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The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader–follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed. [ABSTRACT FROM AUTHOR]

In this work, we study the optimal control of stochastic Burgers equation perturbed by Gaussian and Lévy-type noises with distributed control process acting on the state equation. We use dynamic programming approach for the feedback synthesis to obtain an infinite-dimensional second-order Hamilton–Jacobi–Bellman (HJB) equation consisting of an integro-differential operator with Lévy measure associated with the stochastic control problem. Using the regularizing properties of the transition semigroup corresponding to the stochastic Burgers equation and compactness arguments, we solve the HJB equation and the resultant feedback control problem. [ABSTRACT FROM AUTHOR]

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price. • The collection price uncertainty impact is considered on biomass supply chain. • A combined biomass supply chain design is suggested. • Facility location, resource allocation and vehicle routing problem are considered. • A two-stage stochastic mixed integer programming model is established. • A hybrid simulated annealing algorithm is developed. [ABSTRACT FROM AUTHOR]

Due to favourable economic and environmental concerns, reduced power losses, and technological advancements in data science, large-scale renewable energy sources (RESs) are being inserted into smart microgrids (MGs). Generally, these resources are inserted at the distribution level and feed consumers locally with better reliability. The planning and stable operation of the power networks are complicated at numerous levels as a result of large-scale renewable integration, as the infeed from renewable sources like wind and solar is highly intermittent and uncertain, significantly affecting the resilient, intelligent, and optimal operation of the power system. The creation of scenarios is an essential step in the planning and stable operation of renewable-rich power systems. Scenarios can be generated based on seasonal events to mitigate growing complexity and intermittency. In this paper, time-series solar data is used for forecasting solar irradiance using long-short-term memory (LSTM). Besides, a cost-based optimisation model has been developed by using stochastic programming to obtain both the optimal sizing and optimal switching sequence of distributed units as well as the optimal number of required units. The stochastic nature of solar and load demand is considered in beta (β ) distribution. The objective function formulated covers different costs, like capital, operational, and switching costs associated with required units. The running cost incorporated with the capital cost gives us a broader design for planning the MG, thus making it both planning and operational problem. The entire proposed MG planning problem has been developed in a Python and MATLAB ® environment. [ABSTRACT FROM AUTHOR]

This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden. [ABSTRACT FROM AUTHOR]

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions. [ABSTRACT FROM AUTHOR]

Carbon capture, utilization, and storage (CCUS) is a crucial part of the energy industry nowadays, aiming to reduce the overall carbon emission into the environment. One solution to CCUS is via the means of CO 2 enhanced recovery processes in a depleted oil reservoir. In such a case, life-cycle production optimization plays a crucial component, referring to optimizing a production-driven objective function via varying well controls during a reservoir's lifetime. One challenge is to obtain the optimal cash flow while trying to maintain the maximum CO 2 storage. Another challenge is the nonlinear constraints (such as field liquid production rate) which need to be honored due to the capacity of the processing facilities. This study presents an application of a stochastic gradient-based framework to solve the CO 2 storage multi-objective optimization problem. Our study focuses on carbon capture and storage via the means of nonlinearly constrained production optimization workflow for a CO 2 enhanced recovery process, in which we aim to bi-objectively maximize both the net-present-value (NPV) and the net present carbon tax credits (NPCTC). The main framework used in this work is line-search sequential quadratic programming (LS-SQP) with stochastic simplex approximated gradients (StoSAG). We demonstrate the performance and results of the algorithmic framework in a field-scale realistic problem. The case study being investigated is a multiphase flow Brugge model under CO 2 injection, simulated using a commercial compositional reservoir simulator. Results show that the LS-SQP algorithm with StoSAG gradients performs computationally efficiently and effectively in handling nonlinear state constraints imposed onto the problem. The workflow successfully solves both the single-objective and the multi-objective optimization problems with minimal and acceptable constraint violations. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective optimization problem, showing the trade-off between the two objectives NPV and NPCTC. We have demonstrated an approach to the carbon capture, utilization, and storage (CCUS) in the context of multi-objective production optimization of a CO 2 enhanced recovery process for a field-scale realistic reservoir model. The algorithmic framework used in this study has proven to be computationally effective on the problem and especially useful when utilized in conjunction with commercial flow simulators that lack the capability of computing adjoint-based gradients. [ABSTRACT FROM AUTHOR]

Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, the application of optimality theory, has been comparatively neglected in collective animal behaviour. This article seeks to address this imbalance, by outlining an evolutionary theory framework for the discipline. The application of optimality theory to collective animal behaviour requires a number of questions to be addressed. First, what is the correct quantity to optimize? This can be achieved via a combination of considering the organisms' life history, alongside tools such as statistical decision theory and stochastic dynamic programming. Second, what mechanism is appropriate for optimal behaviour? This involves ensuring that models are self-consistent rather than assuming parameter values. Third, at what level of selection does optimization act? Selection acts on the individual except in very particular circumstances, yet collective animal behaviour phenomena are group level, thus introducing a risk of confusing at what level adaptive properties emerge. This article presents examples under each of the three questions, as well as discussing mismatches between theory and observation. In doing so, it is hoped that collective animal behaviour fully inherits the tools and philosophy of its parent discipline of behavioural ecology. • We undertake a review of collective animal behaviour using Tinbergen's four questions. • We thus identify a lack of optimality theory in collective animal behaviour. • We propose three new questions for optimality theory in collective animal behaviour. • Using these questions we review the literature for successes and missed opportunities. [ABSTRACT FROM AUTHOR]

Linear-scaling techniques for Kohn–Sham density functional theory are essential to describe the ground state properties of extended systems. Still, these techniques often rely on the localization of the density matrix or accurate embedding approaches, limiting their applicability. In contrast, stochastic density functional theory (sDFT) achieves linear- and sub-linear scaling by statistically sampling the ground state density without relying on embedding or imposing localization. In return, ground state observables, such as the forces on the nuclei, fluctuate in sDFT, making optimizing the nuclear structure a highly non-trivial problem. In this work, we combine the most recent noise-reduction schemes for sDFT with stochastic optimization algorithms to perform structure optimization within sDFT. We compare the performance of the stochastic gradient descent approach and its variations (stochastic gradient descent with momentum) with stochastic optimization techniques that rely on the Hessian, such as the stochastic Broyden–Fletcher–Goldfarb–Shanno algorithm. We further provide a detailed assessment of the computational efficiency and its dependence on the optimization parameters of each method for determining the ground state structure of bulk silicon with varying supercell dimensions. [ABSTRACT FROM AUTHOR]

The main concern is the uncertainty in the real-world solid transportation problem. This study examines a supply, demand, and conveyance capacity-based multi-choice solid stochastic multi-objective transportation problem (MCSS-MOTP). Due to uncertainty, the concrete objective function coefficients of the proposed model are of multivariate type. Furthermore, the parameters of the constraints are treated as independent multivariate random variables with normal distribution. First, a Newton divided difference method-based interpolation polynomial is described that extends an interpolation polynomial using practical properties at non-negative integer nodes to deal with any multiple-choice parameter. Second, the probabilistic constraints are converted into precise ones utilizing a stochastic programming approach. In the end, ranking procedure was used to compare the existing approach with the old models. The proposed model’s applicability was confirmed using a numerical example. [ABSTRACT FROM AUTHOR]

The failures of high-consequence systems can cause serious harm to humans, including loss of human health, life security, finance, and even social chaos. To protect high-consequence systems, both optimal system design and maintenance activities contribute to improving system reliability and social safety. The existing works generally optimize these two problems sequentially and assume that the degradation process of components is precisely known. However, sequential optimization often results in significant losses due to redundancies, and such a presumption usually cannot be guaranteed in practice, due to limited historical data or a lack of expert knowledge, referred to as epistemic uncertainty. To fill this gap, in this article, we consider an integrated optimization of system design and maintenance scheduling for multi-state high-consequence systems in which the component's degradation is known with limited distributional information. To address this issue, we utilize the framework of distributionally robust optimization to provide a risk-averse decision to decision-makers even under the worst realizations of random parameters, and develop a two-stage integer distributionally robust model with moment-based ambiguity set to determine the system design and maintenance scheduling simultaneously. The proposed model can be converted to a tractable approximation as an integer linear stochastic programming problem. In order to solve large-scale problems, we develop a sample-based adaptive large neighborhood search algorithm to find the optimal system designs. In the numerical experiments, we present a case study on feedwater heating systems in nuclear power plants and demonstrate that an integrated optimization consideration creates significant benefits in profitability. We also present the out-of-sample performance of the distributionally robust design to avoid extreme risk. [ABSTRACT FROM AUTHOR]

Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand. [ABSTRACT FROM AUTHOR]

