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A machine learning-based adaptive heuristic for vessel scheduling problem under uncertainty via chance-constrained programming

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Li, Runfo Zhang, Xinyu Wang, Chengbo Cui, Jinlong Mu, Mengfeng Dalian Maritime Univ Nav Coll Maritime Intelligent Transportat Res Team Dalian 116026 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Dept Automat Hefei Peoples R China

Efficient vessel scheduling in port is critical for enhancing navigational efficiency. However, it faces substantial challenges due to unforeseeable events. In this context, this paper addresses the vessel scheduling problem with stochastic sailing times in port. The problem is formulated into a chance-constrained programming (CCP) model and then transformed into an equivalent deterministic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this model. In MLDE, a K-means clustering method is employed to generate initial population, aiming to enhance the population's quality and diversity while mitigating the impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is established as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting the optimal scaling factor and crossover probability parameters. Numerical experiments based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in comparison to existing metaheuristic algorithms and traditional differential evolution (DE) variants. A statistical analysis experiment is also conducted to further confirm the superiority of MLDE.

关键词： Vessel scheduling Machine learning Stochastic sailing times chance-constrained programming Adaptive differential evolution algorithm

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A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

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SOFT COMPUTING 2021年第2期25卷 1065-1083页

作者： Razavi Hajiagha, Seyed Hossein Daneshvar, Maryam Antucheviciene, Jurgita Khatam Univ Fac Management & Finance Dept Management Tehran Iran Vilnius Gediminas Tech Univ Dept Construct Management & Real Estate Sauletekio Ave 11 LT-10223 Vilnius Lithuania

Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is considered in a multi-criteria and uncertain environment. The proposed method in this paper inspired by the notion of heterogeneous decision-making problems in which decision-makers deal with different types of data. To this aim, a mathematical modeling-based approach is proposed considering different types of uncertainty in classification information. Demand information is considered to be stochastic due to its time-varying nature and cost information is considered to be fuzzy due to its cognitive ambiguity. A hybrid algorithm based on chance-constrained and possibilistic programming is proposed to solve the problems. Considering the stochastic nature of demand information, solving the proposed model using the hybrid algorithm, the classification of items to three classes of extremely important, class A, moderately important, class B, and relatively unimportant, class C, items are determined along with a minimum inventory level required to deal with the stochasticity of demands information. The proposed approach is applied to a case study of classifying 51 inventory items. The obtained results assigned 22%, 39%, and 39% of the items to A, B, and C classes, respectively.

关键词： Multi-criteria inventory classification chance-constrained programming Possibilistic programming

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Managing Data Quality of the Data Warehouse: A chance-constrained programming Approach

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INFORMATION SYSTEMS FRONTIERS 2021年第2期23卷 375-389页

作者： Liu, Qi Feng, Gengzhong Tayi, Giri Kumar Tian, Jun Xi An Jiao Tong Univ Sch Management 28 Xianning Rd Xian 710049 Shaanxi Peoples R China Minist Educ Proc Control & Efficiency Engn Key Lab 28 Xianning Rd Xian 710049 Shaanxi Peoples R China SUNY Albany Sch Business Albany NY 12222 USA

To make informed decisions, managers establish data warehouses that integrate multiple data sources. However, the outcomes of the data warehouse-based decisions are not always satisfactory due to low data quality. Although many studies focused on data quality management, little effort has been made to explore effective data quality control strategies for the data warehouse. In this study, we propose a chance-constrained programming model that determines the optimal strategy for allocating the control resources to mitigate the data quality problems of the data warehouse. We develop a modified Artificial Bee Colony algorithm to solve the model. Our work contributes to the literature on evaluation of data quality problem propagation in data integration process and data quality control on the data sources that make up the data warehouse. We use a data warehouse in the healthcare organization to illustrate the model and the effectiveness of the algorithm.

关键词： Data quality Data warehouse chance-constrained programming Optimization model Artificial bee Colony algorithm

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Set Squeezing Procedure for Quadratically Perturbed chance-constrained programming

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 682-694页

作者： He, Xin Wu, Yik-Chung Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Hanshan Normal Univ Dept Comp & Informat Engn Chaozhou 521041 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong 999077 Peoples R China

The set squeezing procedure, a new optimization methodology for solving chance-constrained programming problems under continuous uncertainty distribution, is proposed in this paper. The generally intractable chance constraints and unknown convexity are tackled by a novel analyses of local structure of the feasible set. Based on the newly discovered structure, it is proved that the set squeezing procedure converges and local optimality is guaranteed under mild conditions. Furthermore, efficient algorithms are derived for the set squeezing procedure under the widely used quadratically perturbed constraints. The developed method is applied to the mean squared error (MSE) based probabilistic transceiver design as an application example. Simulation results show that the MSE outage probability can be controlled tightly, which leads to lower transmit power, compared to the existing dominant safe approximation method and the bounded robust optimization method.

