Transformations structurelles et essor du métal

The role of multiattribute utility theory is first placed in the overall context of decision analysis. Then an approach that has proven useful in adapting the theory to be a practical tool is illustrated. Several cases where multiattribute utility has been used are briefly discussed. These include both operational and strategic problems involving, for example, siting of large-scale facilities (airports, power plants), medical treatment, the structuring corporate objectives, environmental management, and personal investment strategy

Rethinking feasibility analysis for urban development: a multidimensional decision support tool

Large-scale urban development projects featured over the past thirty years have shown some critical issues related to the implementation phase. Con-sequently, the current practice seems oriented toward minimal and wide-spread interventions meant as urban catalyst. This planning practice might solve the problem of limited reliability of large developments’ feasibility studies, but it rises an evaluation demand related to the selection of coali-tion of projects within a multidimensional and multi-stakeholders deci-sion-making context. This study aims to propose a framework for the generation of coalitions of elementary actions in the context of urban regeneration processes and for their evaluation using a Multi Criteria Decision Analysis approach. The proposed evaluation framework supports decision makers in exploring dif-ferent combinations of actions in the context of urban interventions taking into account synergies, i.e. positive or negative effects on the overall per-formance of an alternative linked to the joint realization of specific pairs of actions. The proposed evaluation framework has been tested on a pilot case study dealing with urban regeneration processes in the city of Milan (Italy)

Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences

Setting competitiveness indicators using BSC and ANP

[EN] In this paper a new approach to assess companies' competitiveness performance in an efficient and reliable way is presented. It introduces a rigorous methodology, based on multi-criteria techniques, which seeks to assist managers of companies within a specific industrial sector in providing information about their relative position in order to define improvement action plans. The approach combines the use of the analytic network process (ANP) method with the balanced scorecard (BSC) to achieve competitiveness indicators. The ANP method allows the aggregation of experts judgments on each of the selected indicators used into one company competitiveness index (CCI). To demonstrate the goodness of the methodology, a case study of the plastic sector of Venezuela has been carried out. Three companies have been analysed using the CCI proposed. The participating experts agreed that the methodology is useful and an improvement from current competitiveness measurement techniques. They found the results obtained coherent and the use of resources significantly less than in other methods.Poveda Bautista, R.; Baptista, DC.; García Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research. 50(17):4738-4752. doi:10.1080/00207543.2012.657964S47384752501

Evaluations of Tactics for Automated Negotiations

[[abstract]]Automated negotiation under the infrastructure of e-commerce is becoming an important issue. However, although the communication protocols and frameworks of automated negotiation have been extensively investigated, the corresponding tactics and strategies are still underdeveloped and need to be evaluated further. Based on the negotiation model proposed by Faratin et al., this paper examines the performance of automated negotiation tactics and intends to provide concise suggestions for the users of automated negotiation. First, theoretical analysis is used to evaluate the behavior-dependent tactics. Constructive conclusions are obtained when single-issue negotiations are considered. Next, a new framework for applying single-issue tactics to multi-issue negotiation is proposed. Based on this framework, theoretical analysis is then extended to multi-issue cases. Finally, different from the previous work, exhaustive simulations based on two-issue negotiations are performed to evaluate the effectiveness of behavior-dependent and time-dependent tactics. The experimental results provide several important insights into negotiation tactics.[[booktype]]紙本[[booktype]]電子

Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case

Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives, there is usually no single solution that performs best on all objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto-optimal designs out of a (small) given set of alternatives. We present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget