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The policy graph decomposition of multistage stochastic programming problems

NETWORKS 2020年第1期76卷 3-23页

作者： Dowson, Oscar Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

We propose the policy graph as a structured way of formulating a general class of multistage stochastic programming problems in a way that leads to a natural decomposition. We also propose an extension to the stochastic dual dynamic programming algorithm to solve a subset of problems formulated as a policy graph. This subset includes discrete-time, convex, infinite-horizon, multistage stochastic programming problems with continuous state and control variables. To demonstrate the utility of our algorithm, we solve an existing multistage stochastic programming problem from the literature based on pastoral dairy farming. We show that the finite-horizon model in the literature suffers from end-of-horizon effects, which we are able to overcome with an infinite-horizon model.

关键词： infinite horizon multistage policy graph stochastic programming

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A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties

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APPLIED ENERGY 2022年 314卷

作者： Sun, Qie Fu, Yu Lin, Haiyang Wennersten, Ronald Shandong Univ Inst Thermal Sci & Technol 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Inst Adv Technol 17923 Jingshi Rd Jinan 250061 Peoples R China Harvard Univ Sch Engn & Appl Sci Harvard China Project Cambridge MA 02138 USA

Uncertainties can affect the operation and planning of integrated energy systems. It is thus critical to understand how uncertainties can be handled in IES optimization. To address this issue, this study proposed a novel integrated stochastic programming-information gap decision theory (IGDT) approach, which can handle multiple uncertainties in the optimization of IES operation and planning. By applying the integrated approach to an office building, two sides of operation schedules and equipment capacities were generated, representing the risk-averse and risk-seeking strategies, respectively. The EES capacity under the risk-averse strategy is smaller than that under the risk-seeking strategy, while the TES capacity is larger under the risk-averse strategy. The consumption of fossil fuels is critical for dealing with uncertainties. Adjusting the operation of the gas turbine, energy storage units, and grid interaction are useful approaches for handling uncertainties and resisting associated risks. Through the comparison between the integrated approach and a two-stage stochastic approach, it is found that the proposed integrated method ensures the accuracy of the results and, at the same time, provides flexible options for system developers and operators regarding various uncertainties and associated risks.

关键词： Uncertainty Integrated energy system Information gap decision theory stochastic programming

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SOLUTION OF STRAIGHT AND DUAL MULTI-EXTREMAL TASKS OF stochastic programming 21

SOLUTION OF STRAIGHT AND DUAL MULTI-EXTREMAL TASKS OF STOCHA...

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21st International Multidisciplinary Scientific Geoconference: Ecology and Renewable Energy Sources. Nano, Bio and Green Technologies for a Sustainable Future, SGEM 2021

作者： Kolbin, Viatcheslav V. Petrov, St. Maxim M. Saint – Petersburg State University Russia

ISBN: (纸本)9786197603637

In this paper, a class of multi-extremal stochastic programming problems is considered. Direct and dual problems are also included in this problem, respectively, in this paper the question of the existence of solutions to such problems is studied, the necessary theorems and proofs are given. Decision making under uncertainty is based on the fact that the probabilities of various scenarios are unknown. Decision-making under risk conditions is based on the fact that each situation of the development of events can be given a probability of its implementation. This allows you to weigh each of the efficiency values and choose the situation with the lowest level of risk for implementation. Justification and selection of specific management decisions related to financial risks is based on the concept and methodology of decision theory. This theory assumes that decisions associated with risk are always characterized by elements of uncertainty about the specific behavior of the initial parameters, which do not allow one to clearly determine the values of the final results of these decisions. Depending on the degree of uncertainty of the future behavior of the initial decision-making parameters, there are risk conditions in which the probability of occurrence of individual events affecting the final result can be established with varying degrees of accuracy, and uncertainty conditions in which, due to the lack of necessary information, such probability cannot be established. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： stochastic programming

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Multi-objective stochastic programming energy management for integrated INVELOX turbines in microgrids: A new type of turbines

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RENEWABLE ENERGY 2020年 145卷 2754-2769页

作者： Shaterabadi, Mohammad Jirdehi, Mehdi Ahmadi Kermanshah Univ Technol Dept Elect Engn Kermanshah Iran

In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many advantages such as six times more power generation than previous types, work at low speed, inconsiderable maintenance and investment costs, and reduce the environmental effects of previous wind turbines. Moreover, other renewable and nonrenewable generators are used in the energy management and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in which the cost and pollution are considered as the objective functions. In the following, Uncertainties of wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution, respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg to 3302.7 and 3925.63 to 3910.2 Kg respectively. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Energy management INVELOX turbine Microgrid Renewable resource stochastic programming

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Technical Note-Waterfall and Agile Product Development Approaches: Disjunctive stochastic programming Formulations

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OPERATIONS RESEARCH 2020年第5期68卷 1356-1363页

作者： Kettunen, Janne Lejeune, Miguel A. George Washington Univ Sch Business Dept Decis Sci Washington DC 20052 USA

The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast. Achieving these goals is complicated because of uncertainties in projects' returns and durations. We develop new disjunctive stochastic programming models that capture the above-mentioned NPD goals. The first stochastic model is static, representing the traditional waterfall product development process, whereas the second one is dynamic, representing the agile product development process. We design a reformulation method and a decomposition algorithm to solve a problem encountered by a U.S.-based software start-up company. Our results indicate counter-intuitively that high reliability in attaining a targeted return may be achieved by investing in projects with a longer development time and higher risk. Furthermore, we show that if the capability to make dynamic decisions is overlooked while available, the time to attain the targeted return is overestimated.

关键词： product development waterfall agile disjunctive chance constraint stochastic programming

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A Two-Stage stochastic programming Model for Pre-positioning of Relief Supplies 14th

A Two-Stage Stochastic Programming Model for Pre-positioning...

