the index is excellent by the malmquist index as extended to productivity measurement. the index developed here is defined in terms of input cost rather than input quantity distance functions in supply chain. therefore, we propose productivity change is decomposed into overall efficiency and cost technical change. these decompositions provide a clearer situation of the root sources of supply ch...

In this paper we propose a simple non-parametric model for multiple crite-ria supplier selection problem. The proposed model does not generate a zeroweight for a certain criterion and ranks the suppliers without solving the modeln times (one linear programming (LP) for each supplier) and therefore allowsthe manager to get faster results. The methodology is illustrated using anexample.

A nonconvex generalized semi-infinite programming problem is considered involving parametric max-functions in both, the objective and the constraints. For a fixed vector of parameters, the values of these parametric max-functions are given as optimal values of convex quadratic programming problems. Assuming that for each parameter the parametric quadratic problems satisfy the strong duality rel...

Many studies and reports support the significant impact of the application of group technology on various aspects of product design and manufacturing. The fact that group tech­nology is the common solution to similar problems is the main source of such impact. In this paper, one of the less frequent­ly utilized features of GNG technology known as parametric part programming is used to implement...

CONTENTS SUMMARY "Measure boundary" concepts for domains with a metric Use of task-related boundary Metrical and non-metrical concepts, and polymorphism Parametric polymorphism in biological designs Family Resemblance Ryle’s Polymorphism Parametric polymorphism in computer science Exercises for the reader Evolution and parametric polymorphism Programming languages with parametric polymorphism M...

In this work, we present a new algorithm for solving complex multi-stage optimization problems involving hard constraints and uncertainties, based on dynamic and multi-parametric programming techniques. Each echelon of the dynamic programming procedure, typically employed in the context of multi-stage optimization models, is interpreted as a multi-parametric optimization problem, with the prese...

This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting the algorithm, we prove a contraction estimate that guarantees the tracking performance of the algorithm. Two variants of this algorithm are investigated. The first can be used to treat online parametric nonline...