Since the development of SoC is very fast, how to reduce the power consumption of SoC and improve the performance of SoC has become a very important issue. The power consumption of a system depends upon the hardware and software of a system. To overcome the issue of power consumption, the hardware circuit provides multi-voltage method to reduce task power consumption. On the other hand, the software tool decides the exact voltage for each task to minimize the total power consumption and finds a pipelined schedule of the periodic tasks to enhance the total throughput. In this thesis, a Tabu search is used to solve the voltage mapping and resource mapping problems of multi-voltage systems. This goal of this Tabu search is to find the solution with minimal power consumption for the multi-voltage system under the time constraints and resource constraints at the same time in the multi-voltage system to. Under the throughput constraints we use Tabu search to find solutions including the task¡¦s execution voltage and resource mapping, and then use list pipelined scheduling to schedule task and data communication and check their correctness. This method can reduce total power consumption. Experimental results show that our proposed algorithm can decide the resources mapping and pipeline in seconds, and it can reduce the power consumption efficiently.

There is an increasing interest in sustainability and a growing debate about environmental policy measures aiming at the reduction of green house gas emissions across di erent economic sectors worldwide. The transportation sector is one major greenhouse gas emitter which is heavily regulated to reduce its dependance on oil. These regulations along with the growing customer awareness about global warming has led vehicle manufacturers to seek di erent technologies to improve vehicle e ciencies and reduce the green house gases emissions while at the same time meeting customer's expectation of mobility and exibility. Plug-in hybrid electric vehicles (PHEV) is one major promising solution for a smooth transition from oil dependent transportation sector to a clean electric based sector while not compromising the mobility and exibility of the drivers. In the medium term, plug-in hybrid electric vehicles (PHEV) can lead to signi cant reductions in transportation emissions. These vehicles are equipped with a larger battery than regular hybrid electric vehicles which can be recharged from the grid. For short trips, the PHEV can depend solely on the electric engine while for longer journeys the alternative fuel can assist the electric engine to achieve extended ranges. This is bene cial when the use pattern is mixed such that and short long distances needs to be covered. The plug-in hybrid electric vehicles are well-suited for logistics since they can avoid the possible disruption caused by charge depletion in case of all-electric vehicles with tight time schedules. The use of electricity and fuel gives rise to a new variant of the classical vehicle routing with time windows which we call the plug-in hybrid electric vehicle routing problem with time windows (PHEVRPTW). The objective of the PHEVRPTW is to minimize the routing costs of a eet of PHEVs by minimizing the time they run on gasoline while meeting the demand during the available time windows. As a result, the driver of the PHEV has two decisions to make at each node: (1) recharge the vehicle battery to achieve a longer range using electricity, or (2) continue to the next open time window with the option of using the alternative fuel. In this thesis, we present a mathematical formulation for the plug-in hybrid-electric vehicle routing problem with time windows. We solve this problem using a Lagrangian relaxation and we propose a new tabu search algorithm. We also present the rst results for the full adapted Solomon instances.

Due to the significant effects on the performance and competitiveness of aircraft, high lift devices are of extreme importance in aircraft design. The flow physics of high lift devices is so complex, that traditional one pass and multi-pass design approaches can’t reach the most optimised concept and multi-objective design optimisation (MDO) methods are increasingly explored in relation to this design task. The accuracy of the optimisation, however, depends on the accuracy of the underlying Computational Fluid Dynamics (CFD) solver. The complexity of the flow around high-lift configuration, namely transition and separation effects leads to a substantial uncertainty associated with CFD results. Particularly, the uncertainty related to the turbulence modelling aspect of the CFD becomes important. Furthermore, employing full viscous flow solvers within MDO puts severe limitations on the density of computational meshes in order to achieve a computationally feasible solution, thereby adding to the uncertainty of the outcome. This thesis explores the effect of uncertainties in CFD modelling when detailed aerodynamic analysis is required in computational design of aircraft configurations. For the purposes of this work, we select the benchmark NLR7301 multi-element airfoil (main wing and flap). This flow around this airfoil features all challenges typical for the high-lift configurations, while at the same time there is a wealth of experimental and computational data available in the literature for this case. A benchmark shape bi-objective optimization problem is formed, by trying to reveal the trade-off between lift and drag coefficients at near stall conditions. Following a detailed validation and grid convergence study, three widely used turbulence models are applied within Reynolds-Averaged Navier-Stokes (RANS) approach. K- Realizable, K- SST and Spalart-Allmaras. The results show that different turbulent models behave differently in the optimisation environment, and yield substantially different optimised shapes, while maintaining the overall optimisation trends (e.g. tendency to maximise camber for the increased lift). The differences between the models however exhibit systemic trends irrespective of the criteria for the selection of the target configuration in the Pareto front. A-posteriori error analysis is also conducted for a wide range of configurations of interest resulting from the optimisation process. Whereas Spalart-Allmaras exhibits best accuracy for the datum airfoil, the overall arrangement of the results obtained with different models in the (Lift, Drag) plane is consistent for all optimisation scenarios leading to increased confidence in the MDO/RANS CFD coupling.

Closed-loop supply chain (CLSC) management provides opportunity for cost savings through the integration of product recovery activities into traditional supply chains. Product recovery activities, such as remanufacturing, reclaim a portion of the previously added value in addition to the physical material. Our problem setting is motivated by the practice of an Original Equipment Manufacturer (OEM) in the automotive service parts industry, who operates a well established forward network. The OEM faces customer demand due to warranty and beyond warranty vehicle repairs. The warranty based demand induces part returns. We consider a case where the OEM has not yet established a product recovery network, but has a strategic commitment to implement remanufacturing strategy. In accomplishing this commitment, complications arise in the network design due to activities and material movement in both the forward and reverse networks, which are attributed to remanufacturing. Consequently, in implementing the remanufacturing strategy, the OEM should simultaneously consider both the forward and reverse flows for an optimal network design, instead of an independent and sequential modeling approach. In keeping with these motivations, and with the goal of implementing the remanufacturing strategy and transforming independent forward and reverse supply chains to CLSCs, we propose to investigate the following research questions: 1. How do the following transformation strategies leverage the CLSC?s overall cost performance? ? Extending the already existing forward channel to incorporate reverse channel activities. ? Designing an entire CLSC network. 2. How do the following network flow integration strategies influence the CLSC?s overall cost performance? ? Using distinct forward and reverse channel facilities to manage the corresponding flows. ? Using hybrid facilities to coordinate the flows. In researching the above questions, we address significant practical concerns in CLSC network design and provide cost measures for the above mentioned strategies. We also contribute to the current literature by investigating the optimal CLSC network design. More specifically, we propose three models and develop mathematical formulations and novel solution approaches that are based on decomposition techniques, heuristics, and meta-heuristic approaches to seek a solution that characterizes the configuration of the CLSC network, along with the coordinated forward and reverse flows.

Wire Bonder and Molding are the most costive equipments in the investment of IC packaging; and the packaging quality, cost and delivery are concerned most in the assembly processes. An inappropriate process scheduling may result in the wastes of resources and assembly bottleneck. Manager must allocate the resources appropriately to adapt the changeable products and production lines. We would introduce several heuristic search methods, especially the Tabu search. Tabu search is one of the most popular methods of heuristic search. We also use Tabu list to record several latest moves and avoid to the duplication of the paths or loops. It starts from an initial solution and keep moving the solution to the best neighborhood without stock by Tabu. The iterations would be repeated until the terminating condition is reached. At last of the report, an example will be designed to approach the best wire bonding and molding scheduling by Tabu search; and verify the output volume is more than those with FIFO in the same period of production time. Tabu search will be then confirmed to be effective for flexible flow shop.

<p> </p><p><strong> </strong></p><p><strong> </strong></p><p>Befintliga studier har påvisat att genetiska algoritmer presterar bättre om de ges en bra startpopulation. I denna rapport presenteras en lokal sökalgoritm för att skapa en population med fokus på ”vehicle routing”-problem. Algoritmen använder sig av heuristik i en blandning av simulated annealing och tabu search för att skapa individerna till populationen. Utvärderingar av algoritmen på ett praktiskt problem visar att den ger en bra start jämfört med en slumpmässig startpopulation, vilket är vanligt att använda. Resultaten av utvärderingen visar också att algoritmen ger bäst resultat vid mer komplexa problem, medan den har mindre effekt om problemet är enklare.</p><p> </p><p><strong>Nyckelord: </strong>Simulated annealing, Tabu search, Genetiska algoritmer , ”vehicle routing”-problem.</p><p> </p>

Title from PDF of title page. / Document formatted into pages; contains 124 pages. / Thesis (Ph.D.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: As technology scales down and CMOS circuits are powered by lower supply voltages, standby leakage current becomes significant. A behavioral level framework for the synthesis of data-paths with low leakage power is presented. There has been minimal work done on the behavioral synthesis of low leakage datapaths. We present a fast architectural simulator for leakage (FASL) to estimate the leakage power dissipated by a system described hierarchically in VHDL. FASL uses a leakage power model embedded into VHDL leafcells. These leafcells are characterized for leakage accurately using HSPICE. We present results which show that FASL measures leakage power significantly faster than HSPICE, with less than a 5% loss in accuracy, compared to HSPICE. We present a comprehensive framework for synthesizing low leakage power data-paths using a parameterized Multi-threshold CMOS (MTCMOS) component library. / ABSTRACT: The component library has been characterized for leakage power and delay as a function of sleep transistor width. We propose four techniques for minimization of leakage power during behavioral synthesis: (1) leakage power management using MTCMOS modules; (2) an allocation and binding algorithm for low leakage based on clique partitioning; (3) selective binding to MTCMOS technology, allowing the designer to have control over the area overhead; and (4) a performance recovery technique based on multi-cycling and introduction of slack, to alleviate the loss in performance attributed to the introduction of MTCMOS modules in the data-path. Finally, we propose two iterative search based techniques, based on Tabu search, to synthesize low leakage data-paths. The first technique searches for low leakage scheduling options. The second technique simultaneously searches for a low leakage schedule and binding. It is shown that the latter technique of unified search is more robust. / ABSTRACT: The quality of results generated bytabu-based technique are superior to those generated by simulated annealing (SA) search technique. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Commuter rail systems, operating on unused or under-used railroad rights-of-way, are being introduced into many urban transportation systems. Since locations of available rail rights-of-way were typically chosen long ago to serve the needs of rail freight customers, these locations are not optimal for commuter rail users. The majority of commuter rail users do not live or work within walking distance of potential commuter rail stations, so provision of quick, convenient access to and from stations is a critical part of overall commuter decisions to use commuter rail. Minimizing access time to rail stations and final destinations is crucial if commuter rail is to be a viable option for commuters. Well-designed feeder routes or circulator systems are regarded as potential solutions to provide train station to ultimate destination access. Transit planning for main line or feeder routes relies upon static demand estimates describing a typical day. Daily and peak-hour demands change in response to the state of the transport system, as influenced by weather, incidents, holiday schedules and many other factors. Recent marketing successes of “smart phones” might provide an innovative means of obtaining real time data that could be used to identify optimal paths and stop locations for commuter rail circulator systems. Such advanced technology could allow commuter rail users to provide real-time final destination information that would enable real time optimization of feeder routes. This dissertation focuses on real time optimization of the Commuter Rail Circulator Route Network Design Problem (CRCNDP). The route configuration of the circulator system – where to stop and the route among the stops – is determined on a real-time basis by employing adaptive Tabu Search to timely solve an MIP problem with an objective to minimize total cost incurred to both transit users and transit operators. Numerical experiments are executed to find the threshold for the minimum fraction of travelers that would need to report their destinations via smart phone to guarantee the practical value of optimization based on real-time collected demand against a base case defined as the average performance of all possible routes. The adaptive Tabu Search Algorithm is also applied to three real-size networks abstracted from the Martin Luther King (MLK) station of the new MetroRail system in Austin, Texas. / text

This dissertation details an algorithm to solve the Intratheater Airlift Operations Problem (IAOP) using advanced tabu search. A solution to the IAOP determines the routes and assignment of customer requests to a fleet of aircraft over a given time horizon. This problem and other variants comprise an ongoing challenge for United States Air Force (USAF) planners who manage detailed logistics throughout many theaters of operations. Attributes of the IAOP include cargo time windows, multiple cargo types, multiple vehicle cargo bay configurations, vehicle capacity, route duration limits, and port capacities. The IAOP multi-criteria objective embraces several components with the primary goal of satisfying as much of the demand as possible while minimizing cost. The algorithm is extended to allow split load deliveries of customer requests, allowing a shipment to be split into two or more sub-loads which are delivered separately to the customer. The split load relaxation, while significantly increasing the complexity of the problem, allows for possible improvement in the solution. The necessary changes to the model and algorithm are detailed, providing a foundation to extend any local search algorithm solving a vehicle routing problem to allow split loading. Results allowing split loading are presented and compared with results without split loading. The algorithm is also extended to include a rolling time horizon. Starting from a solution found at a previous time step, the algorithm is limited on how the solution can be modified. This reflects the reality of operations in which near-term plans are locked as they approach and enter execution while longer-term plans are continually updated as new information arrives. / text

The research reported in this thesis considers the classical combinatorial optimization problem known as the Generalized Assignment Problem (GAP). Since the mid 1970's researchers have been developing solution approaches for this particular type of problem due to its importance both in practical and theoretical terms. Early attempts at solving GAP tended to use exact integer programming techniques such as Branch and Bound. Although these tended to be reasonably successful on small problem instances they struggle to cope with the increase in computational effort required to solve larger instances. The increase in available computing power during the 1980's and 1990's coincided with the development of some highly efficient heuristic approaches such as Tabu Search (TS), Genetic Algorithms (GA) and Simulated Annealing (SA). Heuristic approaches were subsequently developed that were able to obtain high quality solutions to larger and more complex instances of GAP. Most of these heuristic approaches were able to outperform highly sophisticated commercial mathematical programming software since the heuristics tend to be tailored to the problem and therefore exploit its structure. A new approach for solving GAP has been developed during this research that combines the exact Branch and Bound approach and the heuristic strategy of Tabu Search to produce a hybrid algorithm for solving GAP. This approach utilizes the mathematical programming software Xpress-MP as a Branch and Bound solver in order to solve sub-problems that are generated by the Tabu Search guiding heuristic. Tabu Search makes use of memory structures that record information about attributes of solutions visited during the search. This information is used to guide the search and in the case of the hybrid algorithm to generate sub problems to pass to the Branch and Bound solver. The new algorithm has been developed, imp lemented and tested on benchmark test problems that are extremely challenging and a comprehensive report and analysis of the experimentation is reported in this thesis.