Optimum path planning using convex hull and local search heuristic algorithms

Whether in improving quality or productivity the impact of mechatronic systems such as robots in industry is unquestionable. One aspect of interest in robotics is planning the optimum path for a mobile robot or the optimum trajectory for link movements of a stationary robot in order to increase their efficiency. However, for a given set of points complete enumeration of all the possible paths to establish an optimal one is not feasible as the search space increases exponentially (explodes combinatorially) as the number of points increases. This problem, traditionally known as the “Traveling Salesman Problem” (TSP) has attracted a great deal of attention for a long time. Proven enumerative techniques such as “nearest neighbour algorithm”, “branch and bound”, “cutting planes”, and “dynamic programming” as well as approximation methods such as “tabu search”, “greedy algorithm”, “simulated annealing” and “genetic algorithm”, have had only a limited success in solving this problem. Recently “convex hull”, a minimum area and perimeter shape, has been used as an initial sub-tour along with enumerative techniques such as minimising insertion costs to solve the TSP problem. We present a system which uses heuristic rules to augment the convex hull initial sub-tour created by the Graham scan algorithm. The system is able to provide a solution in a polynomial time.     

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

In recent decades many attempts have been made at the solution of Job Shop Scheduling Problem using a varied range of tools and techniques such as Branch and Bound at one end of the spectrum and Heuristics at the other end. However, the literature reviews suggest that none of these techniques are sufficient on their own to solve this stubborn NP-hard problem. Hence, it is postulated that a suitable solution method will have to exploit the key features of several strategies. We present here one such solution method incorporating Genetic Algorithm and Tabu Search. The rationale behind using such a hybrid method as in the case of other systems which use GA and TS is to combine the diversified global search and intensified local search capabilities of GA and TS respectively. The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems. This, the system achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions. These features combined with the hybrid strategy employed enabled the system to solve several benchmark problems optimally, which has been discussed elsewhere in Meeran and Morshed (8th Asia Pacific industrial engineering and management science conference, Kaohsiung, Taiwan, 2007). In this paper we bring out the system’s practical usage aspect and demonstrate that the system is equally capable of solving real life Job Shop problems.     

A generic approach to recognising isolated, nested and interacting features from 2D drawings

Many useful three-dimensional (3D)-feature recognition systems have been developed over the past few decades. However there is a shortage of feature recognition systems in the two-dimensional (2D) domain. This has an adverse effect on the realisation of CADCAM benefits in Small and Medium Enterprises. Here is an attempt to redress this deficit. This paper presents a hint-based feature recognition system which recognises machining features from 2D orthographic projections through a two-stage process of profile searching and feature completion. Three types of profiles: Type I, II or III are defined in this system. Together they are sufficient to describe most profiles, if not all, in any 2D drawing. Profile searching identifies these profiles while feature completion establishes the cavity emanating from these profiles using a multi-level top–down approach. This process enables the system to deal with the isolated, nested and interacting features using a common framework. The nested features are handled by applying the same reasoning used for isolated features recursively while interacting features are decomposed automatically by the process of profile searching into their constituent isolated features.     

Job-shop scheduling using neural networks

Complete enumeration of all sequences to establish global optimality is not feasible as the search space; for a general job-shop scheduling problem,PiG has an upper bound of (n!)m. Since the early fifties a great deal of research attention has been focused on solving PiG , resulting in a wide variety of approaches such as branch and bound, simulated annealing, tabu search, etc. However, limited success has been achieved by these methods due to the shear intractability of this generic scheduling problem. Recently, much effort has been concentrated on using neural networks to solve PiG as they are capable of adapting to new environments with little human intervention and can mimic thought processes. Major contributions in solving PiG using a Hopfield neural network, as well as applications of back-error propagation to general scheduling problems are presented. To overcome the deficiencies in these applications a modified back-error propagation model, a simple yet powerful architecture which can be successfully simulated on a personal computer, is applied to solve PiG.     

An intelligent model to predict the life condition of crude oil pipelines using artificial neural networks

Neural Computing and Applications - Inefficient scheduling of a pipeline system may lead to severe degradation and substantial economic losses. Earlier studies mostly focussed on corrosion and...     

Feature patterns in recognizing non-interacting and interacting primitive,circular and slanting features using a neural network

Many varied techniques have long been suggested for the recognition of features from solid modellers, and the systems which have incorporated these techniques have achieved a moderate success. However the problem of recognition of the wide variety of features, e.g. interacting and non-interacting primitive, circular and slanting features, that any real life component may have, requires complex systems which are inflexible and hence limited in their use. Here, we present a simple and flexible system in which the features are defined as patterns of edges and vertices to deal with all the above types of features. The system starts by searching a B-rep solid model, using a cross-sectional layer method, for volumes which can be considered to represent features. Once the volumes are detected, their edges and vertices are processed and arranged into feature patterns which are used as input for a neural network to recognize the features. Simple conventions used in this work enable the creation of feature patterns for primitive, circular and slanting features. Learning, generalizing and tolerating incomplete data are some of the neural network's attributes exploited in this work to deal with interacting and non-interacting features.     

An active tool-tracking system for increased productivity

Keeping track of tools is essential for smooth running of any production unit. Being one of the critical resources for production, non-availability of tools can affect productivity of any manufacturing shop floor seriously. In this paper, a novel tool-tracking system, which can be used to monitor the movements of tools in a shop floor to prevent tool loss and tool hoarding, is described. The system uses miniature transmitters embedded in the tools together with a number of receiver stations located strategically around the shop floor. Each tool intermittently transmits a coded signal so that it may be identified as well as located. Simulation studies show that the system is effective particularly in situations where the productivity would have been low because of high tool losses.     

Repeated intracerebroventricular administration of glucagon-like peptide-1-(7-36) amide or exendin-(9-39) alters body weight in the rat

Central nervous system glucagon-like peptide-1-(7-36) amide (GLP-1) administration has been reported to acutely reduce food intake in the rat. We here report that repeated intracerebroventricular (i.c.v.) injection of GLP-1 or the GLP-1 receptor antagonist, exendin-(9-39), affects food intake and body weight. Daily i.c.v. injection of 3 nmol GLP-1 to schedule-fed rats for 6 days caused a reduction in food intake and a decrease in body weight of 16 +/- 5 g (P < 0.02 compared with saline-injected controls). Daily i.c.v. administration of 30 nmol exendin-(9-39) to schedule-fed rats for 3 days caused an increase in food intake and increased body weight by 7 +/- 2 g (P < 0.02 compared with saline-injected controls). Twice daily i.c.v. injections of 30 nmol exendin-(9-39) with 2.4 nmol neuropeptide Y to ad libitum-fed rats for 8 days increased food intake and increased body weight by 28 +/- 4 g compared with 14 +/- 3 g in neuropeptide Y-injected controls (P < 0.02). There was no evidence of tachyphylaxis in response to i.c.v. GLP-1 or exendin-(9-39). GLP-1 may thus be involved in the regulation of body weight in the rat.     

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...