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Efficient presolving methods for the influence maximization problem

NETWORKS 2023年第3期82卷 229-253页

作者： Chen, Sheng-Jie Chen, Wei-Kun Dai, Yu-Hong Yuan, Jian-Hua Zhang, Hou-Shan Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China

We consider the influence maximization problem (IMP) which asks for identifying a limited number of key individuals to spread influence in a network such that the expected number of influenced individuals is maximized. The stochastic maximal covering location problem (SMCLP) formulation is a mixed integer programming formulation that effectively approximates the IMP by the Monte-Carlo sampling. For IMPs with a large-scale network or a large number of samplings, however, the SMCLP formulation cannot be efficiently solved by existing exact algorithms due to its large problem size. In this paper, we attempt to develop presolving methods to reduce the problem size and hence enhance the capability of employing exact algorithms in solving large-scale IMPs. In particular, we propose two effective presolving methods, called strongly connected nodes aggregation (SCNA) and isomorphic nodes aggregation (INA), respectively. The SCNA enables to build a new SMCLP formulation that is potentially much more compact than the existing one, and the INA further eliminates variables and constraints in the SMCLP formulation. A theoretical analysis on two special cases of the IMP is provided to demonstrate the strength of the SCNA and INA in reducing the problem size of the SMCLP formulation. We integrate the proposed presolving methods, SCNA and INA, into the Benders decomposition algorithm, which is recognized as one of the state-of-the-art exact algorithms for solving the IMP. We show that the proposed SCNA and INA provide the possibility to develop a much faster separation algorithm for the Benders cuts. Numerical results demonstrate that with the SCNA and INA, the Benders decomposition algorithm is much more effective in solving the IMP in terms of solution time.

关键词： Benders decomposition influence maximization integer programming presolving methods stochastic programming

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Fragility-based lot-sizing in veterinary pharmaceutical plants under demand uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第10期62卷 3716-3752页

作者： Campos, Rafael A. Caunhye, Aakil M. Alem, Douglas Munari, Pedro Univ Laval Dept Operat & Decis Syst Quebec City PQ Canada Univ Edinburgh Business Sch 29 Buccleuch Pl Edinburgh EH8 9JU Scotland Univ Fed Sao Carlos Prod Engn Dept Rodovia Washington Luis Sao Carlos Brazil

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied.

关键词： Lot-sizing robust optimisation fragility-based veterinary pharmaceutical industry stochastic programming

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A dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2023年第2期86卷 669-710页

作者： Lin, Gui-Hua Yang, Zhen-Ping Yin, Hai-An Zhang, Jin Shanghai Univ Sch Management Shanghai 200444 Peoples R China Jiaying Univ Sch Math Meizhou 514015 Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Int Ctr Math Natl Ctr Appl Math Shenzhen Dept MathPeng Cheng Lab Shenzhen 518055 Peoples R China

In this paper, a dual-based stochastic inexact algorithm is developed to solve a class of stochastic nonsmooth convex problems with underlying structure. This algorithm can be regarded as an integration of a deterministic augmented Lagrangian method and some stochastic approximation techniques. By utilizing the sparsity of the second order information, each subproblem is efficiently solved by a superlinearly convergent semismooth Newton method. We derive some almost surely convergence properties and convergence rate of objective values. Furthermore, we present some results related to convergence rate of distance between iteration points and solution set under error bound conditions. Numerical results demonstrate favorable comparison of the proposed algorithm with some existing methods.

