Endogenous Uncertainty Research Articles

An approximate dynamic programming approach for solving aircraft fleet engine maintenance problem: Methodology and a case study

We consider a long-term engine maintenance planning problem for an aircraft fleet. The objective is to guarantee sufficient on-wing engines to reach service levels while effectively organizing shop visits for engines. However, complexity arises from intricate maintenance policies and uncertainty in engine deterioration. To address this problem, we propose a graph-based approach representing high-dimensional engine statuses and transitions. We then formulate the problem as a multi-stage stochastic integer program with endogenous uncertainty. We develop an approximate dynamic programming algorithm enhanced by dynamic graph generation and policy-sifting techniques so as to reduce the computational overhead in large problems. We demonstrate the efficacy of our method, compared with other popular methods, in terms of running time and solution quality. In the case study, we present an implementation in a real-world decision system in China Southern Airlines, in which the proposed method works seamlessly with other supporting modules and significantly improves the efficiency of engine maintenance management.

A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty

A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty

Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery

This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning. Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .

Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

Home country’s economic and political institutions: firms’ ownership decisions in cross-border acquisitions

The influences of a home country’s economic and political institutions on acquirers’ cross-border acquisitions (CBAs) ownership strategies remains unexplored. Acquirers face endogenous uncertainty (i.e., uncertainty that can be resolved in part by acquirers) when transferring headquarters resources to foreign target firms and exogenous uncertainty (i.e., uncertainty that cannot be resolved by acquirers) when there is an unpredictable policy change. We argue that well-developed economic and political institutions in a home country play a market-supporting and constraining role in mitigating endogenous and exogenous uncertainty respectively, enabling acquirers to seek high ownership stakes in CBAs. We also argue that the importance of a home country’s well-developed economic and political institutions for acquirers’ CBA ownership strategic decisions depends on mutual trade dependence between the acquirers’ home country and the target firms’ host countries and also on the economic capabilities of the acquirers developed in different industries and political capabilities developed in different host countries. To test these arguments, we analyze 133,623 CBAs between 2000 and 2020 and find support for the distinct roles played by a home country’s economic and political institutions.

Inter-firm alliance portfolio distance and the partners’ preferences for the distal governance modes to manage endogenous uncertainty in the biotechnology sector

Inter-firm alliances play a pivotal role in the swiftly evolving high-tech landscape, yet they often introduce uncertainty, especially when partners have varying degrees of asymmetry. This study explores the dynamics of such alliances, focusing on endogenous uncertainty (technology and partner-specific) over external factors (culture and institutions). We investigate whether the alliance portfolio distance between partners aligns with distal governance modes, which refers to the continuity from the upfront payment mode to equity investments on the timeline. Furthermore, does the investor’s comparative position influence this alignment between construed uncertainty and governance modes? Drawing on Construal Level Theory (CLT), inter-partner distance corresponds to high-level construal in distal governance modes, while proximity aligns with low-level construal in proximal modes. The analysis includes 13,000 biotechnology sector alliance events across 1,700 firms in 29 countries over a decade. Findings reveal a shift in governance mode preferences from the concrete and proximal (upfront) to abstract and distal (equity), touching milestones and licensing in the middle. The investor’s lead in the alliance portfolio asymmetry further strengthens the preference for abstract modes in the distal and high abstract construal levels. This study advances CLT as a framework, offers an integrated approach, and highlights a preference for high-value tech collaborations despite increased cost and risk over temporal and abstract governance conditions.

Endogenous Uncertainty: Does Investment Inefficiency Contributes to Uncertainty?

Endogenous Uncertainty: Does Investment Inefficiency Contributes to Uncertainty?

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

Biographies of uncertainty regulation in the labor market and extension of working life in Chile

Abstract Despite lacking policies targeting the extension of working life, Chile is the Latin American country that has exhibited the largest increase in the labor force participation rate of people aged 65+ in the last two decades. In this research, following an analytical framework on regulation of endogenous uncertainty and relying on rich qualitative data (life story interviews of 90 older workers aged 60–86, across 21 cities and 6 regions), we approached the complexity of extended working lives in Chile by addressing an unexplored dimension. Specifically, we explore individuals’ agency over their employment trajectories (i.e., both in adulthood and old age) among those who remained active in the labor market after the legal retirement age. Our findings provide strong evidence that extended working lives not only result from precarious social conditions, but are also shaped by complex processes involving both expansive and adaptive individual agency in which people engaged throughout their life course. Therefore, the high exogenous uncertainty in the labor market should not merely be interpreted from the perspective of “precarity,” but also as a scenario that encouraged individuals to behave in a way that led them to engage in the labor force across their lives in accordance with their preferred level of endogenous uncertainty.

Geometric prescribed‐time control of quadrotors with adaptive extended state observers

AbstractThis article studies an adaptive extended state observer (AESO) based prescribed‐time geometric attitude stabilization problem of quadrotors with endogenous uncertainties, exogenous disturbances, and actuator saturation, which can be estimated and compensated by AESOs. The employment of the geometric representation method for system attitude can effectively avoid ambiguity or singularity problems that may arise with other representations. The temporal and scale transformations are employed to solve the global boundedness problem on infinite time instead of the prescribed‐time convergence problem on finite time. By integrating the proposed controller with the backstepping technique, the system of quadrotors can be prescribed‐time stable. Further, a prescribed‐time geometric attitude control technique is devised such that the control signal is bounded and the state signals converge to a small domain near the equilibrium point in the prescribed time. The prescribed time is determined by the user and is independent of the initial conditions. The comparative simulations with different initial information are performed to demonstrate the effectiveness of the proposed design technique.