Changes in demand, various hydrological inputs, and environmental stressors are among the issues that reservoir managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effectively represent a real-world system using traditional methods such as dynamic programming and stochastic dynamic programming for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which means the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function. Deep reinforcement learning (DRL) is an intelligent approach to overcome the curses of stochastic optimization problems for reservoir operation policy decisions. To our knowledge, this study is the first attempt that examines various novel DRL continuous-action policy gradient methods, including deep deterministic policy gradients, twin delayed DDPG (TD3), and two different versions of Soft Actor-Critic (SAC18 and SAC19) for optimizing reservoir operation policy. In this study, multiple DRL techniques were implemented to find an optimal operation policy for Folsom Reservoir in California. The reservoir system supplies agricultural, municipal, hydropower, and environmental flow demands and flood control operations to the City of Sacramento. Analysis suggests that the TD3 and SAC are robust to meet the Folsom Reservoir's demands and optimize reservoir operation policies. Experiments on continuous-action spaces of reservoir policy decisions demonstrated that the DRL techniques can efficiently learn strategic policies in spaces and can overcome the curse of dimensionality and modeling. [ABSTRACT FROM AUTHOR]

We propose logarithmic-barrier decomposition-based interior-point algorithms for solving two-stage stochastic quadratically constrained convex quadratic programming problems in a Hilbert space. We prove the polynomial complexity of the proposed algorithms, and show that this complexity is independent on the choice of the Hilbert space, and hence it coincides with the best-known complexity estimates in the finite-dimensional case. We also apply our results on a concrete example from the stochastic control theory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While hydrogen energy is increasingly incorporated into energy hubs supplying the fuel cell electric vehicles, the question arose as to whether the inflexible hydrogen networks can provide a reliable supply for sustainable transportation. This study proposes a reliability contract method to capture the economic benefits of differentiated reliability according to the preferences of energy hubs for hydrogen outages. The innovative method divides the hydrogen network into multiple zones to construct the premiums and reimbursements for the reliability contract. According to the key factors of uncertainty in the energy hub operation problem, risk management and net premium principle, this research identifies the effective application of the zone-based reliability contract to hydrogen networks by using a stochastic programming model. Simulations implemented on a typical structure of hydrogen network with 5.2 h outage duration per year shows that the zone division of the proposed reliability contract effectively reduce the outage to 3.1 h. • Reliability contract in hydrogen network can guarantee a sustainable transportation. • Multiple zones are created to construct the premiums and reimbursements. • Conditional value at risk method calculates the expected hydrogen demand. • Reinforcement learning can allocate the zones of hydrogen network effectively. [ABSTRACT FROM AUTHOR]

Sample average approximation (SAA), the standard approach to stochastic mixed-integer programming, does not provide guidance for cases with limited computational budgets. In such settings, variance reduction is critical in identifying good decisions. This paper explores two closely related ensemble methods to determine effective decisions with a probabilistic guarantee. (a) The first approach recommends a decision by coordinating aggregation in the space of decisions as well as aggregation of objective values. This combination of aggregation methods generalizes the bagging method and the "compromise decision" of stochastic linear programming. Combining these concepts, we propose a stopping rule that provides an upper bound on the probability of early termination. (b) The second approach applies efficient computational budget allocation for objective function evaluation and contributes to identifying the best solution with a predicted lower bound on the probability of correct selection. It also reduces the variance of the upper bound estimate at optimality. Furthermore, it adaptively selects the evaluation sample size. Both approaches provide approximately optimal solutions even in cases with a huge number of scenarios, especially when scenarios are generated by using oracles/simulators. Finally, we demonstrate the effectiveness of these methods via extensive computational results for "megascale" (extremely large scale) stochastic facility location problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by The Office of Naval Research [Grant N00014-20-1-2077] and the Air Force Office of Scientific Research [Grant FA9550-20-1-0006]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0324) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0324). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

It is critical to estimate the workforce requirements for the production of blocks in shipbuilding. In this study, the number of workforce (man-day) required for the production of a passenger ship's double bottom block was estimated. Initially, the production of the block was observed, and the average working performance of the mounting, welding, and grinding workers was recorded. Block drawings were examined and the work required was calculated. The amount of work increased, depending on any revisions required due to incorrect or incomplete designs. The average working performance of an employee is uncertain due to environmental factors, including the weather and working conditions, as well as health (both physical and mental). A two-stage stochastic programming model with recourse was established to estimate man-day required and a Sample Average Approximation (SAA) technique was used to obtain a near-optimum solution. The results of the study were compared with shipyard records and an agreement of approximately 90% was achieved. [ABSTRACT FROM AUTHOR]

