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A stochastic Drone-Scheduling Problem with Uncertain Energy Consumption

DRONES 2024年第9期8卷 430-430页

作者： He, Yandong Zheng, Zhong Li, Huilin Deng, Jie Nanning Univ Coll Business Nanning 541699 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China St Louis Univ Sch Adv Studies Baguio 2600 Philippines Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China

In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems.

关键词： drone-scheduling problem stochastic energy consumption two-stage stochastic programming variable neighborhood search fixed-sample sampling

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Optimal design and robust operational management of regional bioethanol supply chain with various technological choices and uncertainty fusions

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COMPUTERS & CHEMICAL ENGINEERING 2024年 182卷

作者： Huang, Xianling Ji, Ling Yin, Jianguang Huang, Guohe Beijing Univ Technol Sch Econ & Management Beijing 100124 Peoples R China Shandong Smart Grid Technol Innovat Ctr Jinan 250003 Shandong Peoples R China State Grid Shandong Elect Power Co Elect Power Res Inst Jinan 250003 Shandong Peoples R China Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada Beijing Univ Technol Sch Econ & Management 100 Ping Le Yuan Beijing 100124 Peoples R China

Bioethanol has emerged as a promising alternative to fossil fuels, but its commercialization is hindered by high costs and uncertainties surrounding feedstock supply and policies. To address these challenges, a two-stage stochastic robust programming model is developed for regional biorefineries planning and supply chain management with various technological choices and uncertainty fusions. The model maximizes total profits by optimizing site selection, alternative technology combinations, feedstock inventories, and accounting for uncertain and seasonally varying biomass supply. This method helps decision-makers achieve risk-aversion robust optimal solutions, tailored for a case study in Guangdong, China. The results suggest that installing 8 biorefineries using the LHW-SSF-A technique combination would be optimal. The desired feedstock warehouse capacity would be 358.48 ktons, with low utilization closely tied to seasonal crop harvests. Risk-averse decision makers would take conservative production strategies, potentially reducing investment in biorefineries, while market prices and subsidies could encourage fewer conservative investors.

关键词： Biomass Supply chain management two-stage stochastic programming Robust programming Bioethanol production

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Strategic planning and optimal tactical management of regional bioethanol supply-chain system with high spatial resolution and uncertainties

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BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR 2024年第2期18卷 464-481页

作者： Wang, Mengmeng Ji, Ling Xie, Yulei Beijing Univ Technol Sch Econ & Management Beijing Peoples R China Guangdong Univ Technol Inst Environm & Ecol Engn Guangzhou Peoples R China Beijing Univ Technol Sch Econ & Management 100 Ping Le Yuan Beijing 100124 Peoples R China

Although bioethanol is viewed as a promising alternative to traditional fossil fuels, its commercialization is hindered by high production costs and uncertain policies. This study proposes a novel integrated framework to support decision making in the bioethanol supply-chain system under complex uncertainties. By considering various geographical, economic, and environmental factors, potentially suitable sites are identified using a geographic information systems-based (GIS-based) multi-criteria decision-making approach. Then, a two-stage stochastic, fuzzy, chance-constrained programming model is developed to determine investment decisions (i.e. site and size of biorefineries) and operational strategies (i.e. production schedule, biomass supply network, etc.) in the face of uncertainties. The proposed research framework is tailored for a case study in Henan Province, China, which suggests the installation of six biorefineries with a total capacity of 679.11 million liters to reduce local carbon emissions by 1.54 million tons. Decision makers with different risk preferences can use this method to find optimal tradeoff strategies between system cost and risk under uncertainties. Sensitivity analysis also reveals that increasing the proportion of bioethanol blending targets is the key exogenous variable to promote the use of local biomass resources.

关键词： bioethanol supply chain GIS multi-criteria decision making uncertainty two-stage stochastic programming

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Model-Free Learning of two-stage Beamformers for Passive IRS-Aided Network Design

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 652-669页

作者： Hashmi, Hassaan Pougkakiotis, Spyridon Kalogerias, Dionysis Yale Univ Dept Elect Engn New Haven CT 06520 USA Univ Dundee Sch Sci & Engn Dundee DD1 4HR Scotland

Electronically tunable metasurfaces, or Intelligent Reflecting Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflecting elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers jointly with short-term optimal precoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in a strictly model-free fashion, relying solely on the effective compound channels observed at the terminals, while being independent of channel models or network/IRS configurations. Another remarkable feature of ZoSGA is being amenable to analysis, enabling us to establish a state-of-the-art (SOTA) convergence rate of the order of O(S--root S & varepsilon;(-4)) under minimal assumptions, where S is the total number of IRS elements, and & varepsilon;is a desired suboptimality target. Our numerical results on a standard MISO downlink IRS-aided sumrate maximization setting establish SOTA empirical behavior of ZoSGA as well, consistently and substantially outperforming standard fully model-based baselines. Lastly, we demonstrate that ZoSGA can in fact operate in the field , by directly optimizing the capacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA) on a multiple user/IRS, link-dense network setting, with essentially no computational overheads or performance degradation.

