K�nnen Produktionsentscheidungen als Investitionsentscheidungen modelliert werden?

Interviews with Swiss farmers lead to the hypotheses that production decisions, also in crop growing, are not made annually, but similarly to investment decisions in the long run. This hypothesis is backed by non-parametric time-series analysis for Switzerland and Germany. This creates a theoretical basis for combining Positive Mathematical Programming with flexibility constraints in optimization models. Results of the forecasting model SILAS show that forecasting quality is improved through this approach.Flexibility constraints, production decisions, optimization models, forecasting quality, Farm Management,

Energy Chains Optimization for Selection of Sustainable Energy Supply

The notion of energy chain concept has been defined as the trajectory of energy transformations from the fuel source or energy sources to useful energy form to end users. Production of fuels, heat and electricity from different sources is defined by the appropriate energy supply chain. Every single energy supply chain can be uniquely defined by several sustainability criteria. These criteria are: total energy efficiency of production, total exergy efficiency of overall chain, the coefficient of exergy quality for different products at energy chains, economy of production, investment and environmental criteria. Optimal energy supply chain can be chosen by using multicriteria optimization which fulfils the above-mentioned sustainability criteria. This selected energy chain is close to ideal solution. The ideal energy supply chain is formed from the set of energy production ways which are defined from the perspective of sustainability criteria and which have connection with the current status of technologies, economic, environmental parameters, etc. The concept of optimization in practice is usually based on economics until recently, often neglecting all the other consequences of such a decision. Therefore, multicriteria decision making (MCDM) improves the opportunities in assessing the optimal variant of energy chain for defined ranking criteria. Before the optimization process, it is necessary to create a mathematical model for calculation of optimization criteria. Also, for each specific case of energy production, it is necessary to develop appropriate mathematical formulas to describe the energy chain. Numerical verification, all mathematical calculations and modelling have been applied and confirmed on wood biomass supply chain for energy production in this case. The reason for this is complexity of supply chains in the bioenergy and representation of renewable energy sources. For total ranking of energy chain for production of fuel or energy and selection of optimum variant, the multicriteria optimization and VIKOR method were applied. The significance of energy production from renewable energy sources is particularly expressed nowadays. Basically, the most significant part in the process of energy production from energy sources is the supply chain, final conversion of energy in useful form at the energy plant and the distribution process to end users. Due to the fact that there are various opportunities for the composition of energy chains of fuel supply and different ways of energy production, it is necessary to try to make a unique mathematical approach for this problem. With the proposed sustainability criteria and developed mathematical model, it is possible to unify the overall problem of energy supply chains’ optimization. The proposed developed method can be used for the optimization of any kind of energy supply chains (electricity, heat, fuels or their mix). All of these are enabled by proper selection criteria for the description of overall energy transformations in energy chains and quality evaluation of the energy produced. The developed approach and mathematical model have a very practical application in the selection of optimal variant of energy production and of course in designing new energy chains

An overview of different approaches in hydrogen network optimization via mathematical programming

Goal: Hydrogen has shown increasing demand in oil refineries, due to the importance of its use as a sulfur capture element. As different oils and products require different amounts of hydrogen, their use optimally is an essential tool for refinery production scheduling. A comparison was made between the different approaches used in optimization via mathematical programming.Design / Methodology / Approach: One of the most used methods for hydrogen network optimization is through mathematical programming. Linear and non-linear models are discussed, positive aspects of each formulation and different initialization techniques for non-linear modeling were considered.Results: The optimization through the linear model was more satisfactory, taking into account the payback of the new proposed design, combined with the use of compressor rearrangement, which reduces the investment cost.Limitations of the investigation: The objective function chosen is based on the operational cost, but another approach to be considered would be the total annual cost. In addition, the parameters related to costs are obtained from the literature and may change over the years.Practical implications: The proposal is to discuss the main aspects of each model, showing which models more robust and easier to converge are capable of providing competitive results. Also, different initialization techniques that can be used in future works.Originality / Value: The main contribution is the relationship between hydrogen management and production scheduling and for that, a discussion is made about possible formulations. Linear model is sufficient to optimize the problem, due to its main characteristics discussed

MATHEMATICAL MODEL OF CURRENT CAPITAL’S OPTIMIZATION AND MANAGEMENT

Current capital management is an important part of the enterprise management system. Determining the optimal value of its components allows more effectively using assets and investing resources. In order to determine the optimal value, it is necessary to determine the basic interdependence between these components. The process of optimization is based on the relationships and interactions between the components of current capital. The mathematical model of optimization of current capital components has been formed and proposed for the purpose of determining the optimal value of each of the components, which will enable the company to more effectively use available resources and production capacities and prevent inappropriate spending and investment of resources. This model is based on the Lotka–Volterra equations and represents a system of three differential equations that describe the interactions and interdependencies between the components of current capital, determining their optimal size, which ensures the continuity of economic activity, in the use of all production capacities of the enterprise and will help to prevent inefficient spending and investment in assets. The model is created in the process of PhD thesis writing.Current capital management is an important part of the enterprise management system. Determining the optimal value of its components allows more effectively using assets and investing resources. In order to determine the optimal value, it is necessary to determine the basic interdependence between these components. The process of optimization is based on the relationships and interactions between the components of current capital. The mathematical model of optimization of current capital components has been formed and proposed for the purpose of determining the optimal value of each of the components, which will enable the company to more effectively use available resources and production capacities and prevent inappropriate spending and investment of resources. This model is based on the Lotka–Volterra equations and represents a system of three differential equations that describe the interactions and interdependencies between the components of current capital, determining their optimal size, which ensures the continuity of economic activity, in the use of all production capacities of the enterprise and will help to prevent inefficient spending and investment in assets. The model is created in the process of PhD thesis writing

