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Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年第3期21卷 507-524页

作者： Fani, Amirhossein Golroo, Amir Fahmani, Mohammadsadegh Naseri, Hamed Nejad, Fereidoon Moghadas Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran

This study aims to compare the results of stochastic and deterministic models and their corresponding optimal M&R actions considering different budget and deterioration rate uncertainty scenarios. Moreover, the impacts of uncertainty on network's scale are investigated. Therefore, this study applies Multi-Stage stochastic programming to model the uncertainty of these parameters in pavement M&R optimization. A new approach is proposed to investigate the effects of different uncertainty cases on the mentioned problem. Moreover, two pavement networks, including a large-scale and a small-scale, are utilized to evaluate the role of network size in the optimal solution to the pavement M&R optimization in the uncertainty conditions. Due to the high complexity of the large-scale M&R problem, Progressive Hedging Algorithm as an effective decomposition technique is applied. Three different uncertainty cases, including low, medium, and high are considered for the deterioration rate. Furthermore, two scenarios are taken into account for the budget: low reduction and high reduction. The results show that the probability of selecting preventive maintenance in the optimal M&R plan is increased by increasing the severity of uncertainty cases. Therefore, preventive maintenance is the most effective pavement treatment to reduce the adverse effects of budget and pavement deterioration uncertainty.

关键词： Maintenance and rehabilitation uncertainty risk pavement deterioration stochastic programming progressive hedging algorithm sensitivity analysis

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stochastic planning for transition from shopping mall parking lots to electric vehicle stations

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APPLIED ENERGY 2025年 379卷

作者： Yu, Gang Ye, Xianming Gong, Dunwei Xia, Xiaohua Univ Pretoria Dept Elect Elect & Comp Engn Pretoria 0002 South Africa Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Shandong Peoples R China

Shopping mall parking lots are promising and popular sites across nations to be transitioned into charging stations due to the nature of land availability and attractiveness to people. Sufficient charging poles contribute to satisfactory user experience, but excessive charging poles jeopardise the financial feasibility. In this study, an optimal transition planning strategy is proposed that carefully balances the number of charging poles to maximise financial returns while ensuring user convenience. For this purpose, a charging demand model at shopping malls is obtained from historical parking records. A real-time parking bay allocation strategy is obtained according to the charging requests against the available charging poles with the consideration of the maximum demand tariff. To handle the inherent uncertainty of charging demand, we formulate the optimal transition planning problem into a stochastic programming framework. In the case study, we investigate the optimal transition plan for a shopping mall parking lot in the United Kingdom. The optimal results show the transition planning method increases the annual profit by 34% and user satisfaction by 37% compared to the baseline method. The insights for the transition plans that accommodate varying factors including EV penetration, types of charging poles, and charging prices are provided.

关键词： Charging station Shopping mall parking lot stochastic programming Optimal transition planning

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Effects of uncertainty in the design parameters during the design of horizontal gravity oil, gas and water separators

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2025年第2期15卷 1-16页

作者： Chilala, Abubakar Juma Stanko, Milan Mkinga, Oras J. Univ Dar Es Salaam Dept Petr Sci & Engn Dar Es Salaam Tanzania Norwegian Univ Sci & Technol NTNU Dept Geosci & Petr Trondheim Norway

The design of bulk separators for oil, gas, and water vessel-type gravity separators significantly affects the cost of topside and subsea oil- and gas-processing installations. There are often many uncertainties during the design process, such as the separator performance, fluid properties, and variation and evolution of the inlet conditions. The industry employs mechanistic models, empirical correlations, numerical studies using Computational Fluid Dynamics, and extensive and expensive experimental campaigns with separator prototypes. However, owing to time, personnel, and information integration constraints in the design phase, design evaluation, and validation are often limited to performing comprehensive sensitivity studies. This often leads to suboptimal designs and early bottlenecks. This study proposes using stochastic mathematical programming and optimization to include the effects of uncertainties in the design parameters during the separator design process. The proposed solution involves the development of a three-phase horizontal separator design model. The model was implemented using Microsoft Excel Visual Basic for Applications. The study then considered the effects of the uncertainties in the oil, gas, and water flow rates and slug volume on the optimal design using stochastic optimization. Conservative, optimistic, and ultimate design parameters (P10, P50, and P90) were obtained for each scenario. The results of this study provide a method and tool to aid topside design teams while expanding on previous research in the literature regarding separator design.

