Spatiotemporal uncertainties in the collection of crop residues pose great challenges to the development of a long-term and economic biomass-to-biofuel supply chain network (BSCN). A multiperiod stochastic programming...
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Spatiotemporal uncertainties in the collection of crop residues pose great challenges to the development of a long-term and economic biomass-to-biofuel supply chain network (BSCN). A multiperiod stochastic programming (SP) model considering uncertain collectible corn stover removal and farmer participation rates is developed. The SP model is compared with the deterministic programming for the expected scenario (DPES) model to provide decision-making support for BSCN in two different periods. With the statistical results of separate deterministic programming models for each scenario generated randomly based on the normal distribution as a reference, the economic performance of the SP and DPES models is compared in the model development period and then confirmed in the model validation period. A county-level case study with a 10-year development and a 3-year validation period is applied. The economic performance of the SP model is comparable to that of the DPES model in the development period, and the SP model achieves much higher cost savings in the validation period. Although biomass transportation cost is the most unstable cost component, the variation in bioethanol production cost is largely consistent with that in biomass purchase cost. The SP model demonstrates stronger robustness to uncertainty than the DPES model.(c) 2022 Elsevier Ltd. All rights reserved.
This article introduces an approach to assess the value and manage flexibility in engineering systems design based on decision rules and stochastic programming. The approach differs from standard Real Options Analysis...
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This article introduces an approach to assess the value and manage flexibility in engineering systems design based on decision rules and stochastic programming. The approach differs from standard Real Options Analysis (ROA) that relies on dynamic programming in that it parameterizes the decision variables used to design and manage the flexible system in operations. Decision rules are based on heuristic-triggering mechanisms that are used by Decision Makers (DMs) to determine when it is appropriate to exercise the flexibility. They can be treated similarly as, and combined with, physical design variables, and optimal values can be determined using multistage stochastic programming techniques. The proposed approach is applied as demonstration to the analysis of a flexible hybrid waste-to-energy system with two independent flexibility strategies under two independent uncertainty drivers in an urban environment subject to growing waste generation. Results show that the proposed approach recognizes the value of flexibility to a similar extent as the standard ROA. The form of the solution provides intuitive guidelines to DMs for exercising the flexibility in operations. The demonstration shows that the method is suitable to analyze complex systems and problems when multiple uncertainty sources and different flexibility strategies are considered simultaneously.
This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-tr...
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This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions;the tradeoffs that exist between location and transportation decisions;and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains. (C) 2014 Elsevier Ltd. All rights reserved.
Industrial robots undergo design and re-configuration processes to target extremely challenging precision and reliability performance with agile and efficient architectures. The need for such features currently preven...
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Industrial robots undergo design and re-configuration processes to target extremely challenging precision and reliability performance with agile and efficient architectures. The need for such features currently prevent the exploitation of reconfigurable robotics in manufacturing. The current work presents an approach to design and configure reconfigurable robots for the high precision manufacturing industry. The work proposes a configuration algorithm that enables the "identification of the robot architectures and the related reconfigurability features by selecting the type and number of robot modules to be implemented over time in order to better accomplish a number of production requirements. Particularly, assuming the robot will work by utilising a finite set of robotic modules, the algorithm determines the set of modules to form the arm and the ones to be allocated in the robot storage for possible usage over time. Results show a number of benefits such as a robotic chain with customised reaching and degrees of freedom with a reduced cost by performing an accurate module selection and configuration;this should lead the robot users to prefer reconfigurable robots to commercial rigid catalogue solutions proposed by robot manufacturers. (C) 2016 Elsevier Ltd. All rights reserved.
Practical portfolio investment problems under uncertainty can be modeled well as multiperiod stochastic programs. However, the numerical optimization methods that need to be used to solve such models seriously limit t...
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Practical portfolio investment problems under uncertainty can be modeled well as multiperiod stochastic programs. However, the numerical optimization methods that need to be used to solve such models seriously limit the level of detail in the uncertainty about future asset prices and returns that can be incorporated. Somewhat surprisingly, the question how this necessarily approximate description of the uncertainty should be constructed has received relatively little attention in the stochastic programming literature. Moreover, many of the descriptions that have been used are not arbitrage-free, and therefore inconsistent with modern financial asset-pricing theory. In this paper we will present aggregation methods that tan be used in combination with financial asset-pricing models to obtain a description of the uncertainty that is arbitrage-free, consistent with observed market prices as well as concise enough for a stochastic programming model. Furthermore, we will discuss how these aggregation methods can form the basis of an iterative solution approach.
