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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 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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Partially adaptive multistage stochastic programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期321卷 192-207页

作者： Kayacik, Sezen Ece Basciftci, Beste Schrotenboer, Albert H. Ursavas, Evrim Univ Groningen Fac Econ & Business Dept Operat Groningen Netherlands Univ Iowa Tippie Coll Business Dept Business Analyt Iowa City IA 52242 USA Eindhoven Univ Technol Sch Ind Engn Operat Planning Accounting & Control Grp Eindhoven Netherlands

Multistage stochastic programming is a powerful tool allowing decision-makers to revise their decisions at each stage based on the realized uncertainty. However, organizations are not able to be fully flexible, as decisions cannot be revised too frequently in practice. Consequently, decision commitment becomes crucial to ensure that initially made decisions remain unchanged for a certain period of time. This paper introduces partially adaptive multistage stochastic programming, a new optimization paradigm that strikes an optimal balance between decision flexibility and commitment by determining the best stages to revise decisions depending on the allowed level of flexibility. We introduce a novel mathematical formulation and theoretical properties eliminating certain constraint sets. Furthermore, we develop a decomposition method that effectively handles mixed-integer partially adaptive multistage programs by adapting the integer L-shaped method and Benders decomposition. Computational experiments on stochastic lot-sizing and generation expansion planning problems show substantial advantages attained through optimal selections of revision times when flexibility is limited, while demonstrating computational efficiency attained by employing the proposed properties and solution methodology. By adhering to these optimal revision times, organizations can achieve performance levels comparable to fully flexible settings.

关键词： stochastic programming Multistage stochastic optimization Mixed-integer programming Lot-sizing Generation expansion planning

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A multi-stage stochastic programming model for multi-mission selective maintenance optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 254卷

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

This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each mission. Within each maintenance break, various optional actions ranging from replacements which are perfect to imperfect and also minimal repairs can be chosen for individual components. Evaluating the probabilities of successful future mission accounts for uncertainties associated with component operational conditions. The selective maintenance problem is formulated as a nonlinear mixed-integer model for optimization, and computational challenges are addressed using the progressive hedging algorithm. Numerical examples validate the new proposed model and illustrate the benefits of the model by estimating a more realistic reliability level and lower maintenance cost.

关键词： Selective maintenance stochastic programming Uncertain operating conditions Progressive hedging algorithm

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A stochastic programming approach for the operational fleet composition problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第0期

作者： Fachini, Ramon F. Bicalho, Luis Henrique Souza, Vitor A. A. Negrotto, Daniel Mercado Libre Shipping Optimizat Team SOT 3003 Nacoes Unidas Ave BR-06233200 Osasco SP Brazil

This paper introduces the operational fleet composition problem with stochastic demands (OFCP-SD), which is commonly faced by e-commerce companies in the context of short-term capacity planning for freight transportation. We propose a two-stage stochastic programming formulation, in which the planned fleet size and mix decisions constitute the first stage, while recourse actions are taken in the second stage to hire extra vehicles or to make vehicle cancellations according to the observed demands. Hiring extra vehicles incurs additional costs, while vehicle cancellations involve financial restitutions. The objective is to minimize the fleet overall cost, which comprises the first-stage planned cost as well as the expected cost or restitution stemming from the fleet adjustments made in the second stage. A scenario generation procedure is devised, and a variable-fixing matheuristic is suggested for the OFCP-SD. A case study conducted within the Brazilian middle-mile operation of the leading e-commerce company in Latin America shows the advantages of explicitly modeling the stochastic demands and underlines the benefits of the proposed approach for the business. Compared to a simplified deterministic approach, the use of the OFCP-SD demonstrated a yearly potential cost avoidance of more than USD 2.5 million as well as an annual reduction of more than 20 thousand pallets transported by means of extra vehicles.

关键词： freight transportation fleet composition problem demand uncertainty stochastic programming matheuristic

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A comprehensive stochastic programming model for transfer synchronization in transit networks

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COMPUTERS & OPERATIONS RESEARCH 2025年 179卷

作者： Ansarilari, Zahra Bodur, Merve Shalaby, Amer Univ Toronto Dept Civil & Mineral Engn Toronto ON Canada Univ Edinburgh Sch Math Edinburgh Scotland Univ Edinburgh Maxwell Inst Math Sci Edinburgh Scotland

We investigate the stochastic transfer synchronization problem, which seeks to synchronize the timetables of different routes in a transit network to reduce transfer waiting times, delay times, and unnecessary in-vehicle times. We present a sophisticated two-stage stochastic mixed-integer programming model that takes into account variability in passenger walking times between bus stops, bus running times, dwell times, and demand uncertainty. Our model incorporates new features related to dwell time determination by considering passenger arrival patterns at bus stops which have been neglected in the literature on transfer synchronization and timetabling. We solve a sample average approximation of our model using a problem-based scenario reduction approach, and the progressive hedging algorithm. As a proof of concept, our computational experiments on instances using transfer nodes in the City of Toronto, with a mixture of low- and high-frequency routes, demonstrate the potential advantages of the proposed model. Our findings highlight the necessity and value of incorporating stochasticity in transfer-based timetabling models.

关键词： Transfer synchronization Timetabling Bus running time and passenger demand uncertainty Passenger arrival patterns Dwell time determination stochastic programming

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Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期321卷 925-941页

作者： Siddig, Murwan Song, Yongjia Univ Florida Dept Ind & Syst Engn Gainesville FL USA Clemson Univ Dept Ind Engn Clemson SC 29634 USA

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane's attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane's attributes (intensity and location) at the terminal stage, which corresponds to the hurricane's landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane's attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

关键词： OR in disaster relief stochastic programming Hurricanes Logistics planning

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Piecewise affine decision rules for contextual chance-constrained stochastic programming

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OPERATIONS RESEARCH LETTERS 2025年 61卷

作者： Qin, Junhao Zou, Ziyi Liu, Liu Liu, Yongchao Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Leicester Int Inst Panjin 124221 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China

This paper proposes a contextual chance-constrained programming model (CCCP), where a measurable function from the feature space to the decision space is to be optimized under the chance constraint. We present a tractable approximation of CCCP by the piecewise affine decision rule (PADR) method. We quantify the approximation results from two aspects: the gap of optimal values and the feasibility of the approximate solutions. Finally, numerical tests are conducted to verify the effectiveness of the proposed methods.

关键词： stochastic programming Chance-constrained Contextual stochastic optimization Piecewise affine function Contextual information

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A stochastic programming model for designing bus bridging services under metro disruptions

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2025年第1期17卷 103-118页

作者： Zhen, Lu Du, Xueqin Li, Haolin Wu, Zanyang Shanghai Univ Sch Management Shanghai Peoples R China Wuxi Metro Grp Co Ltd Wuxi Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

With the growing reliance on urban metro networks, any accidental disruption can lead to rapid degradation and significant economic losses. Bus bridging services are common and efficient ways to minimize such adverse impacts. In this study, we investigate the problem of designing bus bridging services in response to unexpected metro disruptions, and propose a routing strategy with multiple bridging routes. In particular, to respond to uncertain factors such as passenger arrivals and bus travel times in the disruption environment, we develop a two-stage stochastic programming model for the collaborative optimization of bus bridging routes, schedules, and passenger assignments. To solve the computational challenges arising with the proposed model, a tailored tabu search algorithm is developed. Finally, several sets of numerical experiments are conducted and experimental results reveal that our proposed routing strategy can effectively improve the service level for the affected passengers during metro disruptions.

关键词： Urban metro system bus bridging schedule passenger assignment stochastic programming tabu search algorithm

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