Our article offers insights on the role of operational research (OR) in understanding financial and economic systems' risks and dynamics. It presents the latest methods in OR to address risks and uncertainties in these systems, covering topics such as options pricing, portfolio optimization, banking resilience, and the analysis of financial and economic co-movements. The included studies utilize advanced analytical tools, like stochastic programming, decision analysis, and machine learning, to create robust models for complex systems. These contributions aim to be valuable for academics, practitioners, policymakers, and regulators in the field. [ABSTRACT FROM AUTHOR]

Uncertain factors can significantly reduce the applicability of production plans and the production stability of enterprises. Therefore, considering the uncertain factors caused by external demands and internal capabilities, the impact of production in advance or backorder is analyzed, and the cost of backorder and loss of sales is determined. Moreover, a multi-objective stochastic programming model for aggregate production planning (APP) problem that minimizes the expected chance cost and employee instability is constructed considering external demand and internal capabilities as random variables. At a certain level of confidence, the opportunity constraints are transformed. In addition, combined with a local search strategy, two stochastic hybrid genetic algorithm-particle swarm optimization algorithms based on stage and multiple population strategies are designed to solve this model. Sensitivity analysis is performed on different confidence levels by using numerical examples, and numerical experiments show that the two hybrid algorithms proposed by this paper can effectively improve the solving efficiency. [ABSTRACT FROM AUTHOR]

Thus far in the available literature, capability-based distributed layout (CBDL) design approaches were only developed under certain environments. Indeed, uncertainties embedded in the machine unavailability (or random machine breakdowns), product demands, and process flow data were not considered by the previous studies to achieve a robust CBDL design. However, many real-life facility layout design applications may involve different types of uncertainties simultaneously, like fuzziness and stochasticity. Based on this motivation, for the first time in the literature, this paper introduces a novel robust capability-based distributed layout (R-CBDL) design problem under a mixed fuzzy-stochastic environment. First, a new fuzzy-stochastic optimization model of the R-CBDL design problem is developed by considering the random machine breakdowns and fuzzy demand/process flow data. Then, a hybrid solution approach based on a chance-constrained stochastic programming technique with a well-known interactive fuzzy resolution method is proposed. Thus, the random machine breakdowns and fuzzy part flow rates among different machining capabilities could be easily handled via the proposed approach. Fortunately, the proposed approach can also generate various risky and risk-free robust layout design alternatives under different probabilistic scenarios and uncertainty levels (α-cuts) according to the facility designer's risk attitude. To show the validity and applicability of the proposed R-CBDL problem and hybrid solution approach, an extensive computational study with comparative analysis is first presented based on an illustrative numerical example under different machine capability overlap cases and probability distributions. Then, the performance of the proposed approach is also tested on a real-life cellular manufacturing system of a company. The computational experiments have shown that the proposed approach can accomplish more efficient robust layout design alternatives with on average 24.5% better total expected layout score when compared to the existing cellular layout of the manufacturing company. [ABSTRACT FROM AUTHOR]

Quality‐based design optimization is a process consisting of factor identification, finding an empirical/physical model between responses and factors, and finding a factor setting leading to the best quality levels. Response surface methodology is a well‐grounded data‐driven approach that applies a collection of statistical‐mathematical techniques to achieve the design requirements. Today's complex systems are usually affected by nuisance factors and have more than one output characteristic with some degree of correlation. This study aims to develop two‐stage stochastic programming for a multi‐response optimization model based on the degree of conformance as the most comprehensive metric for such design problems. The proposed approach can include the model imprecision in the optimization procedure using the probabilistic properties of the estimated parameters. This method would be applied in highly sensitive processes or costly operations that require a more accurate setting to avoid extra reworks or scraps. A simulated annealing algorithm with a boosted scenario checking modification has been applied to cope with the expensive computations for problem instances with a large sample size of uncertain parameters. The proposed model has also analyzed two cases from the literature, and the results support its superiority compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

This study unveils a groundbreaking technique leveraging the superposition of electric field vectors to manipulate liquid crystals (LCs). Demonstrated through a simple configuration of four independent electrodes at the corners of a rectangular enclosure, notably, this configuration can be further simplified or modified as needed, showcasing the versatility of the approach. Significantly, the design showcased in the paper eliminates the need for an alignment layer, highlighting the versatility of the method. Through nuanced adjustments in waveforms, amplitudes, frequencies, and phases in AC or DC from these electrodes, precise control over LC shape deformation and dynamic phase transformation is achieved in both temporal and spatial dimensions. In contrast to traditional methods, the approach presented here abolishes alignment layers and intricate electrode-array systems, opting for a streamlined configuration with varying AC frequencies and DC electric signals. This innovative methodology, founded on simplified governing equations from Q-tensor hydrodynamics theory, demonstrates true 3D control over LCs, displaying efficiency in electrode usage beyond current arrays. The study's contributions extend to temporal control emphasis, superposition techniques, and the elimination of fixed electrodes, promising unprecedented possibilities for programming LC materials and advancing the field of programmable LC devices. [ABSTRACT FROM AUTHOR]

Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting. [ABSTRACT FROM AUTHOR]

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

This paper presents a new formulation for solving the expansion planning of transmission lines and energy storage systems while considering the integration of electricity and gas networks. The proposed model is a bi‐level stochastic planning model. It involves transmission and battery expansion planning at one level, and gas network modeling at the other. The study addresses the impact of high penetration of renewable resources and security constraints on both the electricity and gas networks within the context of network expansion planning. The proposed model is a stochastic mixed‐integer linear programming model at both levels, and its challenging solution is achieved through reformulation and decomposition methods. Two experimental networks are analyzed: a 6‐node network and the IEEE RTS 24‐bus network for the electricity network, coupled with 5‐node and 10‐node gas network systems. The results demonstrate the efficiency of the proposed model. Simulation results indicate that the proposed model is highly effective in scenarios where power and gas network lines are disconnected, preventing load shedding even when the integrated network lines are disconnected. [ABSTRACT FROM AUTHOR]

The integrated optimization of stope design and underground mine production scheduling is an approach that has been shown to effectively leverage the synergies among these two underground mine planning components to generate truly optimal stope layouts and extraction sequences. The existing stochastic integrated methods, however, do not include several elements of a mining complex, such as stockpiles, due to the computational complexity and non-linearity that they might add to the optimization of the long-term mine plan. Additionally, sequential simulation methods that rely on two-point statistics and Gaussian distribution assumptions are commonly used to generate the input realizations of the mineral deposit. These methods, however, are not able to properly characterize complex spatial geometries or the high-grade connectivity of non-Gaussian and non-linear natural phenomena. The present work proposes an extension of previous developments on the integrated stope design and underground mine scheduling optimization through an expanded stochastic integer programming formulation that incorporates long-term stockpiling decisions. An application of the proposed method at an operating underground copper mine compares the cases in which the geological simulated orebody models are based on high-order and Gaussian sequential simulation methods. The extraction sequence and related final stope design are shown to be physically different. It is seen that the optimization process takes advantage of the better representation of high-grade connectivity when high-order sequential simulations are used, by targeting the areas with grades that follow the mill's blending requirements and by making less use of the stockpiles. Overall, a 4% higher copper metal production and a resultant 6% higher net present value are observed when high-order sequential simulations are used. [ABSTRACT FROM AUTHOR]

Due to a wide variety of real-world constraints, proper project portfolio selection is a critical issue for project-oriented organizations. In this paper, a bi-objective stochastic mixed-integer linear programming model is developed to cope with the project selection and scheduling problem in the presence of greenhouse gas emissions, and non-hazardous/hazardous wastes regulatory restrictions. Moreover, reinvesting proceeds of projects as well as loans are allowed to finance projects over the planning horizon. The proposed model maximizes the net present value of the expected project portfolio's terminal wealth under uncertain conditions, as well as the sustainability score of the project portfolio, simultaneously. The sustainability score is calculated by one of the recent multi-criteria decision-making methods, SECA, based on seven qualitative sustainability indicators and by solving a non-linear optimization model. To assess the performance of the proposed model, a case study of eighteen industrial projects is applied. Since the duration of industrial projects is usually uncertain, the proposed model is reformulated as a scenario-based stochastic programming model. Furthermore, the CPLEX solver and Branch and Benders algorithm are used to solve the problem. Results show that the Branch and Benders algorithm is much more efficient than the CPLEX solver. Results show that increasing the carbon and landfill tax rates is not always an appropriate decision made by policymakers to control various types of emissions. Such decisions may not only make the projects less attractive for investment but also, do not significantly reduce the negative environmental effects, which decreases sustainability in both economic and environmental dimensions. This highlights the importance of considering each problem's attitudes for setting regulations where copying does not always create the same solutions for sustainability issues. [ABSTRACT FROM AUTHOR]

