at calculation time. We will in future work, develop an adaption of these methods and techniques to optimization problems with mixed variables or with integer and Boolean variables.Practical implications – The new algorithm can be advantageously compared with other methods such as generalized reduced gradient. Small‐sized numerical exam...

PurposeTo propose a new method for solving constrained global optimization problems using a method that consists of transforming a constrained global optimization problem into an unconstrained one without using any penalty coefficients.Design/methodology/approachUse of an unconstrained global optimization method such as the Alienor method which has been adapted for several variables.FindingsUse of the adapted Alienor method allowed the solution of the transformed problem with little difficulty.Research limitations/implicationsTransforms the original objective function into a new one involves the introduction of some extra parameters. Cannot guarantee the convergence to a global solution of the original problem. The simple described approach, provides new possibilities.Practical implicationsNo further parameters introduced in this new approach, and no conditions or hypotheses are imposed on the objective function or on the constraints.Originality/valueNew method of transforming a constrained problem into an unconstrained one, with use of proven Alienor method adapted to several variables.