关键词： stochastic programming stochastic approximation Duality Convergence rate

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Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2023年第1期170卷

作者： Wang, Duo Yang, Kai Yang, Lixing Dong, Jianjun Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Peoples R China

Disaster relief logistics (DRL) provides adequate relief supplies to victims of natural disasters (e.g., earthquakes and volcanic eruptions). This study explicitly considers supplier selection and inventory pre-positioning corresponding to static preparedness decisions, and post-disaster procurement and delivery associated with dynamic response decisions in actual DRL operations. To tackle issues triggered by shortage and surplus of multi-class relief resources, a flexible option contract is adopted to purchase relief items from suppliers. To measure the risk of demand ambiguity, a worst-case mean-quantile-deviation criterion is introduced to reflect the decision-maker's risk-averse attitude. To handle the ambiguity in the probability distribution of demand, a novel two-stage distributionally robust optimization (DRO) model is developed for the addressed DRL problem. The proposed DRO model can be transformed into equivalent mixed-integer linear programs when the ambiguity sets incorporate all distributions within L1 -norm and joint L1- and L & INFIN;-norms from a nominal (reference) distribution. A computational study of earthquakes in Iran is conducted to illustrate the applicability of the proposed DRO model to real-world problems. The experimental results demonstrate that our proposed DRO model has superior out-of-sample performance and can mitigate the effect of Optimization Bias compared to the traditional stochastic programming model. Some managerial insights regarding the proposed approach are provided based on numerical results.

关键词： Disaster relief logistics Distributionally robust optimization stochastic programming Quantile deviation Ambiguity sets

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Trading Off Environmental and Economic Scheduling of a Renewable Energy Based Microgrid Under Uncertainties

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IEEE ACCESS 2023年 11卷 459-475页

作者： Zafar, Raheel Nezhad, Ali Esmaeel Ahmadi, Abdollah Erfani, Tohid Erfani, Rasool Lahore Univ Management Sci Dept Elect Engn Lahore 54792 Pakistan LUT Univ Sch Energy Syst Dept Elect Engn Lappeenranta 53850 Finland Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia UCL Dept Civil Environm & Geomat Engn London WC1E 6BT England Manchester Metropolitan Univ Dept Engn Manchester M1 5GD England

Smart power grids are transitioning towards effective employment of distributed energy resources including renewable energy sources to address the growing environmental concerns related to the pollutant emissions of fossil fuels. In this context, this paper proposes the directed search domain (DSD) method to compute the combined environmental and economic dispatch in a microgrid with battery energy storage systems, photovoltaic plants, wind turbines, fuel cells, and microturbines. The DSD algorithm is implemented for a multiobjective problem to obtain evenly-distributed Pareto optimal points by shrinking the original search domain into hypercone. This paper computes the optimal unit commitment and the related power dispatch while simultaneously minimizing the total pollutant emissions and operating costs. The best trade-off solution among the entire set of Pareto optimal points is computed by using the Fuzzy satisfying technique. The uncertainties associated with the forecasting of prices, load demand, wind, and photovoltaic power outputs are accounted for by employing the stochastic programming. The empirical results indicate the potential of the presented DSD algorithm in terms of the objective values, solution times, and quasi-even distribution of the Pareto set.

关键词： Microgrids Costs Pareto optimization Uncertainty Renewable energy sources Batteries Optimization Fuzzy systems stochastic processes Directed search domain method environmental/economic dispatch fuzzy decision making multiobjective optimization renewable energy based microgrid stochastic programming

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Farmland allocation in the conversion from conventional to organic farming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期311卷 1103-1119页

作者： Jahantab, Mahboubeh Abbasi, Babak Le Bodic, Pierre RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne VIC 3000 Australia Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

Organic farming enhances food quality and public health, and contributes to a more sustainable environment. Although certified organic farmland grew from 11 to 72.3 million hectares between 1999 and 2019, it constituted only 1.5 percent of the world's agricultural farmland in 2019. The main impediment to the conversion from conventional to organic farming is the financial difficulties that farmers experience during the transition period in terms of decrease in yield and increase in farming costs owing to transitional practices. Furthermore, uncertainty in crop price and yield may aggravate the adverse effects of transitional practices. This article presents a multi-period optimization model for the allocation of farmland among crops and agricultural practices which allows farmers to plan a transition to organic farming while incurring a bounded shortfall of income. We calibrate our model to represent a grower of corn and soybean in Iowa and, using a seemingly unrelated regression model, crops revenues are simulated and utilized in the numerical experiments. The results show that i) our optimized crop rotation pattern outperforms other policies in the agriculture industry, including monoculture and systematic crop rotation, and that ii) our gradual conversion plan mitigates the chance of profit shortfalls.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： OR in agriculture Conversion to organic farming Farmland allocation stochastic programming Sustainable operations