This paper proposes a modified conditional value‐at‐risk interval two‐stage stochastic programming coordination model (MCITSP) for water allocation and illustrates its advantages in risk aversion and pollution control. We analyze its performance in maintaining the equity of water use in various sectors, which is specifically reflected in the water satisfaction of multiple users. In this paper, the MCITSP model and original ITSP model are applied to the case of the Hanjiang River Basin, and three scenarios of water availability are set up to provide theoretical support for water allocation. Our results show that the MCITSP model with a higher risk coefficient has a stronger ability to avoid risks. The MCITSP model simultaneously controls pollutant discharge and guarantees economic benefits, making it superior to the ITSP model under different scenarios. Water shortages primarily affect the agricultural sector, due to its high water demand and low economic value, and the MCITSP model plays a positive role in maintaining equity and coordinating water conflicts among multiple users. Managers can choose appropriate model parameters according to their preferences to formulate more reasonable decisions. [ABSTRACT FROM AUTHOR]

The need for reserve provision in power systems is growing progressively due to the increase in wind power penetration level. This paper aimed to present a model from the ISO point of view for using demand-side resources as flexible resources that are aggregated as a demand-side provider (DSP). The DSP consists of demand response aggregator as grid backup and energy storage systems plus plug-in electric vehicles (PEVs) parking lot aggregators as fast reserve. In this regard, a two-stage stochastic programming model has been presented for energy and reserve determination in the unit commitment problem in the form of mixed integer linear programming (MILP). In this study, the incentive-based model of the capacity market program has been employed to achieve a certain level of demand-side response. Meanwhile, the Weibull probability distribution function and truncated Gaussian distribution have been applied to generate wind speed scenarios and model the uncertainties of PEVs' behavior, respectively. The results obtained in this study demonstrate that the simultaneous scheduling of energy sources and demand-side aggregators for energy supply as well as reserve has led to the reduction of wind energy uncertainty and improved system operational conditions. Also, increase in the number of PEVs present in the parking lot, in addition to reducing the operation costs, leads to increased grid reserve level as well as considerable reduction of wind power spillage. [ABSTRACT FROM AUTHOR]

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency. [ABSTRACT FROM AUTHOR]

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively. [ABSTRACT FROM AUTHOR]

Industrial Symbiosis (IS) networks are structures built by volunteer companies with the aim of exchanging unused or residual resources, benefiting all participating companies. The profitability of these volunteer companies is critical as it affects the sustainability of these networks. Fluctuations in a participating company's production level can potentially disrupt its network by altering the quantity and availability of wastes and by-products. In light of these considerations, we analyse the impact of fluctuations in demand for final products of the companies on company profitability, and waste and by-product usage. For this purpose, we formulated a two-stage stochastic programming model and solved it using the Sample Average Approximation (SAA) method. We tested our model on a theoretical IS network comprising companies in the forest products industry. The results demonstrate that companies in the network keep exchanging by-products and remain profitable despite uncertainties in demand. Consequently, we conclude that the established network exhibits resilience to demand fluctuations, which is an important aspect of its sustainability. • Industrial symbiosis is an important tool that aims at the exchange of waste and by-products. • We proposed a two-stage stochastic model to analyze an industrial symbiosis network under demand fluctuations. • The stochastic model is solved using the Sample Average Approximation. • Results revealed that fluctuations in demand affect the company's decision to stay in the network. • This situation could affect the survival and sustainability of the network. [ABSTRACT FROM AUTHOR]

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has severely increased the concerns over the resilience of power systems. Extreme events may lead to the failure of a large number of system components and result in interruption of supply to lots of consumers. In this research, the main objective is to use and assess the potential of large industrial energy hubs (EHs) in resilience improvement of power systems. Mobile energy storage and demand response programs are also used to decrease involuntary demand shed in system and enhance system resilience. A stochastic mixed-integer linear programming model is developed considering the uncertainties in damaged transmission lines, hurricane time and repair time. Case study is a modified IEEE 24-bus power system with EHs. The studied EH receives gas from a gas network, exchanges power with power system, includes combined heat and power (CHP), generator, electrolyzer, boiler, thermal storage and hydrogen storage system and feeds electric, heat and hydrogen demands. Expected load not supplied (ELNS) is used as resilience metric. The results approve significant impact of EH in improvement of power system resilience. In scenarios in which the bus connected to EH is isolated, EH supplies the power system demand located at that bus and thereby improves system resilience; on the other hand, in other scenarios, EH typically behaves as an electricity consumer of power system. According to the results, mobile energy storage causes a 2.4% improvement in ELNS. The results also show that demand response program improves ELNS by 7.1%. • Energy hub and mobile storage are used for resilience enhancement of power systems. • Uncertainties in damaged lines, hurricane time and repair time are considered. • The effect of responsive demands on power system resilience is evaluated. • The effect of EH generator size on power system resilience is assessed. [ABSTRACT FROM AUTHOR]

