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stochastic programming for selective maintenance optimization with uncertainty in the next mission conditions

RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 241卷

作者： Ghorbani, Milad Nourelfath, Mustapha Gendreau, Michel Interuniv Res Ctr Enterprise Networks Logist & Tra Montreal PQ Canada Laval Univ Fac Sci & Engn Dept Mech Engn Quebec City PQ Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada

This paper studies selective maintenance for multi-component systems that undergo consecutive missions with scheduled breaks after each mission. To increase the likelihood of mission success, maintenance activities are performed on system components during the breaks. This study considers uncertainties in mission time and operating conditions. A two-stage stochastic programming approach is applied to model the uncertainties in the operating conditions of the next mission. Uncertainties in the operating conditions of the next mission affect the likelihood of successfully completing the mission, which may require reducing the mission time in worst-case scenarios. In the proposed two-stage model, the first stage involves making decisions on the maintenance actions required on selected components during the break. In the second stage, decisions are made regarding the completion or termination of the mission, and a penalty is assigned based on the probability of system failure during the next mission. The Sample Average Approximation algorithm, Wait-and-See, and Expected Value approaches are employed to demonstrate the efficiency of the optimal solution obtained from stochastic programming and to conduct large-scale analyses of the problem under various scenarios. Moreover, the effectiveness of the proposed model underscores the importance of incorporating uncertainty into the model.

关键词： Selective maintenance Uncertain mission duration Uncertain future operating conditions System failure cost stochastic programming

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stochastic programming based multi-arm bandit offloading strategy for internet of things

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Digital Communications and Networks 2023年第5期9卷 1200-1211页

作者： Bin Cao Tingyong Wu Xiang Bai State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu611731China China Electrics Technology Cyber Security Co..Ldt Chengdu611731China

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and *** this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is *** uncertainty would be the main obstacle to assign the task ***,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be *** order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and *** the help of stochastic programming(SP),we use the posteriori recourse to compensate for inaccurate ***,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC *** extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and *** results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.

关键词： Multi-access computing Internet of things Offloading stochastic programming Multi-arm bandit

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stochastic programming with primal-dual dynamics: a mean-field game approach

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OPTIMIZATION LETTERS 2023年第4期17卷 1005-1026页

作者： Roling, Casper T. Ramirez, Stefanny Bauso, Dario Tembine, Hamidou Univ Groningen Fac Sci & Engn Engn & Technol Inst Groningen ENTEG Nijenborgh 4 NL-9747 AG Groningen Netherlands Univ Groningen Fac Sci & Engn Jan C Willems Ctr Syst & Control Engn & Technol Inst Groningen ENTEG Nijenborgh 4 NL-9747 AG Groningen Netherlands Univ Palermo Dipartimento Ingn Palermo Italy New York Univ Abu Dhabi Learning & Game Theory Lab Abu Dhabi U Arab Emirates

This study addresses primal-dual dynamics for a stochastic programming problem for capacity network design. It is proven that consensus can be achieved on the here and now variables which represent the capacity of the network. The main contribution is a heuristic approach which involves the formulation of the problem as a mean-field game. Every agent in the mean-field game has control over its own primal-dual dynamics and seeks consensus with neighboring agents according to a communication topology. We obtain theoretical results concerning the existence of a mean-field equilibrium. Moreover, we prove that the consensus dynamics converge such that the agents agree on the capacity of the network. Lastly, we emphasize the ways in which penalties on control and state influence the dynamics of agents in the mean-field game.

关键词： Mean-field games Primal-dual dynamics stochastic programming Network optimization

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stochastic programming Method for Volatile Resource Distribution Problem 15

Stochastic Programming Method for Volatile Resource Distribu...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, SangJoon Lee, HyunYong Ko, Seok Kap Gwangju Korea Republic of

ISBN: (纸本)9798350364637

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 demands must be put into consideration for both economic and resource efficiency. We first present a practical case of such problem with a fuel cell plant waste heat energy utilization issue, then generalize this problem into a stochastic programming problem, and develop a numerical framework that can determine the distribution profile for optimal efficiency. We compare experimental results with distribution profile computed by deterministic algorithm. The stochastic method yields greater expected revenue in more than 99% of cases with average improvement of 1% over the deterministic method. We also discuss the computation time of our method. © 2024 IEEE.

关键词： Fuel cell plant Numerical method Resource distribution stochastic programming Waste heat energy

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Two-Stage Hybrid Interval stochastic programming for Sustainable Utility Systems with Energy Storage

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2025年第18期64卷 9326-9337页

作者： Yang, Kangyuan Mei, Hua Zhao, Liang East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbon emissions of the system, this study integrates the utility system with renewable energy and energy storage devices, transforming it into a sustainable utility system. To address the impact of multiscale uncertainties in renewable energy supply and steam demand on system decision optimization, this study develops a two-stage hybrid interval-stochastic programming method using stochastic intervals to model multiscale uncertainties to enhance modeling flexibility and reduce computational time. In the first stage, the capacities of renewable energy and storage systems are planned. The second stage involves solving an optimization problem under the uncertainties of renewable energy. The stochastic behavior of wind speed, solar irradiance, and steam demand is captured using scenario trees in the stochastic programming framework. In constructing the scenario tree, uncertainties are modeled by combining stochastic intervals. A risk coefficient is defined for the approximate representation of stochastic intervals to address the challenge of solving interval uncertainties while ensuring the flexibility of steam and power generation among the utility system, renewable energy system, and storage devices. Finally, a case study of a utility system in an actual ethylene chemical process validated the economic and environmental benefits of sustainable retrofitting, as well as the effectiveness of the proposed method in handling uncertainties. The optimization results indicate that the proposed model reduces carbon emissions by 5.2%, and the proposed method decreases computational time by 91% compared to stochastic programming.

