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multi-stage stochastic programming for integrated optimization of ethylene production processes and utility systems under uncertainty

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ENERGY 2025年 320卷

作者： Zhao, Liang Rong, Jiyun Ma, Guofu Liang, Chen Long, Jian East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 20237 Peoples R China East China Univ Sci & Technol Minist Educ Engn Res Ctr Proc Syst Engn Shanghai 20237 Peoples R China

multiple time-scale uncertainties in equipment efficiency, process demand, and steam prices pose significant challenges to the modeling and optimization of ethylene production processes and utility systems. This paper introduces a novel multi-stage stochastic programming (MSSP) framework that integrates advanced linearization techniques and data augmentation via a Wasserstein generative adversarial network (WGAN), providing a robust solution for improving energy conversion efficiency and optimizing ethylene production processes with utility systems under uncertainty. A three-stage stochastic programming model addresses the impacts of uncertainties across different time scales. Nonlinear terms in the model are linearized using the McCormick envelope method to simplify the solution process, and a WGAN generates simulated data to augment the dataset. The effectiveness of the proposed method was validated in a real-world ethylene plant case study, where optimization results showed a 3.9 % increase in net profit, a 3.5 % increase in ethylene yield, and a 6.5 % decrease in the cost of the utility system compared to traditional methods.

关键词： Integrated optimization multi-stage stochastic programming Uncertainty set Wasserstein generative adversarial network Energy efficiency

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multi-stage stochastic programming based offering strategy for hydrogen fueling station in joint energy, reserve markets

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RENEWABLE ENERGY 2021年 180卷 605-615页

作者： Wu, Xiong Zhao, Wencheng Li, Haoyu Liu, Bingwen Zhang, Ziyu Wang, Xiuli Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

Hydrogen fueling stations (HFSs) with onsite hydrogen production systems, which are usually composed of electrolyzers, hydrogen storage tanks and fuel cells, not only supply hydrogen for hydrogen-powered vehicles but also serve as a dispatchable technology that can bid in electricity markets. Except partici-pating in energy market, joining in reserve market can compensate the cost in energy market and in-crease the total revenue of HFS. This paper proposes a multi-stage stochastic programming model to find the optimal offering strategy of the HFS in energy, reserve markets taking into account a series of un-certainties: day-ahead price, secondary reserve price, system imbalance price and hydrogen demand. Nonanticipativity constraints are employed to guarantee the decisions are made according to the realized uncertainty information up to the present stage. Compared with traditional stochastic programming model, the proposed model adequately considers the sequential bidding decisions with the gradual revealing of the uncertainty over time. Numerical experiments based on one case study indicate that the participation of reserve market greatly increase the revenue of HFS. In addition, the proposed multi-stage stochastic programming model is effective in characterizing the sequential decision. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Hydrogen fueling station multi-stage stochastic programming Nonanticipacivity constraints Offering strategy Joint markets

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A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2024年第3期15卷 1197-1232页

作者： Gulcan, Berkay Song, Yongjia Eksioglu, Sandra D. Roni, Mohammad Clemson Univ Dept Ind Engn 211 Fernow St Clemson SC 29634 USA Univ Arkansas Bell Engn Ctr 4183 Dept Ind Engn Fayetteville AR 72701 USA Idaho Natl Lab 1955 N Fremont Ave Idaho Falls ID 83415 USA

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

关键词： Bioenergy Biomass Biomass processing system multi-stage stochastic programming

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multi-stage stochastic programming models for provisioning cloud computing resources

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期288卷 886-901页

作者： Bulbul, Kerem Noyan, Nilay Erol, Hazal Sabanci Univ Ind Engn TR-34956 Istanbul Turkey Amazon Amazon Web Serv Seattle WA 98121 USA Amazon Middle Mile Planning Res & Optimizat Sci Seattle WA 98109 USA

We focus on the resource provisioning problem of a cloud consumer from an Infrastructure-as-a-Service type of cloud. The cloud provider offers two deployment options, which can be mixed and matched as appropriate. Cloud instances may be reserved for a fixed time period in advance at a smaller usage cost per hour but require a full commitment and payment for the entire contract duration. In contrast, on-demand instances reflect a pay-as-you-go policy at a premium. The trade-off between these two options is rooted in the inherent uncertainty in demand and price and makes it attractive to complement a base reserved capacity with on-demand capacity to hedge against the spikes in demand. This paper provides several novel multi-stage stochastic programming formulations to enable a cloud consumer to handle the cloud resource provisioning problem at a tactical level. We first formulate the cloud resource provisioning problem as a risk-neutral multi-stage stochastic program, which serves as the base model for further modeling variants. In our second set of models, we also incorporate a certain concept of system reliability. In particular, chance constraints integrated into the base formulation require a minimum service level met from reserved capacity, provide more visibility into the future available capacity, and smooth out expensive on-demand usage by hedging against possible demand fluctuations. An extensive computational study demonstrates the value of the proposed models by discussing computational performance, gleaning practical managerial insights from the analysis of the solutions of the proposed models, and quantifying the value of the stochastic solutions. (C) 2020 Elsevier B.V. All rights reserved.

