296,980 research outputs found

    Modular Optimizer for Mixed Integer Programming MOMIP Version 1.1

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    This Working Paper documents the Modular Optimizer for Mixed Integer Programming (MOMIP). MOMIP is an optimization solver for middle-size mixed integer programming problems, based on a modified branch-and-bound algorithm. It is designed as part of a wider linear programming modular library being developed within the IIASA CSA project on "Methodology and Techniques of Decision Analysis". The library is a collection of independent modules, implemented as C++ classes, providing all the necessary functions of data input, data transfer, problem solution, and results output. The Input/Output module provides data structure to store a problem and its solution in a standardized form as well as standard input and output functions. All the solver modules take the problem data from the Input/Output module and return the solutions to this module. Thus, for straightforward use, one can configure a simple optimization system using only the Input/Output module and an appropriate solver module. More complex analysis may require use of more than one solver module. Moreover, for complex analysis of real-life problems, it may be more convenient to incorporate the library modules into an application program. This will allow the user to proceed with direct feeding of the problem data generated in the program and direct withdrawal results for further analysis. The paper provides the complete description of the MOMIP module. Methodological background allows the user to understand the implemented algorithm and efficient use of its control parameters for various analyses. The module description provides all the information necessary to make MOMIP operational. It is additionally illustrated with a tutorial example and a sample program. Modeling recommendations are also provided, explaining how to built mixed integer models in order to speedup the solution process. These may be interesting, not only for the MOMIP users, but also for users of any mixed integer programming software

    Platform-Specific Code Generation from Platform-Independent Timed Models

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    Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing delays by adjusting the timing parameters in system models, based on an Integer Linear Programming problem formulation. We demonstrate the usefulness of our approach via a case study of infusion pump systems. Experimental results show that the code generated using our approach can better preserve the timing constraints

    The assessment of dynamic efficiency of decision making units using data envelopment analysis

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    The concept of a "production function" as means to measuring efficiency began in 1928 with the seminal paper by Cobb and Douglas (1928). However, until the 1950s, production functions were largely used as a tool for studying the functional distribution of income between capital and labour. Farrell's argument (1957) provides an intellectual basis for redirecting attention from the production function specifically to the deviation from that function as a measure of efficiency. He developed a method so that we can measure efficiency in terms of distance to the "best DMU" on the frontier isoquant. Charnes, Cooper and Rhodes (1978) generalised Farrell's concept to multiple - input multiple - output situations and reformulated it using mathematical programming and thus derived an efficiency measurement known as Data Envelopment Analysis (DEA). Therefore DEA is a linear programming based method for comparing Decision Making Units (DMUs) such as schools, hospitals, etc. In the method originally proposed by Charnes, Cooper and Rhodes (1978) the efficiency of a DMU is defined as a ratio of the weighted sum of outputs to the weighted sum of inputs. Thus in the original DEA approach the notion of time dimension has been ignored. This thesis proposes a IDEA based method for assessing the comparative efficiencies of DMUs operating production processes where input - output levels are inter - temporally dependent. One cause of inter - temporal dependence between input and output levels is stock input which influences output levels over many production periods. Such DMUs cannot be assessed by traditional or 'static' DEA. The method developed in the study overcomes the problem of inter - temporal input - output dependence by using input - output 'paths' mapped out by operating DMUs over time as the basis of assessing them. The aim of this thesis is, therefore, firstly, to address that traditional or "static" IDEA fails to capture the efficiency of DMUs with inter - temporal input - output dependence. Secondly the thesis develops an approach for measuring efficiency under inter - temporal input - output dependence by defining an inter - temporal Production Possibility Set (PPS). The method developed uses path of input - output levels associated with DMUs rather than input - output DMUs observed at one point in time as static IDEA does. Using this PPS, an assessment framework is developed which parallels that of static DEA. The thesis develops mathematical programming models which use input - output paths to measure efficiency, identify peers and target of performance of DMUs. The approach is illustrated using simulated and real data

    Robot programming by demonstration through system identification

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    Increasingly, personalised robots — robots especially designed and programmed for an individual’s needs and preferences — are being used to support humans in their daily lives, most notably in the area of service robotics. Arguably, the closer the robot is programmed to the individual’s needs, the more useful it is, and we believe that giving people the opportunity to program their own robots, rather than programming robots for them, will push robotics research one step further in the personalised robotics field. However, traditional robot programming techniques require specialised technical skills from different disciplines and it is not reasonable to expect end-users to have these skills. In this paper, we therefore present a new method of obtaining robot control code — programming by demonstration through system identification which algorithmically and automatically transfers human behaviours into robot control code, using transparent, analysable mathematical functions. Besides providing a simple means of generating perception-action mappings, they have the additional advantage that can also be used to form hypotheses and theoretical analysis of robot behaviour. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve wall following and corridor passing behaviours

    Evolutionary-based sparse regression for the experimental identification of duffing oscillator

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    In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. The results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. The proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics

    CH-FARMIS - An agricultural sector model for Swiss agriculture

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    This working paper gives an overview of the farm group model CH-FARMIS - a comparative static, process analytical, non-linear programming model that allows a separate assessment of the impacts of policies on organic and non-organic farming in Switzerland. In CH-FARMIS, the agricultural sector is represented by thirty farm groups, which can be char-acterised by their farming system, farm type and geographic location. Book keeping data from the Swiss FADN was used as a primary source for the model. By applying farm-specific weight-ing factors, farm data were aggregated to sector accounts. The technical coefficients of the farm model were either taken directly from farm accounts or calculated on the basis of normative data. Agricultural production is represented by 29 crop activities and 15 livestock activities. The factor allocation and production of each farm group is optimised by maximising farm income under policy and management restrictions. The restrictions cover the area of land and labour use, livestock feeding, fertiliser balance, rearing of young stock, allocation of direct payments and requirements with respect to the organic production system. A positive mathematical pro-gramming approach (PMP) was used to calibrate the production activities in the base year to observed activity levels
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