The stochastic programming problem is considered in the case of a distribution function with partially known random parameters. A minimax approach is taken, and a numerical method is proposed for problems when informa...
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Considered here are extremal convolutions concerned with allocative efficiency, risk sharing, or market equilibrium. Each additive term is upper semicontinuous, proper concave, maybe non-smooth, and possibly extended-...
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Considered here are extremal convolutions concerned with allocative efficiency, risk sharing, or market equilibrium. Each additive term is upper semicontinuous, proper concave, maybe non-smooth, and possibly extended-valued. In a leading interpretation, each term, alongside its block of coordinates, is controlled by an independent economic agent. Vectors are construed as contingent claims or as bundles of commodities. These are diverse, divisible, and perfectly transferable. At every stage two randomly selected agents make bilateral direct exchanges. The amounts transferred between the two parties depend on the difference between their generalized gradients. The resulting process-and the associated convergence analysisfits the frames of stochastic programming. Motivation stems from exchange markets.
This paper analyses the sensitivity of reverse logistic formulation of herbs agro-industry based on fuzzy stochastic mixed integer linear programming. A case study from real world problem of herbs logistic in Indonesi...
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This paper analyses the sensitivity of reverse logistic formulation of herbs agro-industry based on fuzzy stochastic mixed integer linear programming. A case study from real world problem of herbs logistic in Indonesia is provided in order to respond stochastic challenges in the reverse logistic system. For implementation purpose of this current progress, some related historical and hypothetical data were deployed. The model was then used to test how far this fuzzy quantitative modelling is capable to solve the problem within available data ranges with consideration on possibility in each data occurrence. A GRG non-linear was used as model solution to solve the fuzzy stochastic modelling with implementation using Excel solver. The fuzzy quantitative modelling result with a case study in herbs logistic in Indonesia is concluded with verification and validation on current model formulation for decision making purposes in herbs reverse logistic.
Several emerging applications call for a fusion of statistical learning and stochastic programming (SP). We introduce a new class of models which we refer to as Predictive stochastic programming (PSP). Unlike ordinary...
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Several emerging applications call for a fusion of statistical learning and stochastic programming (SP). We introduce a new class of models which we refer to as Predictive stochastic programming (PSP). Unlike ordinary SP, PSP models work with datasets which represent random covariates, often refered to as predictors (or features) and responses (or labels) in the machine learning literature. As a result, these PSP models call for methodologies which borrow relevant concepts from both learning and optimization. We refer to such a methodology as Learning Enabled Optimization (LEO). This paper sets forth the foundation for such a framework by introducing several novel concepts such as statistical optimality, hypothesis tests for model-fidelity, generalization error of PSP, and finally, a non-parametric methodology for model selection. These new concepts, which are collectively referred to as LEO, provide a formal framework for modeling, solving, validating, and reporting solutions for PSP models. We illustrate the LEO framework by applying it to a production-marketing coordination model based on combining a pedagogical production planning model with an advertising dataset intended for sales prediction.
Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as ...
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Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into the effects of correlations on solution structures. We illustrate by an example.
Sustainable manufacturing can be expressed as the consideration of economical, environmental, and societal impact during product design for the entire life cycle. Although interest in nanomaterials and nanodevices has...
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ISBN:
(纸本)9781467320047
Sustainable manufacturing can be expressed as the consideration of economical, environmental, and societal impact during product design for the entire life cycle. Although interest in nanomaterials and nanodevices has been growing rapidly, the uncertainty about human health and environmental impact of the nanomaterials still exits. Moreover, current nanomanufacturing techniques have negative environmental and human health impacts. Therefore, engineers have been working on green design of nanomaterials and nanomanufacturing processes to achieve sustainable manufacturing goals. Companies that compete in the nanotechnology market have to develop a balance among the economic growth, environment protection, and positive social impact to achieve some measure of sustainable manufacturing.
This paper presents an detailed study about the development of an integrative DR policy for the optimal home energy management system under stochastic environment. In this study, home appliances are classified into th...
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ISBN:
(纸本)9781467397148
This paper presents an detailed study about the development of an integrative DR policy for the optimal home energy management system under stochastic environment. In this study, home appliances are classified into three categories and detailed modeling of all kinds of home appliances is given. Then, the optimal HEMS problem is formulated as a stochastic programming model considering the uncertainties of PV production and critical loads to minimize a customer's electricity cost. Monte Carlo simulation method is used to decompose the problem into a mixed integer linear programming problem. Finally, the proposed stochastic programming model is verified through numerical simulation. The simulation results show that the proposed stochastic DR model can reduce the effect of the uncertainties in residential environment on the electricity cost and obtain a better DR policy than the conventional deterministic model.
In this paper a fuzzy goal programming method for modeling and solving multiobjective stochastic decision making problem involving fuzzy random variables associated with the parameters of the objectives as well as sys...
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ISBN:
(纸本)9783642289255
In this paper a fuzzy goal programming method for modeling and solving multiobjective stochastic decision making problem involving fuzzy random variables associated with the parameters of the objectives as well as system constraints is developed. In the proposed approach. an expectation model is generated on the basis of the mean of the fuzzy random variables involved with the objectives of the problem. Then the problem is converted into an equivalent fuzzy programming model by considering the fuzzily defined chance constraints. Afterwards, the model is decomposed on the basis of the tolerance ranges of the fuzzy numbers associated with the fuzzy parameters of the problem. Now to construct the membership goals of the decomposed objectives under the extended feasible region defined by the decomposed system constraints, the individual optimal values of each objective is calculated in isolation. Then the membership functions are constructed to measure the degree of satisfaction of each decomposed objectives in the decision making environment. The membership functions are then converted into membership goals by assigning unity as the aspiration level of the membership goals. Then a fuzzy goal programming model is developed by minimizing the under deviational variables and thereby obtaining the optimal solution in the decision making environment.
In this work, the problem of transmission expansion under risk from demand uncertainty and capacity of the lines is addressed. A deterministic model is expanded into a two-stage stochastic model with fixed recourse by...
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ISBN:
(纸本)9789171785855
In this work, the problem of transmission expansion under risk from demand uncertainty and capacity of the lines is addressed. A deterministic model is expanded into a two-stage stochastic model with fixed recourse by means of considering the various foreseeable levels of demand as random. After this model is analyzed, a way of quantifying risk using the mean-variance Markowitz approach is proposed. The last model presented is such that randomness in the transmission capacity factor for each line is considered using a probabilistic constraint. The concepts of expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also studied.
This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accoun...
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This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving. (C) 2019 Published by Elsevier Ltd.
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