关键词： OR in service industries multi-stage stochastic programming Chance constraints Cloud computing Resource provisioning On-demand instance Reserved instance

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A multi-stage stochastic programming approach for an inventory-routing problem considering life cycle

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RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2537-2559页

作者： Paeizi, Alireza Makui, Ahmad Pishvaee, Mir Saman Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Food waste and proper methods to deal with it are one of the main challenges of supply chain network management. The majority of studies on how to use mathematical models in the supply chain have focused on goods that are at their peak of freshness as soon as they are produced and deteriorate over time. While some products experience an increase in value at the start of their life cycle, this value eventually reaches its maximum level, and after this point, these products experience a decline in value before being eliminated from the consumption cycle. The objective of this study is to develop a comprehensive inventory-routing model suitable for supply chain networks where products exhibit an increase and decrease in value over time. By considering the randomness and dynamic uncertainty of market demands and the fact that each period has effects on the next period, The proposed model employs a multi-stage stochastic programming (MSSP) approach. By doing so, the model ensures a balanced flow between different components of the network while considering nondeterministic demand under various scenarios that are shown in a tree of scenarios. The utilization of MSSP leads to more reliable solutions compared to deterministic models, making it possible for chain stores to make well-informed decisions in their inventory management and distribution strategies. Ultimately, this approach results in cost savings for chain stores handling such products. This research makes a significant contribution to the existing literature by demonstrating the effectiveness of the proposed model on actual data and highlighting the benefits of using stochastic programming in supply chain optimization.

关键词： Inventory-routing problem perishability multi-stage stochastic programming supply chain network design product life cycle

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An Uncertainty-Based Random Boundary Interval multi-stage stochastic programming for Water Resources Planning

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WATER RESOURCES MANAGEMENT 2023年第12期37卷 4571-4587页

作者： Raei, Mehri Hossienzad, Javad Ghorbani, Mohammad Ali Univ Tabriz Fac Agr Dept Agr Econ Tabriz Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Given the increasing population growth and rapid global economic development, the conflict between reduced available water resources and increased water demand in many countries has become a challenging issue;confirming the necessity of allocating optimal water resources to equate water conservation and environmental sustainability. Uncertainties and climate change together deteriorate the complexity of optimal allocation related to water resources used for the agricultural sector. The present study sought to design an uncertainty-based random boundary interval multi-stage stochastic programming (URBIMSP) model by integrating the Kataoka's criterion method and chance-constrained programming within a general optimization framework to control the uncertain information which exists in both constraints and goal functions. The developed model was applied to a case study of water resources allocation in the Marand basin, Iran. Then, a conjunctive water allocation system was arranged to identify the introduced model in two planning periods which were 2023 to 2025 and 2026 to 2028. The results indicated that different water allocation schemes and water shortages with varying net system benefits would be achieved under different stream flow levels and different risk scenarios. The result was significant for carrying out a comprehensive analysis of the tradeoffs between total net benefits and related risks. Finally, the findings demonstrated that improving irrigation efficiency and upgrading irrigation systems are the key strategies for farmers to achieve economic benefits while also help to mitigate environmental impacts.

关键词： Chance-constrained programming Kataoka's criterion Marand basin multi-stage stochastic programming Water resources management

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A multi-stage stochastic programming approach for pre-positioning of relief supplies considering returns

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年第1期88卷

作者： Hu, Shaolong Hu, Qingmi Tao, Sha Dong, Zhijie Sasha Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212003 Peoples R China Nanjing Univ Sch Management Sci & Engn Nanjing 210093 Peoples R China Univ Houston Dept Construct Management 4230 MLK Blvd Houston TX 77204 USA

A dynamic pre-positioning problem is proposed to efficiently respond to victims' need for relief supplies under uncertain and dynamic demand in humanitarian relief. The problem is formulated as a multi-stage stochastic programming model that considers pre-positioning with the dynamic procurement and return decisions about relief supplies over a time horizon. To validate the advantages of dynamic pre-positioning, three additional pre -positioning strategies are presented: pre-positioning with one-time procurement and without returns, pre -positioning with one-time procurement and returns, and pre-positioning with dynamic procurement and without returns. Using data from real-world disasters in the United States in the Emergency Events Database, we present a numerical analysis to study the applicability of the proposed models. We develop a sample average approximation approach to solving the proposed model in large-scale cases. Our main contribution is that we integrate dynamic procurement and return strategies into pre-positioning to decrease both costs and shortage risks in uncertain and dynamic contexts. The results illustrate that dynamic pre-positioning outperforms the other three strategies in cost savings. It also indicates that a higher return price is particularly helpful for decreasing unmet demand. The proposed models can help relief agencies evaluate and choose the solutions that will have the greatest overall effectiveness in the context of different relief practices.