关键词： stochastic programming

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Machine Learning-Based Problem Space Reduction in stochastic programming Models: An Application in Biofuel Supply Chain Network Design

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IEEE ACCESS 2024年 12卷 33852-33866页

作者： Keith, Kolton Castillo-Villar, Krystel K. Alaeddini, Adel Univ Texas San Antonio Texas Sustainable Energy Res Inst Mech Engn Dept San Antonio TX 78249 USA

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.

关键词： Biomass Biological system modeling Costs Supply chains Biofuels stochastic processes Moisture measurement Machine learning Animals Energy conversion Supervised learning supply chain design biofuels stochastic programming

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Projective Hedging Algorithms for Multistage stochastic programming, Supporting Distributed and Asynchronous Implementation

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OPERATIONS RESEARCH 2025年第1期73卷 iii-vii, 1-582, C2-C3页

作者： Eckstein, Jonathan Watson, Jean-Paul Woodruff, David L. Rutgers State Univ Rutgers Business Sch Newark & New Brunswick Dept Management Sci & Informat Syst Piscataway NJ 08854 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp & Global Secur Directorate Livermore CA 94550 USA Univ Calif Davis Grad Sch Management Davis CA 95616 USA

We propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We derive the method from a class of projective operator splitting methods fairly recently proposed by Combettes and Eckstein, significantly expanding the known applications of those methods. Our derivation assures convergence for convex problems whose feasible set is compact, subject to some standard regularity conditions and a mild "fairness" condition on subproblem selection. The meth-od's convergence guarantees are deterministic and do not require randomization, in con-trast to other proposed asynchronous variations of progressive hedging. Computational experiments described in an online appendix show the method to outperform progressive hedging on large-scale problems in a highly parallel computing environment.

关键词： stochastic programming high performance computing decomposition

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A two-stage stochastic programming model for electric substation flood mitigation prior to an imminent hurricane

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IISE TRANSACTIONS 2025年第8期57卷 920-937页

作者： Austgen, Brent Kutanoglu, Erhan Hasenbein, John J. Univ Texas Austin Operat Res & Ind Engn Program Austin TX 78712 USA

We present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electric substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation.

关键词： stochastic programming discrete optimization power grid flooding mitigation resilience risk management

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A two-stage stochastic programming model for the electricity distribution network design problem

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2025年第2期40卷 1027-1038页

作者： Ozdamar, Tugba Alegoz, Mehmet Eskisehir Tech Univ Fac Engn Dept Ind Engn TR-26555 Eskisehir Turkiye

The network that ensures the delivery of electricity to end customers, and consists of actors such as transformer centers, distribution centers, distribution transformers and field distribution boxes is called electricity distribution network. Effective design of electricity distribution networks plays an important role in terms of ensuring the continuous supply of electricity and decreasing the costs of electricity distribution companies. Motivated by this fact, in this study, we focus on an electricity distribution network consisting of transformer centers, distribution centers, distribution transformers and field distribution boxes, and propose a two-stage stochastic programing model for the location, cable and flow decisions under demand uncertainty. The proposed model is tested on a real-life case study regarding Eski & scedil;ehir, which on one hand shows the applicability of the proposed model on real-life instances, and on the other hand brings important managerial insights. As an example, computational results reveal that ignoring the uncertainties in the electricity distribution networks may bring substantial additional costs.

关键词： Supply chain network design electricity distribution network stochastic programming

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A two-stage stochastic programming model for comprehensive risk response action selection: A case study in Industry 4.0

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Hajipour, Vahid Di Caprio, Debora Santos-Arteaga, Francisco J. Amirsahami, Amirali Noshafagh, Samira Vazifeh Altinbas Univ Sch Engn & Architecture Dept Ind Engn Istanbul Turkiye Univ Trento Dept Econ & Management Via Inama 5 I-38122 Trento Italy Univ Complutense Madrid Dept Econ Financiera & Actuarial & Estadist Madrid Spain Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Univ Trento Dept Ind Engn Doctoral Programme Mat Mechatron & Syst Engn Trento Italy Islamic Azad Univ Dept Ind Engn West Tehran Branch Tehran Iran

Effective project risk management is critical in environments where both micro-level and macro-level risks are present. Traditional models often focus on micro-level risks, neglecting broader macroeconomic uncertainties such as geopolitical instability and supply chain disruptions. This research introduces a two-stage stochastic programming model designed to optimize the selection of Risk Response Actions (RRAs) under uncertainty while addressing both types of risk. The model incorporates "here-and-now" decisions at the planning stage and "waitand-see" decisions as uncertainties unfold, enabling adaptive risk management throughout the project lifecycle. To solve the model efficiently, we employ an evolutionary algorithm combined with Sample Average Approximation (SAA) to handle the computational complexity of multiple scenarios. The model is applied to a real-world case study involving the integration of IoT and ERP systems in a smart factory in Iran, a project characterized by significant macroeconomic and geopolitical risks. Our key contribution lies in providing a comprehensive risk response strategy selection model that simultaneously addresses micro- and macro-level risks while incorporating strategic flexibility through outsourcing decisions. The results demonstrate that our model outperforms traditional deterministic models, offering enhanced resilience against macro-level risks and improved project performance under uncertainty. These findings provide valuable insights for project managers aiming to increase resilience and adaptability in volatile environments. By integrating both internal and external risk factors, our model offers a robust tool for managing complex projects, enhancing decision-making and project outcomes in uncertain conditions.

关键词： Risk management Project management Optimization stochastic programming Genetic algorithm Industry 4.0

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