We show that the multistage stochastic linear problem (MSLP) with an arbitrary cost distribution is equivalent to an MSLP on a finite scenario tree. We establish this exact quantization result by analyzing the polyhedral structure of MSLPs. In particular, we show that the expected cost-to-go functions are polyhedral and affine on the cells of a chamber complex, which is independent of the cost distribution. This leads to new complexity results, showing that MSLP becomes polynomial when certain parameters are fixed. [ABSTRACT FROM AUTHOR]

In the era of e-commerce, a new business mode—e-grocery platform has become popular in recent years. An e-grocery platform need not own and operate shops. The platform assigns the orders that customers release on the platform to the part-time couriers; then the couriers go to some nearby shops, buy the commodities required by orders, and deliver them to the orders' shipping address. This study proposes a mathematical model on selecting and accepting customer orders, assigning the accepted orders to couriers, selecting shops for buying each order's required commodities, and routing each courier among her/his visited shops and assigned order's shipping address. Moreover, the uncertain arrivals of future orders is taken into account by the proposed model. For solving the proposed two-stage stochastic integer programming model with the integer recourse function, this study also designs a novel algorithm based on column generation. Based on the real data from platform operators in Shanghai and Hangzhou, numerical experiments are conducted to validate the efficiency of the proposed algorithm. Sensitivity analysis based on the real data are also conducted to derive potentially useful managerial implications for practitioners who operate third-party e-grocery platforms. [ABSTRACT FROM AUTHOR]

We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim at solving problems where the second stage is demanding. Our core idea is to gain large reductions in online solution time, while incurring small reductions in first-stage solution accuracy by substituting the exact second-stage solutions with fast, yet accurate, supervised ML predictions. This upfront investment in ML would be justified when similar problems are solved repeatedly over time—for example, in transport planning related to fleet management, routing, and container yard management. Our numerical results focus on the problem class seminally addressed with the integer and continuous L-shaped cuts. Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi-knapsack (SMKP) problems available in the literature. The proposed method can solve the hardest instances of SSLP in less than 9% of the time it takes the state-of-the-art exact method, and in the case of SMKP, the same figure is 20%. Average optimality gaps are, in most cases, less than 0.1%. History: Accepted by Alice Smith, Area Editor (for this paper) for Design and Analysis of Algorithms–Discrete. Funding: Financial support from the Institut de Valorisation des Données (IVADO) Fundamental Research Project Grants [project entitled "Machine Learning for (Discrete) Optimization"]; Canada Research Chairs; the Natural Sciences and Engineering Research Council of Canada [Collaborative Research and Development Grant CRD-477938-14]; and the Canadian National Railway Company Chair in Optimization of Railway Operations at Université de Montréal is gratefully acknowledged. E. Frejinger holds a Canada Research Chair. Computations were made on the supercomputer Béluga, managed by Calcul Québec and Digital Research Alliance of Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation; the Ministère de l'Économie, de la Science et de l'Innovation du Québec; and the Fonds de Recherche du Québec – Nature et Technologies. [ABSTRACT FROM AUTHOR]

In this article, the problem of achieving the minimum backbone connectivity cost while simultaneously maximizing user coverage for 5G millimeter-wave (mmWave)-based networks is considered. Let G = (N , E) be an input graph instance with a set of nodes N (base stations) and a set of edges E. It is assumed that G represents a wireless backbone network. Let M represent a set of users to be covered by G. Note that mmWave technology has been considered in the literature as an important candidate solution for 5G networks due to its low latency. However, there remain some problems to be addressed before using this technology. A serious one is that millimeter waves cannot cover large transmission distances. In this article, the proposed methodology consists of formulating mixed-integer programming models to deal with the problem from a management point of view. Our models allow the determination of which of the nodes of G should be active and connected while simultaneously maximizing the total number of covered users. The models are solved with the CPLEX solver using its branch and cut and automatic Benders decomposition algorithms. For this purpose, symmetric complete and sparse graphs are considered. Using the symmetry concept, it is considered that the distances between base stations and users and between base stations themselves are symmetrical. Finally, an efficient local search meta-heuristic is proposed that allows for finding near-optimal solutions. Our numerical experiments indicate that the problem is hard to solve optimally. Thus, instances with up to 40 nodes and 500 users have been solved to optimality so far. In particular, it is observed that one of the models presents slightly better performance in terms of CPU time. Finally, the heuristic approach allows us to obtain tight solutions with less computational effort when dealing with even larger instances of the problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider a data-driven distributionally robust two-stage stochastic linear optimization problem over 1-Wasserstein ball centered at a discrete empirical distribution. Differently from the traditional two-stage stochastic programming which involves the expected recourse function as the preference criterion and hence is risk-neutral, we take the conditional value-at-risk (CVaR) as the risk measure in order to model its effects on decision making problems. We mainly explore tractable reformulations for the proposed robust two-stage stochastic programming with mean-CVaR criterion by analyzing the first case where uncertainties are only in the objective function and then the second case where uncertainties are only in the constraints. We demonstrate that the first model can be exactly reformulated as a deterministic convex programming. Furthermore, it is shown that under several different support sets, the resulting convex optimization problems can be converted into computationally tractable conic programmings. Besides, the second model is generally NP-hard since checking constraint feasibility can be reduced to a norm maximization problem over a polytope. However, even with the case of uncertainty in constraints, tractable conic reformulations can be established when the extreme points of the polytope are known. Finally, we present numerical results to discuss how to control the risk for the best decisions and illustrate the computational effectiveness and superiority of the proposed models. [ABSTRACT FROM AUTHOR]