关键词： Optimization Bulk separators stochastic programming Decision support method Uncertainties

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Intelligent parking lot power management: Augmented epsilon-constraint concept with correlation analysis

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IET RENEWABLE POWER GENERATION 2024年第15期18卷 3378-3404页

作者： Nosratabadi, Seyyed Mostafa Peivand, Ali Saadat, Amin Sirjan Univ Technol Dept Elect Engn Sirjan 78137 Iran Isfahan Univ Technol Golpayegan Coll Engn Dept Elect & Comp Engn Golpayegan Iran Univ Isfahan Fac Engn Dept Elect Engn Esfahan Iran

This paper addresses essential aspects of decision-making and management in energy resources. To achieve this, a tri-objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants' output in the proposed multi-objective optimization problem as a scenario-based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load-shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug-in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83-bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi-objective problem defined for each scenario is solved by the augmented epsilon-constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision-making method.

关键词： electric vehicles energy management systems renewable energy sources stochastic programming

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Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

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APPLIED ENERGY 2024年 372卷

作者： Fan, Yukun Liu, Weifeng Zhu, Feilin Wang, Sen Yue, Hao Zeng, Yurou Xu, Bin Zhong, Ping-an Hohai Univ Coll Hydrol & Water Resources Xikang Rd Nanjing 210098 Peoples R China Gen Inst Water Resources & Hydropower Planning & D Beijing 100120 Peoples R China Beifang Invest Design & Res Co Ltd Tianjin 300222 Peoples R China Hohai Univ Cooperat Innovat Ctr Water Safety & Hydro Sci Xikang Rd Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Xikang Rd Nanjing 210098 Peoples R China

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi -energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source -load uncertainties on optimization scheduling are key to formulating accurate and effective multi -energy scheduling plans. To address the uncertainties on both the source side and the load side in wind -solar -hydro hybrid systems, this paper proposes a multi -objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system ' s power generation and minimize the fluctuation of the remaining load. It employs Vine -Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source -load uncertainty scenarios are then reduced to representative scenarios using the K -Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a windsolar -hydro energy base in China, and the results show that: 1) The Vine -Copula -based source -side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios ' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load -side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R 2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source -side and load -side uncertainties significantly impact the optimization scheduling results of multi -en

关键词： Wind-solar-hydro hybrid system stochastic programming Multi-objective Source-load uncertainties Scenario generation

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Dynamic nested column and row generation for the Skiving Stock Problem under uncertain demand

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 201卷

作者： Karaca, Tolga Kudret Istanbul TopkapiUniv Dept Comp Engn TR-34087 Istanbul Turkiye

The pure Skiving Stock Problem aims to maximize the number of products with a threshold width by joining smaller items (e.g., production leftovers) through an optimal production plan. This study addresses the stochastic version of the problem under uncertain demand by proposing a two-stage stochastic programming model with recourse. The first-stage decision variable is the production quantity of the product by using each pattern of the skiving process, followed by the second-stage variables: the overproduction and underproduction quantities. The objective is to minimize the total expected cost, including the first and second-stage costs due to waste and holding or back-ordering costs. A novel solution methodology, incorporating a column generation procedure and the Integer L-Shaped Algorithm with Branch and Cut technique, is proposed to effectively solve this stochastic problem. We implement these two algorithms in a nested structure to combine column and row generation to obtain the optimal integer solution. To assess the efficacy and behavior of the proposed method, various experiments have been conducted on problems of escalating complexity, ranging from low to high. We further analyze and compare our solution approach with two benchmark algorithms, namely, the Progressive Hedging Algorithm and the Two-Phase Algorithm, which are the most pertinent in the literature. The proposed method demonstrates superiority over the Two-Phase Algorithm, exhibiting a relative gap of 2.97% for the benchmark problem. Finally, we present the value of the stochastic solution and the expected value of perfect information, offering a comprehensive view of decision-making under uncertainty.