关键词： Emergency logistics Dynamic pre-positioning Return multi-stage stochastic programming Sample average approximation

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Design of a Supply Chain-Based Production and Distribution System Based on multi-stage stochastic programming

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2023年第3期48卷 357-370页

作者： Iswanto, A. Heri Alazzawi, Fouad Jameel Ibrahim Guerrero, John William Grimaldo Ahmed, Alim Al-Ayub Chetthamrongchai, Paitoon Vladimirovich, Kabanov Oleg Kadhim, Mustafa M. Jawad, Mohammed Abed Surendar, A. Univ Pembangunan Nas Vet Jakarta Fac Hlth Sci Publ Hlth Dept SKMMARS Depok Indonesia AL Rafidain Univ Coll Comp Engn Dept Baghdad Iraq Univ la Costa Dept Energia Barranquilla Colombia Jiujiang Univ Sch Accounting 551 Qianjindonglu Jiujiang Jiangxi Peoples R China Kasetsart Univ Fac Business Adm Bangkok Thailand Natl Res Ogarev Mordovia State Univ 68 Bolshevistskaya Str Saransk 430005 Russia Kut Univ Coll Dept Dent Kut Wasit Iraq Al Nisour Univ Coll Baghdad Iraq Saveetha Dent Coll & Hosp Saveetha Inst Med & Tech Sci Dept Prosthodont Chennai India

Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.

关键词： demand uncertainty location capacity planning multi-stage stochastic programming production and distribution

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multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2022年 159卷 111784-111784页

作者： Wang, Zekai Ding, Tao Jia, Wenhao Huang, Can Mu, Chenggang Qu, Ming Shahidehpour, Mohammad Yang, Yongheng Blaabjerg, Frede Li, Li Wang, Kang Chi, Fangde Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Lawrence Livermore Natl Lab Livermore CA 94550 USA Illinois Inst Technol ECE Dept Chicago IL 60616 USA Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark State Grid Shaanxi Elect Power Co Xian 710048 Shaanxi Peoples R China

Recent energy systems have witnessed a large number of extreme weather events due to the global climate change, resulting in severe damages and economic losses. To address this problem, this paper proposes a multi-stage stochastic programming model for resilient integrated electricity and natural gas distribution systems under typhoon natural disasters. A three-stage modeling is employed to quantify the system resilience against the dynamic process of a typhoon, where the network reinforcement, network reconfiguration, and network repairing are coordinated. Moreover, the interaction between the power system and natural gas system during a typhoon attack is reflected in this model. To address the uncertainties introduced by the typhoon moving paths, non-anticipativtity constraints are adopted in the proposed multi-stage stochastic programming model. Case studies on a 33-bus-48-node and a 144-bus-85-node integrated electricity and natural gas distribution systems verify the effectiveness of the proposed method. The results of the case studies show that the current IEGDS is still fragile when facing natural disasters, and building a resilient IEGDS to recover from natural disasters rapidly is meaningful in recent vigorous trends of developing integrated energy systems. By applying the proposed model, the proposed model and strategies can enable the IEGDS to reduce the load curtailment by about 52%, and the total economic loss will be reduced by 54%.

关键词： Resilience Integrated electricity and natural gas distribution system Typhoon natural disasters multi-stage stochastic programming Network reinforcement

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Two-stage linear decision rules for multi-stage stochastic programming

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MATHEMATICAL programming 2022年第1期191卷 347-380页

作者： Bodur, Merve Luedtke, James R. Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

multi-stage stochastic linear programs (MSLPs) are notoriously hard to solve in general. Linear decision rules (LDRs) yield an approximation of an MSLP by restricting the decisions at each stage to be an affine function of the observed uncertain parameters. Finding an optimal LDR is a static optimization problem that provides an upper bound on the optimal value of the MSLP, and, under certain assumptions, can be formulated as an explicit linear program. Similarly, as proposed by Kuhn et al. (Math Program 130(1):177-209, 2011) a lower bound for an MSLP can be obtained by restricting decisions in the dual of the MSLP to follow an LDR. We propose a new approximation approach for MSLPs, two-stage LDRs. The idea is to require only the state variables in an MSLP to follow an LDR, which is sufficient to obtain an approximation of an MSLP that is a two-stage stochastic linear program (2SLP). We similarly propose to apply LDR only to a subset of the variables in the dual of the MSLP, which yields a 2SLP approximation of the dual that provides a lower bound on the optimal value of the MSLP. Although solving the corresponding 2SLP approximations exactly is intractable in general, we investigate how approximate solution approaches that have been developed for solving 2SLP can be applied to solve these approximation problems, and derive statistical upper and lower bounds on the optimal value of the MSLP. In addition to potentially yielding better policies and bounds, this approach requires many fewer assumptions than are required to obtain an explicit reformulation when using the standard static LDR approach. A computational study on two example problems demonstrates that using a two-stage LDR can yield significantly better primal policies and modestly better dual policies than using policies based on a static LDR.

关键词： multi-stage stochastic programming Linear decision rules Two-stage approximation

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