This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-tim...
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This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-time jobs when the due dates are uncertain. The stochastic model decomposes the problem into two stages: The first (FS) determines the optimal capacities of the machines whereas the second (SS) computes an estimate of the expected profit of the on-time jobs for given machines' capacities. For a given sample of due dates, SS reduces to the deterministic parallel weighted number of on-time jobs problem which can be solved using the efficient branch and bound of M'Hallah and Bulfin [16]. FS is tackled using a sample average approximation (SAA) sampling approach which iteratively solves the problem for a number of random samples of due dates. SAA converges to the optimum in the expected sense as the sample size increases. In this implementation, SAA applies a ranking and selection procedure to obtain a good estimate of the expected profit with a reduced number of random samples. Extensive computational experiments show the efficacy of the stochastic model. (C) 2011 Elsevier Ltd. All rights reserved.
作者：
Royset, J. O.USN
Postgrad Sch Dept Operat Res Monterey CA 93943 USA
Optimality functions define stationarity in nonlinear programming, semi-infinite optimization, and optimal control in some sense. In this paper, we consider optimality functions for stochastic programs with nonlinear,...
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Optimality functions define stationarity in nonlinear programming, semi-infinite optimization, and optimal control in some sense. In this paper, we consider optimality functions for stochastic programs with nonlinear, possibly nonconvex, expected value objective and constraint functions. We show that an optimality function directly relates to the difference in function values at a candidate point and a local minimizer. We construct confidence intervals for the value of the optimality function at a candidate point and, hence, provide a quantitative measure of solution quality. Based on sample average approximations, we develop an algorithm for classes of stochastic programs that include CVaR-problems and utilize optimality functions to select sample sizes.
Conventional design and optimization methods are being challenged by the rapid evolution of electronic and optical communication networks. It becomes necessary to incorporate the stochastic effect of traffic flows int...
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Conventional design and optimization methods are being challenged by the rapid evolution of electronic and optical communication networks. It becomes necessary to incorporate the stochastic effect of traffic flows into network models. This paper introduces the stochastic programming (SP) methodology to characterize the stochastic traffic. A multi-commodity network model is proposed. Two SP approaches, here-and-now and scenario tracking, are described through case studies for a prototype network. (c) 2004 Elsevier Ltd. All rights reserved.
Demand fulfillment and capacity utilization directly affects customer satisfaction, market growth, and the profitability of the company in the semiconductor industry. These characteristics boost the significance of al...
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Demand fulfillment and capacity utilization directly affects customer satisfaction, market growth, and the profitability of the company in the semiconductor industry. These characteristics boost the significance of allocating various customer demands to a number of wafer fabrication facilities (fabs) with different capacity configurations. Before volume production, the introduction of new semiconductor product, namely new tape-out (NTO), requires extremely sophisticated and lengthy qualification with high-cost masks and pilot runs in the qualified fabs. Thus, the NTO allocation will affect future product mix of the qualified fabs, and the flexibility to fulfill the volume demands of the allocated NTOs in the corresponding fabs. This research aims to construct a two-stage stochastic programming (2-SSP) demand fulfillment model. The first stage considers NTO allocation decisions to a number of qualified fabs before the corresponding demand volume is realized. The second stage allocates the capacity to fulfill demand requirements based on the results of four options of capacity reconfiguration: (1) qualifying a product to more than one fab (share);(2) physically transferring a set of masks for a product from one fab to another, where a requalification is required (transfer);(3) moving tools from under-loaded fabs to over-utilized fabs (backup);and (4) utilizing different technologies to capacity inside a fab to support the technology with insufficient capacities (exchange). Both the share and transfer options require long lead time for qualification, whereas the backup and exchange options can be accomplished within a planned timeframe. A numerical study based on real settings is conducted to estimate the validity of the proposed 2-SSP model via values of stochastic solution (VSS) and expected values of perfect information (EVPI). The results showed the benefits of adopting 2-SSP models, especially in an environment with high-demand fluctuation. Furthermore, the propos
stochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situati...
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stochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision-dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi-source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision-dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two-stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools. This paper reviews the stochastic programming with decision-dependent uncertainty (DDU), especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a taxonomy of available modelling techniques and solution approaches for stochastic programming are presented and discussed. Eventually, insights of two-stage stochastic programming with DDU for future power system optimization are explored. image
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