Background: Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. This would be particularly interesting when considering the additional freedom and accuracy in needle pose which robotic needle placement enables. However, needle insertion typically leads to tissue deformation, resulting in uncertainty regarding the actual pose of the needles with respect to the tissue. Purpose: To efficiently address uncertainty during inverse planning for HDR BT in order to robustly optimize the pose of the needles before insertion, that is, to facilitate path planning for robotic needle placement. Methods: We use a form of stochastic linear programming to model the inverse treatment planning problem. To account for uncertainty, we consider random tissue displacements at the needle tip to simulate tissue deformation. Conventionally for stochastic linear programming, each simulated deformation is reflected by an addition to the linear programming problem which increases problem size and computational complexity substantially and leads to impractical runtime. We propose two efficient approaches for stochastic linear programming. First, we consider averaging dose coefficients to reduce the problem size. Second, we study weighting of the slack variables of an adjusted linear problem to approximate the full stochastic linear program. We compare different approaches to optimize the needle configurations and evaluate their robustness with respect to different amounts of tissue deformation. Results: Our results illustrate that stochastic planning can improve the robustness of the treatment with respect to deformation. The proposed approaches approximating stochastic linear programming better conform to the tissue deformation compared to conventional linear programming. They show good correlation with the plans computed after deformation while reducing the runtime by two orders of magnitude compared to the complete stochastic linear program. Robust optimization of needle configurations takes on average 59.42 s. Skew needle configurations lead to mean coverage improvements compared to parallel needles from 0.39 to 2.94 percentage points, when 8 mm tissue deformation is considered. Considering tissue deformations from 4 to 10 mm during planning with weighted stochastic optimization and skew needles generally results in improved mean coverage from 1.77 to 4.21 percentage points. Conclusions: We show that efficient stochastic optimization allows selecting needle configurations which are more robust with respect to potentially negative effects of target deformation and displacement on the achievable prescription dose coverage. The approach facilitates robust path planning for robotic needle placement. [ABSTRACT FROM AUTHOR]

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases. • Joint optimization of maintenance and spare provisioning for deep space habitats. • Telemetry sensor data is used to describe the uncertainty of the optimization model. • Chance constraints for restricting the probability of unexpected failures. • Scalable optimization model validated by simulation studies. [ABSTRACT FROM AUTHOR]

In this article, we consider a robust optimal proportional reinsurance and investment problem in a model with delay and dependent risks, in which the insurer's surplus process is assumed to follow a risk model with two dependent classes of insurance business. The insurer is allowed to purchase proportional reinsurance and invest his surplus in a financial market, which contains one risk-free asset and one risky asset whose price process satisfies a jump-diffusion model. Under the consideration of the performance-related capital inflow or outflow, the insurer's wealth process is modeled by a stochastic differential delay equation. The insurer's aim is to develop the robust optimal reinsurance and investment strategy for the insurer by maximizing the expected exponential utility of the combination of the average historical performance and terminal wealth under the worst-case scenario of the alternative measures. By adopting the stochastic dynamic programming technique, the expressions of the robust optimal strategy and value function are explicitly obtained. Finally, we present some numerical examples to illustrate the effects of some model parameters on the optimal strategy. [ABSTRACT FROM AUTHOR]

Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high Operations and Maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, transportation requirements, and production losses, significantly inflate the O&M costs relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator's ability to identify the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler—a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the opportunities that arise due to favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed-integer linear program, which we solve using a scenario-based rolling horizon algorithm that aligns with the industrial practice. Tested on real-world data from the U.S. North Atlantic where several offshore wind farms are in-development, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across various O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions. [ABSTRACT FROM AUTHOR]