In the current paper, we present an optimization system solving multi objective production scheduling problems (MOOPPS). The identification of Pareto optimal alternatives or at least a close approximation of them is possible by a set of implemented metaheuristics. Necessary control parameters can easily be adjusted by the decision maker as the whole software is fully menu driven. This allows the comparison of different metaheuristic algorithms for the considered problem instances. Results are visualized by a graphical user interface showing the distribution of solutions in outcome space as well as their corresponding Gantt chart representation. The identification of a most preferred solution from the set of efficient solutions is supported by a module based on the aspiration interactive method (AIM). The decision maker successively defines aspiration levels until a single solution is chosen. After successfully competing in the finals in Ronneby, Sweden, the MOOPPS software has been awarded the European Academic Software Award 2002 (http://www.bth.se/llab/easa_2002.nsf)

The article presents a local search approach for the solution of timetabling problems in general, with a particular implementation for competition track 3 of the International Timetabling Competition 2007 (ITC 2007). The heuristic search procedure is based on Threshold Accepting to overcome local optima. A stochastic neighborhood is proposed and implemented, randomly removing and reassigning events from the current solution. The overall concept has been incrementally obtained from a series of experiments, which we describe in each (sub)section of the paper. In result, we successfully derived a potential candidate solution approach for the finals of track 3 of the ITC 2007.

HE term "bin packing" describes a class of well-known, classical problems with numerous applications in logistics, operations research and related disciplines. From single dimensional to multidimensional problems, various types can be identified in practice. Common to all is the overall task of packing a finite number of n items into a minimum number of bins (knapsacks) subject to a set of practical constraints and requirements. These include given capacities of the bins, but also other considerations such as irregularly shaped bins, load balancing of the bins, etc. Numerous approaches including exact, heuristic, and metaheuristic algorithms have been proposed for the resolution of bin packing problems, and a rich literature on packing problems exists, with important classifications by D

The article presents an approach to interactively solve multi-objective optimization problems. While the identification of efficient solutions is supported by computational intelligence techniques on the basis of local search, the search is directed by partial preference information obtained from the decision maker. An application of the approach to biobjective portfolio optimization, modeled as the well-known knapsack problem, is reported, and experimental results are reported for benchmark instances taken from the literature. In brief, we obtain encouraging results that show the applicability of the approach to the described problem.

The tests have been performed on 50 problem instances generated based on the job characteristics of the practical case in the Sherwood Press, and the results have been averaged. Results Conducting significance tests at a level of 0.99, it has been possible to obser ve that for the single criterion formulation EX leads in 90% of the instances to better results than BSH, which is found to be better than FSH. The results are stable over time, thus the altering of the weights does not lead to significantly different results. The comparison of the neighbourhood search on the bicriteria extension of the problem with the results of the single criterion version reveals an interesting pattern, shown in Figure 1. After approximately 1,000 initial iterations, the single criterion search is able to significantly outperform the bicriteria formulation in more problem instances than vice versa. However, this effect is reversed after the local search approaches have been allowed more evaluations. While the monocriterion hillclimber tends to get stuck in local optima, its bicriterion counterpart successfully overcomes local optimality, leading to significantly better results. As Figure 1 reveals, this effect is each time repeated after changing the weight settings. While the neighbourhood search for the single criterion problem allows comparably fast improvements, its advantage over the multi criterion extension is decreasing over time, and the results are reversed given the necessary amount of iterations.

The article presents a framework for the resolution of rich vehicle routing problems which are difficult to address with standard optimization techniques. We use local search on the basis on variable neighborhood search for the construction of the solutions, but embed the techniques in a flexible framework that allows the consideration of complex side constraints of the problem such as time windows, multiple depots, heterogeneous fleets, and, in particular, multiple optimization criteria. In order to identify a compromise alternative that meets the requirements of the decision maker, an interactive procedure is integrated in the resolution of the problem, allowing the modification of the preference information articulated by the decision maker. The framework is prototypically implemented in a computer system. First results of test runs on multiple depot vehicle routing problems with time windows are reported.

We model anomaly and change in data by embedding the data in an ultrametric space. Taking our initial data as cross-tabulation counts (or other input data formats), Correspondence Analysis allows us to endow the information space with a Euclidean metric. We then model anomaly or change by an induced ultrametric. The induced ultrametric that we are particularly interested in takes a sequential - e.g. temporal - ordering of the data into account. We apply this work to the flow of narrative expressed in the film script of the Casablanca movie; and to the evolution between 1988 and 2004 of the Colombian social conflict and violence.

These group interactions can appear to an outside observer to mimic the intelligence of a single entity. In social sciences some prominent theorists have discussed emergence - perhaps without even realising it. In all events, many social phenomena can be modeled using emergence. This paper discusses emergence as a tool of political analysis, examines existing political simulations, and proposes a simple simulation using an agent model to determine emergent characteristics of a political system. The paper is divided into three sections: First, a description of emergence in social science.

The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.

The paper describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its' simplicity as it does require the setting of only very few parameters. The implementation of the Pareto Iterated Local Search metaheuristic is based on the MOOPPS computer system of local search heuristics for multi-objective scheduling which has been awarded the European Academic Software Award 2002 in Ronneby, Sweden (http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf)