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Partially adaptive multistage stochastic programming

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32卷 3693-3728页

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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stochastic programming approaches for an energy-aware lot-sizing and sequencing problem with incentive

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第19期60卷 5746-5768页

作者： Perraudat, Antoine Dauzere-Peres, Stephane Mason, Scott Jennings Ecole Mines St Etienne CMP Dept Mfg Sci & Logist CNRS UMR 6158 LIMOS F-13541 Gardanne France BI Norwegian Business Sch Dept Accounting & Operat Management Oslo Norway Clemson Univ Coll Engn Comp & Appl Sci Clemson SC USA

Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers.

关键词： Lot sizing optimisation stochastic programming incentive-based programs energy management

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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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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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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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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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Uncertain optimization for initial disturbance of projectile for moving tank based on stochastic programming and deep learning surrogate model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第6期238卷 1898-1910页

作者： Li, Cong Yang, Guolai Wang, Xiuye Xu, Fengjie Ma, Yuze Wang, Liqun Nanjing Univ Sci & Technol Sch Mech Engn 200 Xiaoling Wei St Nanjing 290014 Jiangsu Peoples R China Nanjing Chenguang Grp Co Ltd Nanjing Peoples R China Inner Mongolia North Heavy Ind Grp Co Ltd Baotou Peoples R China

To enhance the hitting accuracy of tank with moving firing, an uncertain optimization method based on stochastic programming is adopted to reduce the initial disturbance of projectile. Firstly, the firing dynamics model of moving tank is modeled to simulate the initial disturbance of projectile. Secondly, the controllable interior ballistic parameters such as projectile structure parameters and propellant parameters are treated as random design variables. The surrogate model for the firing dynamics model of moving tank is constructed by using the neural network based on deep learning. The uncertain optimization problem is transformed into a deterministic optimization problem by stochastic programming method. Then multi-objective genetic algorithm is adopted to settle optimization model, and reasonable design interval of random design variables is obtained. Finally, a six-degree-of-freedom rigid external ballistics model is used to establish a hitting accuracy evaluation model of moving tank based on interval uncertainty analysis. Through this model, the effectiveness of the optimization method is demonstrated.

关键词： Firing dynamics of moving tank uncertainty optimization stochastic programming deep learning the initial disturbance of projectile

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