The Stochastic Goal Programming Model: Theory and Applications

Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision‐making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision‐making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains. Copyright © 2012 John Wiley & Sons, Ltd.     

Instruction Modes for Motor Control Skills Acquisition: A Randomized Controlled Trial

Abstract

The objective was to compare two different instruction modes used to teach patients with nonspecific chronic low back pain (CLBP) to perform a lumbar motor control task. The three intervention instruction modes used were: common verbal explanation of a motor task based on a motor control therapeutic exercise (MCTE-control group), MTCE instructed using motor imagery (MI) and MCTE instructed using tactile feedback (TF). The main outcome measure was lumbar motor control of the neutral position test. Forty-eight patients with CLBP were randomly allocated into three groups of 16 patients per group. The MI strategy was the most effective mode for developing the motor control task in an accurate and controlled manner, obtaining better outcomes than TF or verbal instruction.