Bayes Optimal Integration of Social and Endogenous Uncertainty in Numerosity Estimation.

One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self-sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results demonstrated adjustment of initial estimations toward group means only when group estimations were more reliable (or "certain"), compared to participants' endogenous metric uncertainty. Our results support Bayes optimal social conformity while also pointing to an implicit form of metacognition.

Geometric Attitude Fault-Tolerant Control of Quadrotor Unmanned Aerial Vehicles with Adaptive Extended State Observers

This paper is concerned with the attitude tracking problem of quadrotor unmanned aerial vehicles (UAVs) with respect to endogenous uncertainties, exogenous disturbances and actuator failures. Two different control methods are proposed to solve this problem. First, an adaptive extended state observer (AESO)-based control framework is devised to tackle the difficulties caused by model uncertainties and external disturbances. A fault-tolerant control method is proposed to cope with the occurrence of actuator failure, which is modeled as a constant loss of effectiveness. Another method employs AESOs to compensate for lumped disturbances, which include endogenous uncertainties, exogenous disturbances and actuator failures. Then, the error can exponentially converge to a bounded set. Finally, simulations are performed to ensure the feasibility of the designed technique.

Resilient Unit Commitment for Transmission Lines Hardening Under Endogenous Uncertainties

Resilient Unit Commitment for Transmission Lines Hardening Under Endogenous Uncertainties

V2G for Frequency Regulation Service: a Stackelberg Game Approach Considering Endogenous Uncertainties

V2G for Frequency Regulation Service: a Stackelberg Game Approach Considering Endogenous Uncertainties

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

On the Use of Mobile Power Sources in Distribution Networks Under Endogenous Uncertainty

Mobile power sources (MPSs) have promising potential for spatiotemporal flexibility exchange in power distribution systems (DS). They can be strategically employed to enhance the network resilience when facing the aftermath of high-impact low-probability (HILP) events. This paper proposes a novel service restoration formulation that models and accounts for the endogenous uncertainty in the MPSs routing and scheduling decision making. The proposed restoration model is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex continuous relaxation. We derive computationally tractable linearization procedures to reformulate the MINLP model as an equivalent mixed-integer linear programming (MILP) problem. Case studies on the IEEE 33-node and 123-node test systems demonstrate the role of incorporating endogenous uncertainties in the decision-making process and the effectiveness of the proposed restoration scheme in boosting the DS resilience.

Endogenous Uncertainty and Credit Crunches

Abstract We develop a theory of endogenous uncertainty in which the ability of investors to learn about firm-level fundamentals is impaired during financial crises. At the same time, higher uncertainty reinforces financial distress. Through this two-way feedback loop, a temporary financial shock can cause a persistent reduction in risky lending, output, and employment that coincides with increased uncertainty, default rates, credit spreads, and disagreement among forecasters. We embed our mechanism into standard real business cycle and New-Keynesian models and show how it generates endogenous and internally persistent processes for the efficiency and labour wedges.

Markov chain stochastic DCA and applications in deep learning with PDEs regularization

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we assume that it is only possible to access Markov chains whose sequences of distributions converge to the target distributions. This setting is legitimate as Markovian noise arises in many contexts including Bayesian inference, reinforcement learning, and stochastic optimization in high-dimensional or combinatorial spaces. We then design a stochastic algorithm named Markov chain stochastic DCA (MCSDCA) based on DCA (DC algorithm) - a well-known method for nonconvex optimization. We establish the convergence analysis in both asymptotic and nonasymptotic senses. The MCSDCA is then applied to deep learning via PDEs (partial differential equations) regularization, where two realizations of MCSDCA are constructed, namely MCSDCA-odLD and MCSDCA-udLD, based on overdamped and underdamped Langevin dynamics, respectively. Numerical experiments on time series prediction and image classification problems with a variety of neural network topologies show the merits of the proposed methods.

Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively.

A joint chance constrained optimization algorithm with robust reconstruction for multi-echelon and multi-period closed-loop manufacturing adjustable system design under multi-source uncertainty

The complex electronic assembly scheduling problem under uncertainty poses a new challenge. The joint chance constraint programming method based on robust reconstruction is used to hedge the endogenous and external uncertainty in the closed-loop manufacturing scheduling model of multi-echelon and multi-product electronic assembly process under multi-source uncertainty in this study. The numerical cases of electronic factory in assembly and closed-loop manufacturing mode are studied, the sensitivity of cost to different types of uncertain parameters is analyzed. The results show that different types of uncertainty have different effects on economic benefits, and endogenous uncertainty has a greater impact on manufacturing costs. Decision makers can adjust the adjustable parameters according to external policies and economic situation to control the conservatism and economic benefits of the scheme, and at the same time reduce the demand for uncertain data and improve the efficiency of solution. The model provides a new sustainable production method for electronic assembly manufacturing practitioners, and makes the best production decision when weighing economic benefits and risks.