Abstract
Genetic distributed fuzzy (GDF) controllers are proposed for multi-part-type production line. These production systems can produce more than one part type. For these systems, "production rate" and "priority of production" for each part type is determined by production controllers. The GDF controllers have already been applied to single-part-type production systems. The methodology is illustrated and evaluated using a two-part-type production line. For these controllers, genetic algorithm (GA) is used to tune the membership functions (MFs) of GDF. The objective function of the GDF controllers minimizes the surplus level in production line. The results show that GDF controllers can improve the performance of production systems. GDF controllers show their abilities in reducing the backlog level. In production systems in which the backlog has a high penalty or is not allowed, the implementation of GDF controllers is advisable. © World Scientific Publishing Company.

Abstract
In the previous paper of this series [Vasili et al., 2006. Comparison of different dwell point policies for SP-AS/RS. Int J of Eng and Tech, 3(1):91-106], two reliable travel time models for SP-AS/RS under return to middle and return to start, dwell point policies were developed and presented based on the continuous rack approximation approach. In this paper, the authors present an alternative configuration for the input/output (I/O) station in SPAS/ RS, in order to reduce average handling time of this system. A travel time model is developed for this new configuration and the accuracy of the model is validated by Monte Carlo simulation. The results of new model and previous models are compared and the best policy is introduced. The results show that in a range of shapes (when shape factor is less than or equal to 2), new configuration of SP-AS/RS is more preferable.

Abstract
Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering "what-if' questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main idea is to detect interesting changes in a multidimensional space (MDS). Thus, changes in a set of measures (aggregates) are associated with changes in sector characteristics (dimensions). MDS contains a huge number of cells which poses great challenge for mining gradient cells on a useful time. Dong et al [2] have proposed gradient constraints to smooth the computational costs involved in such queries. Even by using such constraints on large databases, the number of interesting cases to evaluate is still large. In this work, we are interested to explore best cases (Top-K cells) of interesting multidimensional gradients. There several studies on Top-K queries, but preference queries with multidimensional selection were introduced quite recently by Dong et al [9]. Furthermore, traditional Top-K methods work well in presence of convex functions (gradients are non-convex ones). We have revisited iceberg cubing for complex measures, since it is the basis for mining gradient cells. We also propose a gradient-based cubing strategy to evaluate interesting gradient regions in MDS. Thus, the main challenge is to find maximum gradient regions (MGRs) that maximize the task of mining Top-K gradient cells. Our performance study indicates that our strategy is effective on finding the most interesting gradients in multidimensional space.

Abstract
Controlling the flow of materials inside job-shops involves several decisions such as the acceptance or rejection of an incoming order, the order's due date definition, the releasing and the dispatching of the job. This study applies a multiple decision-making scheme involving these four decision phases to examine the sensitivity of job-shop performance to different order release parameters. The performance criteria of shop workload and order delivery were collected to demonstrate the influence of the most significant order release parameters: the queue workload limit and the planning parameter of the latest release date. The influence of each parameter is evaluated by computational simulations. The way we compute the machine workload limit affects not only the workload but also delivery performance measures. However, surprisingly, the latest release date has not a significant impact on shop-floor performance measures. The effect of the queue workload limit in an input-output control. mechanism on delivery and workload related performance measures had not been studied up to date. Neither any analysis had investigated the influence of the latest release date calculus on the performance of the job-shop.

Abstract
The Commission for Coordination and Regional Development of the North of Portugal (CCDR-N) promoted NORTINOV 2015, a project created to define a regional innovation strategy for the North of Portugal based on automotive clusters and Information Technology, Communication and Electronics (ITCE) clusters. Accordingly, it developed a technology forecast for the year 2015 in order to help the North of Portugal cope with the intensifying global competition and rapid technological changes. This paper describes the methodology used to identify critical technologies for the regional economy of the North of Portugal under the project NORTINOV 2015. It involved three main steps: technology identification and study, in which a list of emerging technologies was implemented; refinement of emerging technologies, in which 90 technologies were identified and finally, classification and hierarchisation of technologies, in which 30 technologies were identified as critical for the crafting of regional innovation strategy. In this final step, a workshop involving major stakeholders of the three clusters under study played a major role. Copyright © 2007 Inderscience Enterprises Ltd.

Abstract
This paper reports the main results relative to a project concerning the development of a simulation-based scheduler (System SABE - Sistema de Apoio an Balanceamento e Escalonamento) for a manufacturing system, in a large Portuguese footwear industry. The physical system under consideration is, basically, a flexible flow line (the Transporter) and an automatic warehouse, which supplies the line with boxes containing the mix of components. The implementation of such a system is a response to the need of technological innovation to face new challenges in this fashion industrial sector. The flexibility of the line allows the system to manufacture simultaneously various products in lower quantities, but it also increases the complexity of the line balancing and the automatic selection of the boxes, from the warehouse - therefore the relevance of a software system to support production scheduling in the lines. The simulation model is described and it is explained that it may be used to analyse an ongoing production plan and to effectively support and guide the scheduling of all the operations in the line. The simulation package used is SIMPLE ++. The main results, which can be obtained by the system SABE, are presented. Conclusions are drawn, in particular the practical advantages reported by the footwear industry.