关键词： stochastic programming Skiving stock problem Column generation L-shaped algorithm

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Solving a class of two-stage stochastic nonlinear integer programs using value functions

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第1期91卷 129-153页

作者： Zhang, Junlong Oezaltin, Osman Y. Trapp, Andrew C. Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA Worcester Polytech Inst WPI Business Sch Worcester MA 01609 USA

We propose a level-set characterization of the value function of a class of nonlinear integer programs with finite domain. We study the theoretical properties of this characterization and show the equivalence between the set of level-set minimal vectors and a set of non-dominated right-hand side vectors. We use these properties to develop a solution approach for two-stage nonconvex integer programs with stochastic right-hand sides. The proposed approach can solve problems with pure integer variables in both stages. We demonstrate the effectiveness of the proposed approach using a nonlinear generalized assignment problem with uncertain capacity. We also conduct computational experiments using two-stage quadratically-constrained quadratic integer programs with stochastic right-hand sides. The proposed value function-based approach can solve instances whose extensive forms are among the largest stochastic quadratic integer programs solved in the literature with respect to the number of rows and variables in extensive form, and with considerably more rows.

关键词： Nonlinear integer programming Value function stochastic programming Generalized assignment problem

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The Value of Drilling-A Chance-Constrained Optimization Approach

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MINING METALLURGY & EXPLORATION 2024年第5期41卷 2279-2289页

作者： Jeuken, Rick Forbes, Michael Univ Queensland Sch Math & Phys Brisbane Qld 4072 Australia

Managing uncertainty is a core challenge in mine planning. Mine planners often represent various planning variables, such as equipment performance and geological parameters, as random variables due to inherent uncertainties. This paper looks at geological uncertainty and its impact on mine planning. Some traditional approaches to manage this uncertainty include using conditional simulations or mathematical programming in the planning process. Drilling additional holes, despite its cost, is a common method to reduce uncertainty using additional samples to reduce deposit variance. In this paper, we first outline an ore blending optimization model which uses chance-constrained programming to manage property limit risk when selecting the order of ore feed into a processing facility. In coal mining, in tactical planning horizons, the order of coal seam removal is usually predetermined, allowing a blending model to ensure optimal feed properties. Using chance-constrained programming allows us to blend the uncertainties from geological models to maximize plant output while adhering to property constraints. We use the chance-constrained blending model to determine the value of additional information from infill drilling. The model prioritizes drilling locations that reduce uncertainty and improve blending outcomes. A case study on a coking coal mine in Queensland, Australia, demonstrates the model's application, highlighting significant improvements in blending by reducing the variance of high-quality blocks. The study concludes that targeting high-quality blocks for variance reduction can better accommodate lower-quality material, offering a more valuable approach than the traditional focus of reducing uncertainty in low-quality blocks. This approach provides insights for improving mine planning strategies and showcases the potential of chance constraints in optimizing ore blending under uncertainty.

关键词： stochastic programming Chance constraints Blending model Mining optimization Coal mining

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Rolling horizon policies for multi-stage stochastic assemble-to-order problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第14期62卷 5108-5126页

作者： Gioia, Daniele Giovanni Fadda, Edoardo Brandimarte, Paolo Politecn Torino Dept Math Sci Giuseppe Luigi Lagrange DISMA Turin TO Italy Politecn Torino Dept Math Sci Giuseppe Luigi Lagrange DISMA Corso Duca Abruzzi 24 I-10129 Turin TO Italy

Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behaviour. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterised by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling.

关键词： stochastic programming end-of-horizon effect scenario tree generation assemble-to-order production data-driven optimisation

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A Progressive Decoupling Algorithm for Minimizing the Difference of Convex and Weakly Convex Functions

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2025年第3期204卷 1-24页

作者： de Oliveira, Welington Souza, Joao Carlos de Oliveira Univ PSL Ctr Math Appl Mines Paris Sophia Antipolis France Univ Fed Piaui Dept Math CCN BR-64049550 Teresina PI Brazil

Commonly, decomposition and splitting techniques for optimization problems strongly depend on convexity. Implementable splitting methods for nonconvex and nonsmooth optimization problems are scarce and often lack convergence guarantees. Among the few exceptions is the Progressive Decoupling Algorithm (PDA), which has local convergence should convexity be elicitable. In this work, we furnish PDA with a descent test and extend the method to accommodate a broad class of nonsmooth optimization problems with non-elicitable convexity. More precisely, we focus on the problem of minimizing the difference of convex and weakly convex functions over a linear subspace. This framework covers, in particular, a family of stochastic programs with nonconvex recourse and statistical estimation problems for supervised learning.

关键词： Nonconvex optimization Decomposition Splitting stochastic programming

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