Resource distribution is an important problem in many fields. It is particularly important when the supplied resource is volatile and the individual distinct demands are stochastic. In such cases, the uncertainty of d...
详细信息
In this article, we presents a two-stage stochastic programming model for the problem of designing a closed-loop supply chain network which is applicable in the context of modular structured products. The model accoun...
详细信息
In this article, we presents a two-stage stochastic programming model for the problem of designing a closed-loop supply chain network which is applicable in the context of modular structured products. The model accounts for uncertainty in the quality status of the return stream, modeled as binary scenarios for each component in the reverse bill of material corresponding to such products. The stochastic model is solved by the aid of an accelerated L-shaped algorithm by considering a reduced set of scenarios.
The agent routing process considering weather conditions is a multi-objective optimization problem constrainedly, which various optimization criteria and constraints should be considered. The quality of the given rout...
详细信息
Using the GARCH model to describe the risky asset's return process so thatits time-varying moments and conditional heteroskedasticity can be properly reflected,general multiperiod optimal investment and consumptio...
详细信息
Using the GARCH model to describe the risky asset's return process so thatits time-varying moments and conditional heteroskedasticity can be properly reflected,general multiperiod optimal investment and consumption problems with both fixed andproportional transactions costs are investigated in this paper. We model this kind ofdifficult problems as a dynamic stochastic optimization problem, which can cope withdifferent utility functions and any number of time periods. The procedure to solve theresulting complex nonlinear stochastic optimization problem is discussed in detail and abranch-decomposition algorithm is devised.
In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we f...
详细信息
In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we formulate models to compare simultaneous multiperiod optimization and sequential single period optimization for the maximization of net present value and the maximization of total oil production over long term time horizons. This study helps to identify the importance of multiperiod optimization in oil and gas production planning. Further, we formulate a bicriterion optimization model to determine the ideal compromise solution between maximization of the two objective functions, the net present value (NPV) and the total oil production. To account for the importance of hedging against uncertainty in the oil production, we formulate a two-stage stochastic programming model to compute an improved expected value of NPV and total oil production for uncertainties in oil prices and productivity indices.
Biofuels derived from feedstock offer a sustainable source for meeting energy needs. The design of supply chains that deliver these fuels needs to consider quality variability with special attention to shipping costs,...
详细信息
Biofuels derived from feedstock offer a sustainable source for meeting energy needs. The design of supply chains that deliver these fuels needs to consider quality variability with special attention to shipping costs, because biofuel feedstocks are voluminous. stochastic programming models that consider all these considerations incur a heavy computational burden. The present work proposes a hybrid strategy that leverages machine learning to reduce the computational complexity of stochastic programming models via problem space reduction. First, numerous randomly generated reduced-space versions of the problem are solved multiple times to generate a set of solution data based on the concept of bootstrapping. Next, a supervised machine learning algorithm is implemented to predict a potentially beneficial mixed integer linear program problem space from which a near-optimal solution can be obtained. Finally, the mixed integer linear program selects the optimal solution from the reduced space generated by the machine learning algorithm. Through extensive numerical experimentation, we determine how much the problem space can be reduced, how many times the reduced space problem needs to be solved and the best performing machine learning techniques for this application. Several supervised learning algorithms, including logistic regression, decision tree, random forest, support vector machine, and k-nearest neighbors, are evaluated. The numerical experiments demonstrate that our proposed solution procedure yields near-optimal outcomes with a considerably reduced computational burden.
Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand ...
详细信息
The field of stochastic programming, sometimes with slight ambiguity referred to as stochastic optimization, deals with optimization problems involving uncertain data, and this uncertainty can be captured by probabili...
详细信息
For the internal trade export containers, the uncertainty of weight information which is provided by the cargo owners when booking the shipping space has made the route stowage planning decision more complex. Existing...
详细信息
Inventory space requirements in remanufacturing facilities can vary significantly over time and by type of space needed, due variability in recaptured component quality, availability of refurbished components, remanuf...
详细信息
Inventory space requirements in remanufacturing facilities can vary significantly over time and by type of space needed, due variability in recaptured component quality, availability of refurbished components, remanufactured product demand, and returned product rates. Multi-period stochastic programming recourse models are developed to identify optimal schedules of internal, external, and reconfigured amounts of storage space in each time period. Results are compared to deterministic expected value models, and computational issues are discussed.
暂无评论