Sheik Meeran

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 multi-level hybrid framework applied to the general flow-shop scheduling problem

Abstract Despite the large amount of research conducted in flow-shop scheduling most of it has concentrated on the permutation problem in which passing is not allowed, i.e. a job cannot pass (overtake) another job while waiting in a queue to be processed by a machine. In this work the general flow-shop problem, in which passing is allowed, is dealt with as it is considered to be a better representation of flow-shop instances. The evolutionary techniques of scatter search (SS) and its generalised form, path relinking (PR) are applied to this problem as they are able to provide a wide exploration of the search space and they can be integrated with intelligent search methods such as tabu search. The SS and PR strategies are embedded within a core and shell framework. Initiated from a powerful starting solution, the core and shells iteratively search the solution space to find the best possible solutions. The core consists of a highly constrained neighbourhood, estimation strategies and a dynamic tabu tenure which provide efficiency and effectiveness during various improving and dis-improving phases of the search. Several shell strategies are superimposed on to the core in order to provide the necessary mixture of intensification and diversification. This framework is able to provide substantially better results than the tabu search approach of Nowicki and Smutnicki (Management Science, 42 (6) (1996b) 797–813). The proposed framework is able to achieve an average deviation from optimum of 8.475% while equalling 53 best solutions and finding 42 new best solutions on a suite of 202 benchmark problems. Scope and purpose This paper explores the efficient and effective interaction of intensifying and diversifying strategies in search techniques within the context of the general flow-shop-scheduling problem. In this work we aimed to create a multi-level hybrid system that is able to provide a better solution to the flow-shop-scheduling problem than the existing methods. The techniques of scatter search and path relinking along with tabu search and evolutionary algorithms provided a unifying environment for us to find new solutions. We have also demonstrated that by providing an intelligent search of the solution space some of the current barriers could be overcome.

Predicting the Seam Strength of Notched Webbings for Parachute Assemblies Using the Taguchi's Design of Experiment and Artificial Neural Networks

Webbings are used in parachute assemblies as reinforcing units for the strength they provide. The strength of these seams is an important characteristic which has a substantial influence on the mechanical property of the parachute assemblies. It is well established that factors such as fabric width, folding length of joint, seam design and seam type will all have an impact on seam strength. In this work, the effect of these factors on seam strength was studied using both Taguchis design of experiment (TDOE) as well as an artificial neural network (ANN). In TDOE, two levels were chosen for the factors mentioned above. An L8 design was adopted and an orthogonal array was generated. The contribution of each factor to seam strength was analyzed using analysis of variance (ANOVA) and signal to noise ratio methods. From the analysis it was found that the fabric width, folding length of joint and interaction between the folding length of joint and the seam design affected seam strength significantly. Further, using TDOE, an optimal configuration of levels of factors was found. In order to contrast and compare the results from TDOE, an ANN was also used to predict seam strength using the above mentioned factors as inputs. The prediction from TDOE and ANN methodologies were compared with physical seam strength. It was established from these comparisons, in which the root mean square error was used as an accuracy measure, that the predictions by ANN were better in accuracy than those predicted by TDOE.

Recognition of simple and complex interacting non-orthogonal features

Abstract Feature recognition systems that deal with non-orthogonal features are seldom reported. This paper addresses this gap in the research by presenting a neural network-based feature recognition system to deal with non-orthogonal interacting features. The system accesses the B-rep data of a solid model, and searches for the feature volumes, using a cross-sectional layer method. The volumes are then transformed into 2D patterns of edges and vertices, using the conventions of ‘crosses and dots’ and ‘solid and dashed lines’. These feature patterns are later translated into input matrices for the recognition by a multilayer feedforward neural network.

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.

When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

Forecasting the sales or market share of new products is a major challenge as there is little or no sales history with which to estimate levels and trends. Choice-based conjoint (CBC) is one of the most common approaches used to forecast new products’ sales. However, the accuracy of forecasts based on CBC models may be reduced when consumers’ preferences for the attributes of products are labile. Despite this, there is a lack of research on the extent to which lability can impair accuracy when the coefficients estimated in CBC models are assumed to be constant over time. This paper aims to address this research gap by investigating the prevalence of lability for consumer durable products and its potential impact on the accuracy of forecasts. There are reasons to expect that lability may be particularly evident where a product is subject to rapid technological change and has a short product life-cycle. We carried out a longitudinal survey of the preferences of 161 potential consumers relating to four different types of products. We established that for both functional and innovative products: (i) the CBC models vary significantly over time, indicating changes in consumer preferences and (ii) such changes may cause large differences in forecasts of the probabilities that consumers will purchase particular brands of products. Hence employing models where coefficients do not change over time can potentially lead to inaccurate market share forecasts for high-tech, short life-cycle products that are launched even a short time after the choice-based modelling has been conducted.

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.

Student Performance-University Preference Model: A Framework for Helping Students Choose the Right A-Level Subjects.

Every year, many students in the UK fail to achieve a place at their preferred university because they take the wrong A‐level subjects. This study aims to suggest a framework for helping students choose the right subjects. Data on student achievement in A‐level examinations were obtained from a UK sixth form college over a four‐year period. Statistical techniques were employed to support our hypothesis that a student’s choice of A‐level subjects should be based on both the student’s ability and a university’s preference for particular subjects and grades. Despite the limitation of small sample size, a model has been created that will maximise a student’s chance of achieving a place at his/her university of choice. The model presented could easily be extended in future to incorporate more levels in each of the attributes considered, and in this way it could provide the optimal choice of subjects for each individual student given his/her particular aspirations.

Recognizing features from orthographic images using neural networks: a framework for CADCAM and AVI integration

In order to satisfy the current market demands for shorter lead-time and high quality products manufacturing enterprises have to integrate their design, production and quality assurance functions. In a computerized environment these functions are manifested in CAD, CAM and AVI respectively. In the recent past, much success has been achieved in integrating CAD and CAM. It is widely accepted that feature recognition systems, both 2D and 3D, have been one of the main contributors to CADCAM integration. However the existing feature recognition systems could not link CADCAM and AVI and hence could not close the manufacturing loop that ensures the production of designer intended features. This is mainly because the input formats of existing feature recognition systems and AVI systems have been different in format and structure. The system described in this paper attempts to redress this deficit at least partially by using a monochrome bitmap as a generic input, to which both CAD models and vision images could be converted. Hence the input to the system takes a form of third angle orthographic views, however, without hidden lines in order to facilitate dealing with vision images. This, in turn, augments difficulty faced in recognizing features. To overcome this difficulty, the help of evolutionary computing and artificial intelligence is sought in this system. This paper outlines the basis of a 2D feature recognition system that uses artificial neural networks along with a chain code method for eliciting feature information from monochrome bitmap of either vision images or CAD inputs hence providing a generic framework to integrate CADCAM and AVI. With further improvement to deal with geometry of the features, annotations and symbols this system could also help to salvage the massive store of engineering knowledge that exists in 2D form.