Modern mobile devices incorporate several transmit and receive antennas in highly constrained volumes. As miniaturized antennas impinge upon fundamental physical limits on efficiency, new design approaches are required to support ever-smaller devices with more varied and robust communication performance. We take an unconventional design approach in which an arbitrary metallic structure and its components can be modified to act as efficient radiators. Using eigenmode analysis and the theory of characteristic modes (TCM), we develop algorithms that allow for effective integration of antennas with mechanical structures and enable structure reuse, helping meet stringent space and weight constraints without sacrificing electromagnetic performance. We derive TCM-based objectives for effective exploration of the design space in the electromagnetic (EM) domain. The procedure includes a feed placement technique that identifies viable excitation points on the structure without running full EM analysis. In addition to computational advantages, this provides a point of comparison among a variety of antenna shapes. Empirical evaluation shows that the estimates of radiated power from TCM can effectively guide optimization toward structures with improved radiating properties. Automated feed placement increases the proportion of good-quality designs among the explored candidates by consistently selecting the most promising feed positions. The ability of the TCM-based algorithm to direct the search is further validated on two real-world applications: integration of a GPS antenna with the frame of a mobile phone and integration of an S-band antenna with the frame of a small spacecraft. To the best of our knowledge, this is the first work that applies TCM to automated optimization of antennas. We investigate how to leverage domain-specific methods and solution representations in the coupled optimization of antennas. We develop a novel multiobjective optimization framework based on local search in each domain. In this procedure, the local optima in each objective are obtained and modified to create a new population of candidate designs. On a number of benchmark problems, the proposed technique is competitive with leading multi-objective algorithms: while it finds a less uniform distribution along the Pareto front, it shows better performance in locating solutions at the boundaries of the tradeoff curve. The local search algorithm is successfully applied to topology optimization of an antenna for a CubeSat, a small low-cost satellite platform.

This research has been defined in response to the Winnipeg children's hospital (WCH) challenges such as long waiting times, delays and cancellations in surgical flow. Preliminary studies on the surgical flow revealed that definition and implementation of successful process improvement projects (PIPs) along with application of an efficient master surgical schedule (MSS) are efficient solutions to the critical problems in WCH. In the first phase of this work, a process improvement program including three major PIPs, is defined and implemented in WCH in order to improve the efficiency of the processes providing surgical service for patients. In the second phase, two new multi-objective mathematical models are presented to develop efficient MSSs for operating room department (OR) in WCH.

Every production system has its constraints. Ever since Goldratt presented the theory of constraints in the mid 80’s a lot of effort has been made to find the best methods for constraint identification and ways to minimize the constraints in order to gain higher capacity in production. A novel method presented is Simulation-based COnstraint Removal (SCORE). The SCORE method has been proved to be more effective and detailed in the identification and sorting of the constraints when compared with other bottleneck detection methods (Pehrsson 2013). The work in this bachelor’s project has been focused on applying the method to a complex production system in order to further explore the SCORE method’s ability to identify bottlenecks and reveal opportunities to increase the throughput of a production system. NorthStar Battery Company (NSB) wishes to perform a bottleneck analysis and optimization in order to find improvements to increase the throughput with 10%. By using the SCORE method, improvement options with a potential to meet the goals of NSB was identified. It also facilitated for the author to further exploit the possibilities of simulation-based optimization and knowledge extraction through the SCORE method. By building a valid discrete event simulation model of the production line and use it for optimization, followed by a knowledge extraction, it was possible to identify the top three constraints and the level of improvement needed in the constraining operations. The identified improvements could potentially increase the throughput of the production line by 10-15 percent. The project was delimited to exclude the finishing part of the production line and only one battery variant has been included. Through continued work and analysis of the line using the SCORE method it will most likely be possible to even further increase the throughput of the production system and to provide NSB with more knowledge and opportunities to enhance their production effectiveness.

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.

Power converter has played a very important role in modern electric power systems. The control of power converters is necessary to achieve high performance. In this study, a dc-dc buck converter is studied. The parameters of a notional proportional-integral controller are to be selected. Genetic algorithms (GAs), which have been widely used to solve multi-objective optimization problems, is used in order to locate appropriate controller design. The control metrics are specified as phase margin in frequency domain and voltage error in time-domain. GAs presented the optimal tradeoffs between these two objectives. Three candidate control designs are studied in simulation and experimentally. There is some agreement between the experimental results and the simulation results, but there are also some discrepancies due to model error. Overall, the use of multi-domain, multi-objective-optimization-based approach has proven feasible.

Session ID: TH-E1-4 / SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan

2011 IEEE Congress on Evolutionary Computation (CEC). June 5-8, 2011, Ritz-Carlton, New Orleans, LA, USA

No description available.

Liquid biofuels produced from biomass have the potential to partly replace gasoline. One of the most promising biofuels is butanol which is produced in acetone-butanol-ethanol (ABE) fermentation. The ABE fermentation is characterized by its low butanol concentration in the final fermentation broth. In this research, the simulation of three in situ recovery methods, namely, vacuum fermentation, gas stripping and pervaporation, were performed in order to increase the efficiency of the continuous ABE fermentation by decreasing the effect of butanol toxicity. The non-integrated and integrated butanol production systems were simulated and optimized based on a number of objectives such as maximizing the butanol productivity, butanol concentration, and butanol yield. In the optimization of complex industrial processes, where objectives are often conflicting, there exist numerous potentially-optimal solutions which are best obtained using multi-objective optimization (MOO). In this investigation, MOO was used to generate a set of alternative solutions, known as the Pareto domain. The Pareto domain allows to view very clearly the trade-offs existing between the various objective functions. In general, an increase in the butanol productivity resulted in a decrease of butanol yield and sugar conversion. To find the best solution within the Pareto domain, a ranking algorithm (Net Flow Method) was used to rank the solutions based on a set of relative weights and three preference thresholds. Comparing the best optimal solutions in each case study, it was clearly shown that integrating a recovery method with the ABE fermentation significantly increases the overall butanol concentration, butanol productivity, and sugar conversion, whereas butanol yield being microorganism-dependent, remains relatively constant.

Optimizing supply chain is the most successful key for manufacturing systems to be competitive. Supply chain (SC) has gotten intensive research works at all levels: strategic, tactical, and operational levels. These levels, in some researches, have integrated with each other or integrated with other planning issues such as inventory. Optimizing inventory location and level of safety stock at all supply chain partners is essential in high competitive markets to manage uncertain demand and service level. Many works have been developed to optimize the location of safety stock along supply chain, which is important for fast response to fluctuation in demand. However, most of these studies focus on the design stage of a supply chain. Because demand at different horizon times may vary according to different reasons such as the entry of different competitors on market or seasonal demand, safety stock should be optimized accordingly. At the planning (tactical) level, safety stock can be controlled according to each planning horizon to satisfy customer demand at lower cost instead of being fixed by a decision taken at the strategic level. On the other hand, most studies that consider safety stock optimization are tied to a specific system structure such as serial, assembly, or distribution structure. This research focuses on formulating two different models. First, a multi- echelon safety stock optimization (MESSO) model for general supply chain topology is formulated. Then, it is converted into a robust form (RMESSO) which considers all possible fluctuation in demand and gives a solution that is valid under any circumstances. Second, the safety stock optimization model is integrated with tactical supply chain planning (SCP) for manufacturing systems. The integrated model is a multi-objective mixed integer non-linear programming (MINLP) model. This model aims to minimize the total cost and total time. A case study for each model is provided and the numerical results are analyzed.