关键词： Uncertainty programming Probabilistic logic Array signal processing Transceivers Wireless communication Perturbation methods chance-constrained programming probabilistic transceiver design and beamforming outage probability constraint

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Multi-Time Scale Economic Dispatch of Integrated Electricity and Natural Gas Systems with Flexibility Constraints Based on chance-constrained programming

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PROCESSES 2023年第7期11卷 1987页

作者： Yang, Peng Cheng, Huilin Liu, Zhenyu Zhang, Jing He, Liangce Liu, Yujie Lu, Zhigang State Grid Hebei Elect Power Co Ltd Shijiazhuang 050051 Peoples R China State Grid Shijiazhuang Elect Power Supply Co Shijiazhuang 050051 Peoples R China Yanshan Univ Key Lab Power Elect Energy Conservat & Motor Drive Qinhuangdao 066004 Peoples R China State Grid Jinzhou Cty Elect Power Supply Co Shijiazhuang 052200 Peoples R China

The connection between various energy types in the integrated power and natural gas system has grown stronger in recent years, as has the penetration rate of clean energy. Wind power generation volatility offers a considerable barrier to power system operation. This research provides a multi-time scale economic dispatch model with flexibility limitations to address this issue. Through chance-constrained programming, the equipment flexibility is described by probability functions and predetermined confidence levels in this model, and the generating cost and wind power consumption are improved through day-ahead and intra-day optimal scheduling. Finally, the effectiveness of the proposed model is verified by two case studies of integrated energy systems, where the results show that about 68.0-72.1% wind power curtailment can be effectively reduced while satisfying all load and system safety requirements.

关键词： integrated energy system chance-constrained programming multi-time scale flexibility constraints uncertainties

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A chance-constrained programming approach to optimal planning of low-carbon transition of a regional energy system

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ENERGY 2023年第PartA期278卷

作者： Zhang, Jiaqi Tian, Guang Chen, Xiangyu Liu, Pei Li, Zheng Tsinghua Univ Tsinghua BP Clean Energy Res & Educ Ctr Dept Energy & Power Engn Beijing 100084 Peoples R China State Grid Hebei Elect Power Co LTD Wuhan Peoples R China

Low-carbon transition of energy systems is an inevitable trend to address climate change challenges. For developing regions, proper planning is essential for reducing transition costs during low-carbon transition of their energy systems, featuring a higher proportion of intermittent renewable power connected to power grids. Impact of uncertainty must be considered for more feasible planning of peak-shaving and energy storage units. In this study, a chance-constrained programming approach to optimal planning of low-carbon transition of a regional energy system is presented. This approach considers uncertainties of wind power, photovolatic (PV) power and load to ensure power supply reliability. A developing region in central China is taken as a case study. Results show that considering uncertainty, an additional 4.79% of power generation capacity needs to be installed per year on average, with a 3.02% increase in the transition cost. Finally, sensitivity analysis results are provided, showing that a rapid increase in transition costs occurs when the confidence level exceeds 99%. The results in this study provide references for decision-makers to plan the low-carbon transition of energy systems as well as weighing transition costs against energy supply stability.

关键词： chance-constrained programming Energy system Optimization Decarbonization

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Optimal Dispatch of Hydropower Stations based on chance-constrained programming

Optimal Dispatch of Hydropower Stations based on Chance-Cons...

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Power System and Green Energy Conference (PSGEC)

作者： Zhang, Xuan Wei, Hua Su, Xianxin Gao, Wei Chen, Danlei Hu, Faxiang Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning Guangxi Peoples R China

ISBN: (纸本)9781728190952

Aiming at the optimal dispatching problem of hydropower stations considering the uncertainty of natural inflow, an optimal dispatching model of hydropower stations based on chance-constrained programming is proposed. The uncertainty of natural inflow is an important factor affecting the optimal operation of hydropower stations. In this paper, the prediction error of natural inflow is taken as the uncertainty, and the randomness of natural inflow is described by the probability distribution of uncertain quantity. The chance-constrained programming theory is introduced into the optimal operation of hydropower stations, and the chance-constrained problem is transformed into a deterministic problem to solve. Finally, the results of numerical examples show that the proposed method is feasible and can effectively balance the robustness and economy of the system by controlling the risk coefficient.

