Abstract
Work flows in a job-shop are determined not only by the release load but also by the number of accepted orders. In this paper the common assumption of accepting all incoming orders regardless of shop condition is relaxed. Instead of placing the orders in a 'pre-shop pool' queue, as in previous research, orders that arrive at the shop, when it is highly congested, may be immediately rejected or their due dates may be negotiated. This paper explores the idea of controlling the workload since the acceptance/rejection stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. The effect of the negotiation phase on the job-shop performance is evaluated using a simulation model of a generic random job-shop that allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. Different tolerances of the workload limit slightly affect the performance of the job-shop.

Abstract
Partner selection in virtual enterprises (VE) can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria. In general, this is a very complex problem due to the dynamic topology of the network, the large number of alternatives and the different types of criteria. In this paper we propose an exploratory process to help the decision-maker obtain knowledge about the network in order to identify the criteria and the companies that best suit the needs of each particular project. This process involves a multi-objective tabu search metaheuristic designed to find a good approximation of the Pareto front, and a fuzzy TOPSIS algorithm to rank the alternative VE configurations. In the exploratory phase we apply clustering analysis to confine the search according to the decision-maker beliefs, and case base reasoning, an artificial intelligence approach, to totally or partially construct VEs by reusing past experiences. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Abstract
In this paper, we present greedy randomised dispatching heuristics for the single-machine scheduling problem with quadratic earliness and tardiness costs and no machine idle time. The several heuristic versions differ, on the one hand, on the strategies involved in the construction of the greedy randomised schedules. On the other hand, these versions also differ on whether they employ only a final improvement step or perform a local search after each greedy randomised construction. The proposed heuristics were compared with existing procedures as well as with optimum solutions for some instance sizes. The computational results show that the proposed procedures clearly outperform their underlying dispatching heuristic, and the best of these procedures provide results that are quite close to the optimum. The best of the proposed algorithms is the new recommended heuristic for large instances as well as a suitable alternative to the best existing procedure for the larger of the middle-sized instances.

Abstract
A virtual enterprise (VE) is a temporary organization that pools the core competencies of its member enterprises and exploits fast changing market opportunities. The success of such an organization is strongly dependent on its composition, and the selection of partners becomes therefore a crucial issue. This problem is particularly difficult because of the uncertainties related to information, market dynamics, customer expectations and technology speed up. In this paper we propose an integrated approach to rank alternative VE configurations in business environments with uncertainty, using an extension of the TOPSIS method for fuzzy data, improved through the use of a stochastic multiobjective tabu search meta-heuristic. Preliminary computational results clearly demonstrate the potential of this approach for practical application.

Abstract
This paper presents an innovative approach to support the definition of strategies for the design of alternative configurations of hospital supply chains. This approach was developed around a hybrid Tabu Search / Variable Neighbourhood Search metaheuristic, that uses several neighbourhood structures. The flexibility of the procedure allows its application to supply chains with different topologies and atypical cost characteristics. A preliminary computational experience shows the approach potential in solving large scale supply chain configuration problems. The future incorporation of this approach in a broader Decision Support System (DSS) will provide a tool that can significantly contribute to an increase of healthcare supply chains efficiency and encourage the establishment of collaborative partnerships between their members.

Abstract