Abstract
This paper describes an application of genetic algorithms to the bus driver scheduling problem. The application of genetic algorithms extends the traditional approach of Set Covering/Set Partitioning formulations, allowing the simultaneous consideration of several complex criteria. The genetic algorithm is integrated in a DSS but can be used as a very interactive tool or a stand-alone application. It incorporates the user's knowledge in a quite natural way and produces solutions that are almost directly implemented by the transport companies in their operational planning processes. Computational results with airline and bus crew scheduling problems from real world companies are presented and discussed.

Abstract
Traditional Production Planning and Control systems do not successfully deal with new organisational forms of manufacturing, like production "islands", product oriented or customer driven production. These current trends lead in practice to a strong decentralisation of production management tasks and to an "object oriented structuring" of the manufacturing process. Planning and operation in this global environment requires new skills and new approaches, namely, the co-ordination of global networks of manufacturing units and of large complex supply chains. In this paper, we present a decentralised information system designed to address the tasks of production planning that result from sales orders, originated in customers located anywhere in the world, and accomplished through a distributed manufacturing network. The system addresses the requirements of a make-to-order environment and is hopefully able to produce realistic satisfactory delivery dates. The information infrastructure designed and implemented using distributed object-oriented technology with a component based architecture has proven to be efficient and powerful, satisfying all the major tight requirements of information systems in an environment of distributed manufacturing.

Abstract
Although single objective metaheuristics are widely spread and applied in many combinatorial optimisation problems, only very recently have multiobjective metaheuristics (MOMH) been designed and used in practice. They aim at obtaining good approximations of the set of nondominated solutions of a problem, in an efficient way. In this work, we have applied multiobjective versions of simulated annealing and taboo search to the resource constrained project scheduling problem (RCPSP), in order to minimise the makespan, the "weighted" lateness of activities and the violation of resource constraints. Computational experience performed on randomly generated instances shows that this general approach is flexible, effective and able to deal with multiple objectives and with variations in the problem structure.

Abstract
This paper describes a study of planning vehicle routes for the collection of urban recyclable waste. The aim is to create collection routes for every day of the month, to be repeated every month, minimizing the operation cost. Two important features of the problem are the planning of a relatively long period of time and the separate collection of three types of waste. The collection operation was modelled in accordance to the practice of the company that manages the collection system. Heuristic techniques were developed to solve the model in three phases: definition of the geographic zones served by the vehicles, definition of the waste type to collect on each day of the month, definition of the collection routes. Preliminary results suggest that significant economies in collection costs are possible.

Abstract
This paper applies network analysis to study the evolution of aviation networks. It takes a different approach from previous research that usually only explores airline networks. The aviation network of the airports of Lisbon, Faro and Porto, is modelled using the supply of seats and the passenger demand between 2001 and 2010. This analysis is complemented with a study of the commercial evolution of the three airports. It is noticeable the impact of low-cost carriers in the evolution of the configuration of the network over time. A de-concentration effect is also shown to occur, due to the interaction between airport and airline decisions that favour network development. The results of the analysis highlight how critical it has become for airport managers to assess and satisfy the real requirements of the different types of airlines, in order to reduce uncertainty and increase traffic.

Abstract
In this paper, we propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with process flexibility, irreversibility, lumpiness and economies of scale in capacity costs. In each period, discrete decisions concerning the investment in capacity expansion, and continuous decisions concerning the utilization of the available capacity to satisfy demand are considered. We solve the capacity utilization problems with linear programming, in order to find the minimum capacity for each resource with the other resources remaining unchanged, this way providing information on the feasibility of the discrete investment decisions. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented, that show the approach is capable of obtaining high-quality approximations to the efficient sets, with a modest computational effort.