Cerebrovascular accident (CVA) or stroke is one of the leading causes of disability and loss of motor function. Millions of people around the world are effected by it each year. Stroke results in disabled arm function. Restoration of arm function is essential to regaining activities of daily living (ADL). Along with traditional rehabilitation methods, robot-aided therapy has emerged in recent years. Robot-aided rehabilitation is more intensive, of longer duration and more repetitive. Using robots, repetitive dull exercises can turn into a more challenging and motivating tasks such as games. Besides, robots can provide a quantitative measure of the rehabilitation progress. This article overviews the terms used in robot-aided upper-limb rehabilitation. It continues by investigating the requirements for rehabilitation robots. Then the most outstanding works in robot-aided upper-limb rehabilitation and their control schemes have been investigated. The clinical outcomes of the built robots are also given that demonstrates the usability of these robots in real-life applications and their acceptance. This article summarizes a review done along with a research on the design, simulation and control of a robot for use in upper-limb rehabilitation after stroke. Implications for Rehabilitation Reviewing common terms in rehabilitation of upper limb using robots Reviewing rehabilitation robots built up to date Reviewing clinical outcomes of the mentioned rehabilitation robots.

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This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm's generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

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