Not available

The process of multi-objective optimization involves finding optimal solutions to several objective functions. However, these are typically in conflict with each other in many real-world problems, such as production system design. Advanced post-optimization analysis can be used to provide the decision maker with information about the underlying system. The analysis can be based on the combination of simulation-based multi-objective optimization and learning from the obtained solution set. The goal of the analysis is to gain a deeper understanding of the problem at hand, to systematically explore and evaluate different alternatives, and to generate essential information and knowledge to support the decision maker to make more informed decisions in order to optimize the performance of the production system as a whole. The aim of this work is to explore the possibilities on how post-optimization analysis can be used in order to provide the decision maker with essential information about an underlying system and in what way this information can be presented. The analysis is mainly done on production system development problems, but may also be transferred to other application areas. The research process of the thesis has been iterative, and the initial approach for post-optimization analysis has been refined several times. The distance-based approach developed in the thesis is used to allow the extraction of information about the characteristics close to a user-defined reference point. The extracted rules are presented to the decision maker both visually, by mapping the rules to the objective space, and textually. The method has been applied to several industrial cases for proof-by-demonstration as well as to an artificial case with information known beforehand to verify the distance-based approach, and the extracted rules have also been used to limit the search space in the optimization. The major finding in the thesis is that to learn from optimization solution sets of production system problems with stochastic behavior, a distance-based approach is advantageous compared with a binary classification of optimal vs. non-optimal solutions. / <p>At the time of the doctoral defence the following articles were unpublished and had a status as follows: Paper 5: Epubl ahead of print; Paper 6: Accepted.</p>

published_or_final_version / Computer Science / Master / Master of Philosophy

No description available.

In this dissertation, we explore the issues related to opportunistic and cooperative communications in a multiuser environment. In the first part of the dissertation, we consider opportunistic scheduling for delay limited systems. Multiuser communication over fading channels is a challenging problem due to fast varying channel conditions. On the other hand, it provides opportunities to exploit the varying nature of the channel and maximize the throughput by scheduling the user (or users) with good channel. This gain is termed as multiuser diversity. The larger the number of users, the greater is the multiuser diversity gain. However, there is an inherent scheduling delay in exploiting multiuser diversity. The objective of this work is to design the scheduling schemes which use multiuser diversity to minimize the system transmit energy. We analyze the schemes in large system limit and characterize the energy--delay tradeoff. We show that delay tolerance in data transmission helps us to exploit multiuser diversity and results in an energy efficient use of the system resources. We assume a general multiuser environment but the proposed scheduling schemes are specifically suitable for the wireless sensor network applications where saving of transmit energyat the cost of delay in transmission is extremely useful to increase the life of battery for the sensor node. In the first part of the thesis, we propose scheduling schemes withthe objective of minimizing transmit energy for a given fixed tolerable transmission delay. The fixed delay is termed as hard deadline. A group of users with channels better than a transmission threshold are scheduled for transmission simultaneously using superposition coding. The transmission thresholds depend onthe fading statistics of the underlying channel and hard deadline of the data to be scheduled. As deadline is approached, the thresholds decrease monotonically to reflect the scheduling priority for theuser. We analyze the proposed schedulers in the large system limit. We compute the optimized transmission thresholds for the proposed scheduling schemes. We analyze the proposed schemes for practically relevant scenarios when the randomly arriving packets have individual, non--identical deadlines. We analyze the case when loss tolerance of the application is exploited to further decrease the system energy. The transmitted energy is not a convex function oftransmission thresholds. Therefore, we propose heuristic optimization procedures to compute the transmission thresholds and evaluate the performance of the schemes. Finally, we study the effect of outer cell interference on the proposed scheduling schemes. The second part of the thesis investigates the problem of cooperative communication between the nodes which relay the data of other sources multiplex with their own data towards a common destination, i.e. a relay node performs as a relay and data source at the same time. This problem setting is very useful in case of some wireless sensor network (WSN) applications where all the nodes relay sensed data towards a common destination sink node. The capacity region of a relay region is still an open problem. We use deterministic network model to study the problem. We characterizethe capacity region for a cooperative deterministic network with single source, multiple relays and single destination. We also characterize the capacity region when communicating nodes have correlated information to be sent to the destination. / Cross Layer Optimization of Wireless Sensor Networks

No description available.

No description available.

No description available.

No description available.

No description available.