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14th International Conference on Management Science and Engineering Management, ICMSEM 2020

作者： Wang, Yusheng Gong, Zaiwu Lev, Benjamin School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China Drexel University PhiladelphiaPA United States

ISBN: (纸本)9783030498887

Pre-positioning and distribution of emergency supplies are important activities in the preparedness and response stages for disasters. The goal of this paper is to address the joint decision-making of pre-positioning and distribution of relief supplies under uncertain environment. A two-stage stochastic programming model with the objective of the minimizing total cost is formulated and the uncertainties of disasters are taken into account. A case study focusing on addressing hurricane threats in the Gulf Coast area of the US is conducted to illustrated the application of the proposed model. The results analysis provides managerial insights for relief agencies. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： stochastic programming

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A Multi-product stochastic programming Model for Supplier Selection in a Humanitarian Relief Chain 4th

A Multi-product Stochastic Programming Model for Supplier Se...

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4th EAI International Conference on Computer Science and Health Engineering in Health Services, COMPSE 2020

作者： Miranda, Juan Luis Mejía Saucedo, Jania Astrid Martínez Faculty of Mechanical and Electrical Engineering Universidad Autónoma de Nuevo León San Nicolas de los Garza Mexico

ISBN: (纸本)9783030698386

This paper focuses on the supplier selection in a humanitarian relief chain. Considering the negative impact that natural disasters have in society and their environment, the procurement costs could be a challenging issue for humanitarian organizations (HO). Post-disaster procurement and pre-positioning inventory usually carry large costs due to the haste of the situation in the first or the extensive administration in the latter. This work analyzes supplier selection tools proposed in the literature and focuses primarily on the final decision stage where an organization optimizes the purchase of items from a supplier base. We propose a scenario-based two-stage stochastic programming model for a statutory agency;it considers a limited budget and the procurement from a higher-level source like the federal government. We also consider framework agreements (FA’s) where the buyer commits to purchase some reserved capacity from suppliers during a given period of time (also called horizon) so that better prices could be negotiated and help the organization lower their procurement costs. The proposed model delivers an optimal solution for a statutory agency and helps in the decision-making process as to what candidate suppliers are desirable to establish a framework agreement with. The solution shows important information as to what percentage of demand is covered from the selected suppliers. Limited funding in HO's is a very important factor for decision-making. The model incorporates a budget constraint and delivers information about the amount of relief items to procure form a different source other than the supplier base. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： stochastic programming

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Joint optimization of quality and cost in brass casting using stochastic programming

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ENGINEERING OPTIMIZATION 2020年第10期52卷 1645-1657页

作者： Sakalli, Umit Sami Birgoren, Burak Kirikkale Univ Dept Ind Engn Kirikkale Turkey

A critical process in brass casting is the blending of pure and scrap materials to satisfy specified metal ratios. The primary focus in such blending problems has always been cost minimization. The optimal blends produced by mathematical models use large amounts of scrap materials, which are cheaper but have high variations in ingredient ratios. This gives rise to quality problems. This study aims at joint optimization of cost and quality. A chance-constrained nonlinear mathematical model is developed for maximizing the minimum process capability level for a fixed cost. Then parametric programming is used to run the model for different costs to produce a Pareto-optimal frontier. An application to data from a brass factory showed that the frontier is highly nonlinear, enabling the decision maker to select a competitive process capability and cost value combination. The proposed approach is applicable to any blending problem in which ingredient amounts have statistical variation.

关键词： Joint optimization stochastic programming process capability brass casting blending problem

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A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期285卷 670-694页

作者： Bhuiyan, Tanveer Hossain Medal, Hugh R. Harun, Sarah Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA

Designing and maintaining a reliable and efficient transportation network is an important industrial problem. Integrating infrastructure protection with the network design model is efficient as these models provide strategic decisions to make a transportation network simultaneously efficient and reliable. We studied a combined network design and infrastructure protection problem subject to random disruptions where the protection is imperfect and multi-level and the effect of disruption is imperfect. In this research, we modeled a resource-constrained decision maker seeking to optimally allocate protection resources to the facilities, and construct links in the network to minimize the expected post-disruption transportation cost (PDTC). We modeled the problem as a two-stage stochastic program with both endogenous and exogenous uncertainty: a facility's post-disruption capacity depends probabilistically on the protection decision, making the uncertainty endogenous, while the link construction decision directly affects the transportation decision. We implemented an accelerated L-shaped algorithm to solve the model and predictive modeling techniques to estimate the probability of a facility's post-disruption capacity for a given protection and disruption intensity. Numerical results show that solution quality is sensitive to the number of protection levels modeled;average reduction in the expected PDTC is 18.7% as the number of protection levels increases from 2 to 5. Results demonstrate that the mean value model performs very poorly as the uncertainty increases. Results also indicate that the stochastic programming model is sensitive to the estimation error of the predictive modeling techniques;on average the expected PDTC becomes 6.38% higher for using the least accurate prediction model. (C) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Predictive modeling Endogenous uncertainty Infrastructure protection Disruption intensity

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A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2020年第9期120卷 1635-1657页

作者： Qin, Yichen Ma, Hoi-Lam Chan, Felix T. S. Khan, Waqar Ahmed Shanghai Maritime Univ Sch Econ & Management Shanghai Peoples R China Hang Seng Univ Hong Kong Dept Supply Chain & Informat Management Shatin Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China

Purpose This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations. Design/methodology/approach A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model. Findings Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed. Research limitations/implications The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved. Practical implications Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems. Originality/value A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

关键词： Rotable MRO spare Parts stochastic programming Aircraft maintenance Bender's decomposition algorithm

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