Méthodes entropiques appliqués au problème inverse en magnétoencéphalographie

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Les probabilités de traversée sur les plages de spins identiques pour le modèle d'Ising bidimensionnel

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Client-contractor bargaining on net present value in project scheduling with limited resources

The client-contractor bargaining problem addressed here is in the context of a multi-mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity-on-Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two-party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights

The relation between microstructure and thermoelectric properties in Ta-substituted A-site deficient CaMnO3

Calcium manganite, CaMnO3, is an n-type semiconductor with promising thermoelectric (TE) properties. However, for commercial applications the performance of the material, described by the thermoelectric figure of merit (zT), needs to be enhanced. In this study, Mn is substituted with 2% Ta (donor doping) to significantly increase the electrical conductivity, whereas the overall composition is Ca-deficient to promote exsolution of a secondary phase, resulting in the composition Ca(0.931)Mn(0.98)Ta(0.02)O(3-delta)(CMTO). This nanosized powder was spark plasma sintered (SPS) in its reduced rock-salt phase of CaO-MnO(ss) between 850°C and 1250°C, and then annealed in air at 1100°C to obtain the oxidized phase of CMTO with the secondary phase CaMn2O4 (marokite). Structural and thermoelectric properties of the material were investigated in order to determine correlations between material's microstructure, composition and TE properties. Sintering resulted in dense samples with 98% of the theoretical density even at 850°C, and increasing grain size with increasing sintering temperature. After annealing in air, relative density of 95% was obtain. In addition, significant grain growth was observed in the samples sintered at the lowest temperatures, and estimated grain sizes were between 600 nm and 1.9 µm. In addition to the secondary phase, marokite, a third Ta-rich phase (CaTa2O6) was observed. Increasing amount of marokite resulted in a significant reduction in electrical conductivity, as a result of marokite's insulating behaviour. Consequently, the absolute value of the Seebeck coefficient increased. The secondary phase lowered the thermal conductivity. However, high thermal conductivity was obtained as the grains were large. Nanosized grains enhance the phonon scattering at grain boundaries and a reduction of thermal conductivity with reduced grain size is anticipated. Due to grains in the micrometer range, this effect was not observed. High thermal conductivity in combination with low electrical conductivity resulted in relatively low zT, with highest value of 0.028 at 900°C. From investigated samples, the one sintered at 850°C is most promising, and improved results are achievable if the amount of marokite is reduced and the annealing temperature lowered

Trading bargaining weights

Ervig U, Haake C-J. Trading bargaining weights. Working Papers. Institute of Mathematical Economics. Vol 350. Bielefeld: Universität Bielefeld; 2003

Trading bargaining weights

Ervig U, Haake C-J. Trading bargaining weights. JOURNAL OF MATHEMATICAL ECONOMICS. 2005;41(8):983-993.We consider a model, in which two agents are engaged in two separate bargaining problems. We introduce a notion of bargaining weights (bargaining power), which is basically given by asymmetric versions of the Perles-Maschler bargaining solution. Thereby, we view bargaining power as ordinary goods that can be traded in an exchange economy. With equal initial endowment of bargaining power there exists a Walrasian equilibrium in this exchange economy such that the utility allocation in equilibrium coincides with the Perles-Maschler bargaining solution of the aggregate bargaining problem. Equilibrium prices are given by the primitives of the two bargaining problems. (c) 2005 Elsevier B.V. All rights reserved

Client-contractor bargaining on net present value in project scheduling with limited resources

The client-contractor bargaining problem addressed here is in the context of a multi-mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity-on-Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two-party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights