Evaluation of a hybrid genetic tabu search framework on job shop scheduling benchmark problems

It has been well established that to find an optimal or near-optimal solution to job shop scheduling problems (JSSPs), which are NP-hard, one needs to harness different features of many techniques, such as genetic algorithms (GAs) and tabu search (TS). In this paper, we report usage of such a framework which exploits the diversified global search and the intensified local search capabilities of GA and TS, respectively. The system takes its input directly from the process information in contrast to having a problem-specific input format, making it versatile in dealing with different JSSP. This framework has been successfully implemented to solve industrial JSSPs. In this paper, we evaluate its suitability by applying it on a set of well-known job shop benchmark problems. The results have been variable. The system did find optimal solutions for moderately hard benchmark problems (40 out of 43 problems tested). This performance is similar to, and in some cases better than, comparable systems, which also establishes the versatility of the system. However for the harder benchmark problems it had difficulty in finding a new improved solution. We analyse the possible reasons for such a performance.     

Titanium dichalcogenide-decorated reduced graphene oxide nanocomposite for high-performance photovoltaic cell fabrication

Journal of Materials Science: Materials in Electronics - This study describes the flexible DSSC devices based on efficient TiS2/Reduced Graphene Oxide (TiS2/RGO) hybrid photoanodes that have been...     

Recognizing features from engineering drawings without using hidden lines: A framework to link feature recognition and inspection systems

Many two-dimensional (2D) feature recognition systems have recently been developed to salvage the massive store of engineering knowledge in 2D form and bring the benefit of computer-aided design (CAD)-computer-aided manufacturing (CAM) integration to 2D CAD users. However, the significant amount of effort spent in developing these systems has little use in the related field of 'Automated Visual Inspection' (AVI). This is because there are some differences in the formats of input to feature recognition and AVI systems with respect to hidden feature information. This paper presents a method to extract feature information from 2D drawings without using hidden lines. This is an initial step towards establishing a common input format between AVI and feature recognition systems, and in turn a common framework to link them. The absence of hidden edges in photographic and video images, which are used as input to the AVI systems, is mimicked by not using hidden lines, although they are normally available in any drawing. However, the lack of hidden line information is compensated for by isometric projections, which are of the same format as that of the photographic input to AVI systems. The ability to recognize features from orthographic projections coupled with isometric views but without hidden lines will be directly transferable to AVI systems, which will have a substantial impact on automatic inspection, reverse modelling and manufacturing automation and, in turn, on manufacturing productivity.