Abstract
Regular and non-regular production can often be found in multipurpose batch plants, requiring two distinct operating strategies: campaign and short-term production. This paper describes a sequential approach for the simultaneous scheduling of campaign and short-term products in multipurpose batch plants. Campaign products follow a periodic scheduling and are constrained to a monthly demand and a safety stock level. Shortterm products have a non-periodic scheduling and must respect tight delivery time windows. Our integrated model is based on the Resource-Task Network (RTN) representation proposed by Pantelides (1994) and uses a discrete-time formulation.

Abstract
We propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with irreversibility, lumpiness and economies of scale in capacity costs. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented and indicate that the approach is capable of producing high-quality approximations to the efficient sets with a modest computational effort. The best results are achieved with a new hybrid approach, combining Tabu Search and Variable Neighbourhood Search.

Abstract
In this paper we address two major challenges presented by stochastic discrete optimisation problems: the multiobjective nature of the problems, once risk aversion is incorporated, and the frequent difficulties in computing exactly, or even approximately, the objective function. The latter has often been handled with methods involving sample average approximation, where a random sample is generated so that population parameters may be estimated from sample statistics-usually the expected value is estimated from the sample average. We propose the use of multiobjective metaheuristics to deal with these difficulties, and apply a multiobjective local search metaheuristic to both exact and sample approximation versions of a mean-risk static stochastic knapsack problem. Variance and conditional value-at-risk are considered as risk measures. Results of a computational study are presented, that indicate the approach is capable of producing high-quality approximations to the efficient sets, with a modest computational effort.

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
A virtual enterprise (VE) is a temporary organization that pools member enteiprises core competencies and exploits fast changing market opportunities Partner selection can be viewed as a multi-criteria decision making problem that involves, assessing trade-offs between conflicting tangible and intangible criteria, and stating preferences based on incomplete or non-available information. In general, this is a very complex problem due to the large number of alternatives and criteria of different types. In this paper we propose an integrated approach to rank alternative VE configurations using an extension of the TOPSIS method for fuzzy data, improved through the use of a tabu search meta-heuristic. Preliminary computational results clearly demonstrate its potential for practical application.