关键词： Hydropower Stations Natural Inflow chance-constrained programming Confidence Level

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Fuzzy distributional chance-constrained programming for handling stochastic and epistemic uncertainties during flotation processes

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2020年 164卷 248-260页

作者： Liang, Yu He, Dakuo Wang, Qingkai Lu, Xianlun Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China State Key Lab Proc Automat Min & Met Beijing 100160 Peoples R China

During the flotation process, a fuzzy distributional chance-constrained programming approach is proposed to design the circuit structure, equipment size and operating conditions considering stochastic and epistemic uncertainties. Stochastic uncertainties are irreducible, such as fluctuations in copper price and circuit feed mass flowrate, and epistemic uncertainties are due to the complicated flotation mechanism. To achieve the probability measure in the chance constraints, a finite number of scenarios are defined for the stochastic uncertainties by using discrete probability distribution sets. Moreover, the epistemic uncertainties are handled by fuzzy sets. In this context, a fuzzy probability-box of profits combining probability theory with fuzzy theory can be obtained to assess the distribution profile. The integral whole optimization of the flotation process is solved by using rule-oriented genetic algorithm, adapted from the non-dominated sorting genetic algorithm (NSGA-II), and the design rules are embedded in initialization, crossover and mutation. Finally, the sensitivity analysis of epistemic uncertainties on the optimal designs is carried out to get the critical uncertain variable. The method proposed here can fully reflect the flexibility of the process design under mixed uncertainties, and the optimal designs are compromise solutions that take into account the economic indicator and uncertainty quantification. (c) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Fuzzy distributional chance-constrained programming Flotation process Uncertainty Scenario generation Fuzzy simulation NSGA-II

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A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 132卷 103382-103382页

作者： Wang, Xinwei Brownlee, Alexander E., I Weiszer, Michal Woodward, John R. Mahfouf, Mahdi Chen, Jun Cent South Univ Sch Traff & Transportat Engn Changsha Hunan Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London England Univ Stirling Div Comp Sci & Math Stirling Scotland Queen Mary Univ London Sch Engn & Mat Sci London England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process.

关键词： Air traffic management Airport ground movement chance-constrained programming Quickest path search Taxi time uncertainties

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An interval chance-constrained programming-based optimization model for carbon capture, utilization, and storage system planning

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 772卷 145560-145560页

作者： Zhai, Mingyang Jia, Haifeng Yin, Dingkun Tsinghua Univ Sch Environm Beijing 100084 Peoples R China

Carbon capture, utilization, and storage (CCUS) are widely regarded as a crucial technological option for industrial large-scale carbon dioxide (CO2) emissions reduction. However, high-cost and uncertainties hinder the widespread application of CCUS technology. In this study, an interval-chance-constrained programming-based optimization model was proposed to address random probability distributions, interval values, complex interactions, and the dynamics of capacity expansion issues. The model was applied to a CCUS project in China. A set of violation probability levels (0.01, 0.05, 0.1, and 0.2) were designed to reflect system costs and risk levels. And then the solutions for system costs, capacity expansion, and operating schemes under four violation probability levels (p(i')) could be generated. The results revealed that the model could ensure the highest reliability and largest CO2 storage under p(i') = 0.01. At this probability level, the amount of CO2 storage would range from 4972.05-5429.75 kilotons per annum (ktpa), the CCUS system cost would be highest at $166.57 million, and the net system benefits would be slightly less at $105.91 million. If policymakers strive to achieve the net system benefits of the project, the highest net system benefits would be achieved under p(i') = 0.05. At this probability level, the net system benefits would increase to $135.45 million, the system cost would reduce to 5138.62 million, but the total amount of CO2 storage would decrease to between 4090.01 and 4653.24 ktpa, which would entail a high risk of system violation. These findings enable policymakers to determine the trade-offs among system reliability, CO2 reduction, and the benefits of the project. The modeling approach can also address interactions among CCUS activities and the dynamics of facility expansion issues as well as help policymakers develop adaptive operational strategies. This study enriches CCUS research through an interval chance-constrained optimization

关键词： Carbon capture, utilization, and storage CO2 compression Enhanced oil recovery Interval linear programming chance-constrained programming Uncertainties

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