Abstract

Abstract
Customers are known as a brilliant source of knowledge for the companies, because they gain knowledge and expertise while selecting and using products or services. Customer knowledge management is a new stage of relationship management between organizations and the customers. Most of the models in the literature are focused on human resources to set up a framework to exchange knowledge with the customers. In this paper, the applicability of agent-based systems to the customer knowledge management was investigated. As a feasibility study, characteristics of the agents and their role in knowledge management systems were reviewed in advance. Then, the requirements of customer knowledge management systems were described. Finally, using an introductory model, the applicability of the intelligent agents in customer knowledge management systems were shown and discussed.

Abstract
In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose a genetic approach based on a random key alphabet and present several algorithms based on this approach. These versions differ on the generation of both the initial population and the individuals added in the migration step, as well as on the use of local search. The proposed procedures are compared with the best existing heuristics, as well as with optimal solutions for the smaller instance sizes. The computational results show that the proposed algorithms clearly outperform the existing procedures and are quite close to the optimum. The improvement over the existing heuristics increases with both the difficulty and the size of the instances. The performance of the proposed genetic approach is improved by the initialization of the initial population, the generation of greedy randomized solutions, and the addition of the local search procedure. Indeed, the more sophisticated versions can obtain similar or better solutions and are much faster. The genetic version that incorporates all the considered features is the new heuristic of choice for small and medium size instances.

Abstract
The process of learning is emphasised in all organisations as a way to improve performance, to more effectively manage projects, processes, and activities, and thus, it is of extreme importance to the business in general, because it enables sustainable competitive advantage. However, organisations do not seem to learn from their mistakes, rarely exploring the reasons for their projects' success or failure, and very rarely applying those lessons learned to the business management. In fact, there is little or no point in learning unless management adapts its behavior accordingly. Our proposal is aimed at creating an organisational framework for managing the knowledge produced, as well as incorporating the lessons learned in the daily organisational routines. The purpose is to better respond to the problems that may occur and therefore, strategically realign the organisation towards efficiency and results improvement. © 2012, IGI Global.

Abstract
In the present paper, management strategies are analysed in order to evaluate the degree of entrepreneurship in firms' management by the use of innovation, risk and proactivity strategies. Since we are dealing with management strategies, it is possible to relate them to the concept of Intrapreneurship. This study was done in a region of northern (Portugal Vale do Sousa) and focus on Industrial and Construction sectors. The region is composed of six concelhos1 in some of which it is possible to identify some industrial districts. In order to get a valid sample, a group of 251 firms were analysed. Each strategy was analysed individually and the results pointed to a lack of culture of entrepreneurship in firms' management. Only Proactivity presented a positive result in firms' management. When grouping the results, it was possible to conclude that the degree of intrapreneurship is very low and firms are surviving (even succeeding) by following conventional (old fashioned) management strategies.

Abstract
The three-dimensional bin packing problem consists of packing a set of boxes into the minimum number of bins. In this paper we propose a new GRASP algorithm for solving three-dimensional bin packing problems which can also be directly applied to the two-dimensional case. The constructive phase is based on a maximal-space heuristic developed for the container loading problem. In the improvement phase, several new moves are designed and combined in a VND structure. The resulting hybrid GRASP/VND algorithm is simple and quite fast and the extensive computational results on test instances from the literature show that the quality of the solutions is equal to or better than that obtained by the best existing heuristic procedures.