On the Optimal Boundary of a Three-Dimensional Singular Stochastic Control Problem Arising in Irreversible Investment

de Angelis T, Federico S, Ferrari G. On the Optimal Boundary of a Three-Dimensional Singular Stochastic Control Problem Arising in Irreversible Investment. Center for Mathematical Economics Working Papers. Vol 509. Bielefeld: Center for Mathematical Economics; 2014.This paper examines a Markovian model for the optimal irreversible investment problem of a firm aiming at minimizing total expected costs of production. We model market uncertainty and the cost of investment per unit of production capacity as two independent one-dimensional regular diffusions, and we consider a general convex running cost function. The optimization problem is set as a three-dimensional degenerate singular stochastic control problem. We provide the optimal control as the solution of a Skorohod reflection problem at a suitable free-boundary surface. Such boundary arises from the analysis of a family of two-dimensional parameter-dependent optimal stopping problems and it is characterized in terms of the family of unique continuous solutions to parameter-dependent nonlinear integral equations of Fredholm type

On the Optimal Boundary of a Three-Dimensional Singular Stochastic Control Problem Arising in Irreversible Investment

de Angelis T, Federico S, Ferrari G. On the Optimal Boundary of a Three-Dimensional Singular Stochastic Control Problem Arising in Irreversible Investment. Center for Mathematical Economics Working Papers. Vol 509. Bielefeld: Center for Mathematical Economics; 2014.This paper examines a Markovian model for the optimal irreversible investment problem of a firm aiming at minimizing total expected costs of production. We model market uncertainty and the cost of investment per unit of production capacity as two independent one-dimensional regular diffusions, and we consider a general convex running cost function. The optimization problem is set as a three-dimensional degenerate singular stochastic control problem. We provide the optimal control as the solution of a Skorohod reflection problem at a suitable free-boundary surface. Such boundary arises from the analysis of a family of two-dimensional parameter-dependent optimal stopping problems and it is characterized in terms of the family of unique continuous solutions to parameter-dependent nonlinear integral equations of Fredholm type

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Application of a Spatial Water Model in a Chinese Watershed

China's fast growing economy has brought some environmental problems, especially in water administration. Inefficiencies in irrigation activities have created severe negative effects to the environment of rural communities, and the more serious water shortages hamper food production, too. Major questions at hand are, how to improve water use efficiency, to reduce negative external effects, to optimize water allocation in agriculture, to invest in water saving technologies, and to assure more water for high value added agriculture. This paper investigates the impacts of irrigation technologies and investments in water saving on the rural economy and the environment. By taking into account individual farmers' inclination to adopt modern water-saving technologies and governments willingness to improve public water transit systems, we optimize water use in a Chinese watershed. The main contribution is a model that shows how to optimize spatial allocation and adoption of irrigation technology given farm and investment costs. The paper employs a mathematical, spatial programming model using GAMS for optimization. It shows the importance of water pricing and discusses various policy measures such as pricing and public conveyance. The model results are of value for policy makers and project managers to allocate water more efficiently, to optimize irrigation projects, and to provide references for farmers in applying water conservation technologies.a spatial model, water use efficiency, adoption of irrigation technology, technology, Resource /Energy Economics and Policy, C61, Q25, Q56,

Modelling of Centrally Planned Food and Agriculture Systems: A Framework for a National Policy Model for the Hungarian Food and Agriculture Sector

In this paper the general structure and mathematical description of the Hungarian Agricultural Model is presented. As an introduction the basic characteristics of food and agriculture systems in the centrally planned economies and IIASA's approach in their modelling and some features of Hungarian agriculture are discussed. The Hungarian Agricultural Model has a descriptive and dynamic (recursive with a one year time increment) character. Besides the disaggregated food and agriculture (25 agricultural and 25 processed food commodities) the rest of the economy is also considered. The model is in fact a system of interconnected models. The economic management and planning submodel describes the decision making and control of socialist state following the idea of central planning of the economy. The desired structure of food production, export, import and investment targets are calculated by a linear programming model. The submodel of real sphere covers the whole national economy. The major blocks of the latter submodel are related to production (linear programming models for socialist agriculture and food processing sector, nonlinear optimization model for household and private agriculture), consumption and trade including nonlinear demand system as well as updating available resource and other model parameters

A Strategy for the Economic Optimization of Combined Cycle Gas Turbine Power Plants by Taking Advantage of Useful Thermodynamic Relationships

Optimal combined cycle gas turbine power plants characterized by minimum specific annual cost values are here determined for wide ranges of market conditions as given by the relative weights of capital investment and operative costs, by means of a nonlinear mathematical programming model. On the other hand, as the technical optimization allows identifying trends in the system behavior and unveiling optimization opportunities, selected functional relationships are obtained as the thermodynamic optimal values of the decision variables are systematically linked to the ratio between the total heat transfer area and the net power production (here named as specific transfer area). A strategy for simplifying the resolution of the rigorous economic optimization problem of power plants is proposed based on the economic optima distinctive characteristics which describe the behavior of the decision variables of the power plant on its optima. Such approach results in a novel mathematical formulation shaped as a system of nonlinear equations and additional constraints that is able to easily provide accurate estimations of the optimal values of the power plant design and operative variables.Fil: Godoy, Ezequiel. UTN. FRRo. CAIMI; ArgentinaFil: Benz, Sonia Judith. UTN. FRRo. CAIMI; ArgentinaFil: Scenna, Nicolás José. UTN. FRRo. CAIMI; Argentina - CONICET. INGAR; ArgentinaPeer Reviewe