关键词： Optimization Wireless networks stochastic processes Standards Quality of service Millimeter wave communication Array signal processing 6G intelligent reflecting surfaces (IRS/RIS) two-stage stochastic programming zeroth-order optimization model-free learning sumrate maximization equivalent circuit model

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Optimal planning and management of the energy-water-carbon nexus in hybrid AC/DC microgrids for sustainable development of remote communities

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APPLIED ENERGY 2025年第PartB期377卷

作者： Valencia-Diaz, Alejandro Toro, Eliana M. Hincapie, Ricardo A. Univ Tecnol Pereira Fac Ingn Programa Ingn Elect Pereira Colombia Univ Tecnol Pereira Fac Ciencias Empresariales Programa Ingn Ind Pereira Colombia

This paper presents a two-stage stochastic mixed-integer nonlinear programming model, which is linearized to a mixed-integer linear programming (MILP) form, for optimizing the energy-water-carbon (EWC) nexus in remote AC/DC microgrids aimed at sustainable community development. The model optimizes the selection, location, and operation of diesel generators, voltage source converters, photovoltaic systems, wind turbines, and battery energy storage systems while managing CO2 emissions. It accurately models the AC/DC microgrid's operation, determining optimal voltages, currents, and converter states (inverter or rectifier). Uncertainties in power demand, water consumption, solar irradiation, and wind speed are modeled using stochastic scenarios generated via k-means clustering. The MILP model is implemented in GAMS and solved with the commercial solver CPLEX, ensuring global optimization. Results show that integrating distributed energy resources, compared to a case without these elements, reduces CO2 emissions of the diesel generators and the operational costs of the EWC nexus, highlighting the proposed approach's environmental and economic benefits. These findings underline the importance of incorporating distributed energy resources in the sustainable development of microgrids.

关键词： AC/DC microgrid Energy-water-carbon nexus Sustainable development goals Distributed energy resources Mixed-integer linear programming two-stage stochastic programming

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Integrated vehicle allocation and relocation for shared micromobility under competition and demand uncertainty

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IISE TRANSACTIONS 2025年第8期57卷 994-1013页

作者： Jin, Ziliang Pan, Kai Shen, Zuo-Jun Max Xu, Wenxin Southeast Univ Sch Econ & Management Nanjing Peoples R China Hong Kong Polytech Univ Fac Business Kowloon Hong Kong Peoples R China Univ Hong Kong Fac Engn Hong Kong Peoples R China Univ Hong Kong Fac Business & Econ Hong Kong Peoples R China Univ South Carolina Darla Moore Sch Business Columbia SC USA

This article aims to understand the shared micromobility firms' operations under competition and provide managerial guidance to the firms and the regulator. We consider two shared micromobility firms competing in the same service area, each providing micromobility vehicles to satisfy uncertain demands. Concerning allocation restrictions by the regulator, we propose an innovative capacity-sharing agreement between the two firms. Each firm solves an integrated vehicle allocation and relocation problem, modeled as a two-stage stochastic program on a spatial-temporal network. We explore the optimality condition of each firm's decision-making and seek a Nash equilibrium by optimizing certain objectives over the joint optimality conditions of both firms. We prove that capacity sharing helps reduce the total demand loss in the system. We perform extensive numerical experiments based on real data to obtain managerial insights. We find that regulator restrictions impact firms' profitability and service level. After introducing capacity sharing, one firm may act like a free rider that relies on the vehicles transferred from her opponent. Meanwhile, many vehicles are shared in periods and regions with high trip demands. Capacity sharing can reduce the number of relocated vehicles by serving as a substitution for relocation and also improves the firms' profitability.

关键词： Shared micromobility allocation and relocation two-stage stochastic programming competition capacity sharing

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Performance evaluation of concurrent supply chain resilience strategies

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 279卷

作者： Alikhani, Reza Ranjbar, Amirhossein Torabi, S. Ali Zobel, Christopher W. MBS Sch Business Montpellier France Temple Univ Fox Sch Business Philadelphia PA USA Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Virginia Tech Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA

Despite the growing research on supply chain resilience (SCR) strategies, a comprehensive performance assessment for implementing multiple strategies simultaneously, considering both positive and negative synergistic effects, is still lacking. Given the complex nature of resilience, it is important for any such assessment to incorporate a multi-dimensional view of resilient behavior. To help address this gap in the literature, we propose an extended slack-based super-efficiency data envelopment analysis for evaluating the performance of SCR strategies when they might be utilized concurrently. The new approach characterizes resilience performance by considering the cost of implementing a set of resilience strategies (resilience cost) as an input to the assessment process, and by considering (1) the resulting cost of the disruption, (2) the impact on the service level, and (3) the associated recovery time as outputs. Taken together, these metrics allow for assessing the combined performance of each candidate set of resilience strategies under their synergistic effects. This new and comprehensive approach for resilience assessment is carefully incorporated into a framework that ranks different subsets of relevant strategies for any given supply chain, according to their relative effectiveness across a variety of different disruption scenarios. The proposed decision framework is able to help decision- makers identify the best ensemble of resilience strategies available. A real-world case study is used to illustrate the significant potential of the framework, and a number of important managerial insights are provided to help decision-makers more effectively analyze and implement their own optimal subsets of SCR strategies, particularly in the presence of network quality or budgetary constraints. Theoretical implications include advancing the understanding of multi-strategy interactions in SCR, while practical implications focus on guiding SC managers in selecting and imp

