Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Friction welding is one of the foremost welding processes for similar and dissimilar metals. Previously, the process has been modeled utilizing the rudimentary techniques of constant friction and slip-stick friction. The motivation behind this article is to present a new characteristic for temperature profile estimation in modeling of the rotary friction welding process. For the first time, a unified model has been exhibited, with an implementation of the phase transformation of similar and dissimilar materials. The model was generated on COMSOL Multiphysics^® and thermal and structural modules were used to plot the temperature curve. The curve for the welding of dissimilar metals using the model was generated, compared and analyzed with that of practical curves already acquired through experimentation available in the literature, and then the effect of varying the parameters on the welding of similar metals was also studied.

Multi-source information fusion diagnosis is usually more reliable than fault diagnosis with a single source employed. However, fusion results may be absurd when fusing highly conflicting information. To address this problem, the Dempster–Shafer (DS) evidence theory is updated by weighting each piece of evidence according to the corresponding contribution to the decision, and a novel fault diagnosis method based on multi-source conflict information fusion is proposed. First, the basic probability assignment of evidence corresponding to the sensor information is given by introducing the feature parameters of electromyographic signals and using the back-propagation neural network. Then, the importance of each piece of evidence is determined by solving the difference degree and exclusion degree among the evidence, and the evidence is assigned weights according to the degree of importance of each piece of evidence in the fusion decision-making process. Next, the weighted evidence is combined for making decisions and further diagnosis after weighted averaging of the evidence with different weights. Finally, the performance of the proposed method is assessed using receiver operating characteristic (ROC) curves. The experimental results show that the areas under the ROC curves for the proposed method are 0.3229, 0.0729 and 0.9271 higher than those of the traditional DS method, Murphy’s method and Yager’s method, respectively, which proves that the proposed method has better diagnostic performance and reliability.