关键词： Synergistic effects Performance metrics Data envelopment analysis Supply chain risk management two-stage stochastic programming

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Optimization of Plane Slots Number for Container Yard Based on two-stage stochastic programming

Optimization of Plane Slots Number for Container Yard Based ...

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International Conference on Advances in Energy and Environmental Science (ICAEES)

作者： Guo, Zijian Yu, Xuhui Wang, Wenyuan Tang, Guolei Dalian Univ Technol Fac Infrastruct Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9783037858622

Appropriate plane slots number of the container yard is of great importance to the sustainable development of low-carbon container ports. Considering the container type, handling process, cost and benefit of unit yard space, etc., a two-stage stochastic programming model with the goal of maximizing the yard profit was established by choosing the maximum daily storage number of containers as a random variable. The maximum yard profit and the minimum penalty function are respectively chosen as the goal in the first and second stage. The example results show that the value obtained by the two-stage stochastic programming is smaller than that by the specification. The proposed model provides an optimization method for the determination of plane slots number through effectively lessening the influence of uncertainties and saving resource cost.

关键词： Container Yard Plane Slots Number Uncertainty two-stage stochastic programming Yard profit

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stochastic programming approach for unidirectional quay crane scheduling problem with uncertainty

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JOURNAL OF SCHEDULING 2021年第2期24卷 137-174页

作者： Ma, Shoufeng Li, Hongming Zhu, Ning Fu, Chenyi Tianjin Univ Inst Syst Engn Coll Management & Econ Tianjin 300072 Peoples R China

Quay crane scheduling is a key aspect of container terminal operation, which can be regarded as a decision-making process with uncertainty. Each task involves stochastic loading and unloading operation times owing to the existence of uncertainty. In this study, we investigate the unidirectional quay crane scheduling problem for a stochastic processing time, which requires that all the quay cranes move in the same direction either from bow to stern, or vice versa, throughout the planning horizon. The problem is formulated as a two-stage stochastic mixed-integer programming model, where the binary first-stage decision variables correspond to the assignment of tasks to quay cranes, and the mixed-integer second-stage decision variables are related to the generation of detailed schedules. To make the model solvable, we develop an alternative equivalent reformulation with a special structure that involves binary variables in the first stage and continuous variables in the second stage. To solve this reformulated model, an integer L-shaped method is presented for small-size instances, and a simulated annealing algorithm is presented for large-size instances to obtain near-optimal solutions. Numerical experiments show that the integer L-shaped method and simulated annealing algorithm could efficiently solve the unidirectional quay crane scheduling problem with uncertainty. The results also indicate that the stochastic model has distinct advantages in terms of shortening the completion time of vessels and improving the service level of container terminals compared with the expected value problem solutions.

关键词： Unidirectional quay crane scheduling Uncertainty two-stage stochastic programming Integer L-shaped method Simulated annealing algorithm

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stochastic red-blue set covering: a decomposition approach

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第4期91卷 923-951页

作者： Islip, David Kwon, Roy H. Univ Toronto Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G8 Canada

This work presents the two-stage stochastic red-blue set covering problem (2S-SRBSC). The red-blue set covering problem (RBSC) selects a subcollection of sets to cover all the blue elements while minimizing the number of red elements covered. Although RBSC has many application areas, such as classification and fraud detection, it does not capture randomness related to set membership and color uncertainty or any sense of recourse in response to the uncertainty. This work motivates these considerations via a fraud-prevention problem mortgage lenders face in practice. 2S-SRBSC is introduced to address this. In the proposed problem, uncertainty is represented via scenarios, and the decision-maker pays for sets in the first stage and has recourse to cover blue elements in the second stage that remain uncovered post-uncertainty. Different variants of 2S-SRBSC are considered, highlighting how they can model the real-life problem mortgage lenders face. In the case of 2S-SRBSC with simple recourse, a Benders decomposition (BD) and Benders dual decomposition (BDD) are presented and implemented in a commercial branch-and-cut solver. The resulting BDD subproblems yield cuts that are significantly tighter than those BD produces. The decompositions are tested and offer improvements in terms of solution quality and time for large-scale instances relative to the extensive form. Furthermore, the BDD exhibits desirable performance for instances where the commercial solver can quickly solve the mixed-binary subproblems. This work presents the first computational experience with decomposition algorithms applied to 2S-SRBSC problems.

关键词： Combinatorial optimization two-stage stochastic programming Mixed binary programming Branch-and-cut

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