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Portfolio rebalancing based on time series momentum and downside risk

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2023年第2期34卷 355-382页

作者： Guo, Xiaoshi Ryan, Sarah M. Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

To examine the familiar tradeoff between risk and return in financial investments, we use a rolling two-stage stochastic program to compare mean-risk optimization models with time series momentum strategies. In a backtest of allocating investment between a market index and a risk-free asset, we generate scenarios of future return according to a momentum-based stochastic process model. A new hybrid approach, time series momentum strategy controlling downside risk (TSMDR), frequently dominates traditional approaches by generating trading signals according to a modified momentum measure while setting the risky asset position to control the conditional value-at-risk (CVaR) of return. For insight into the outperformance of TSMDR, we decompose each strategy into two aspects, the trading signal and the asset allocation model that determines the risky asset position. We find that 1) weighted moving average can better capture the trend of the stock market than time series momentum computed as past 12-month excess return, 2) mean-risk strategies generally provide better returns whereas risk parity strategies have less investment risk and 3) controlling CVaR limits the investment risk better than controlling variance does.

关键词： time series momentum mean-risk risk parity conditional value-at-risk stochastic programming scenario generation

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Dual SDDP for risk-averse multistage stochastic programs

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OPERATIONS RESEARCH LETTERS 2023年第3期51卷 332-337页

作者： da Costa, Bernardo Freitas Paulo Leclere, Vincent EMAp FGV Rio De Janeiro Brazil Ecole Ponts CERMICS Marne La Vallee France

Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. stochastic Dual Dynamic programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems. (c) 2023 Elsevier B.V. All rights reserved.

关键词： stochastic programming Dynamic programming SDDP Risk measures Duality

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Transmission Defense Hardening Against Typhoon Disasters Under Decision-Dependent Uncertainty

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第3期38卷 2653-2665页

作者： Zhang, Weixin Shao, Changzheng Hu, Bo Xie, Kaigui Siano, Pierluigi Li, Mushui Cao, Maosen Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa State Grid Chongqing Changshou Elect Power Supply Chongqing 401220 Peoples R China

Transmission defense hardening (TDH) is an effective practice to ensure the safe operation of the power system during and after natural disasters, such as typhoons. However, the coupling among the typhoon disaster, hardening decision, and transmission line status is not addressed in the previous research works. Besides, the enhancement measures for different types of transmission line failures have not yet been assessed. This paper proposes a comprehensive framework to improve power transmission system resilience against typhoon disasters. Firstly, the motion path and the wind field of typhoons are simulated using Monte Carlo sampling to quantify the spatiotemporal impacts of wind speed on the transmission line status. The decision-dependent uncertainty (DDU), which reflects the effects of hardening decisions on the transmission lines, is addressed by sampling according to the corresponding failure probabilities. Then, the scenario simulation is integrated into the proposed two-stage stochastic mixed-integer programming (SMIP) model considering various enhancement measures for different failures. Since the way to handle the DDU needs a large number of scenarios, it leads to high computation complexity. Hence, the sample average approximation (SAA) algorithm is introduced to cope with it. Finally, the proposed framework and its solution algorithm are carried out on the modified IEEE RTS-79 system and the modified IEEE 118-Bus system. The significant cost saving and resilience improvement demonstrate the effectiveness of the proposed framework.

关键词： Tropical cyclones Transmission line measurements Uncertainty Power transmission lines Costs Power systems stochastic processes Decision-dependent uncertainty transmission defense hardening typhoon disasters sample average approximation stochastic programming

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Blockchain for Energy Trading in Energy Communities Using stochastic and Distributed Model Predictive Control

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2023年第5期31卷 2132-2145页

作者： Sivianes, Manuel Maestre, Jose M. Zafra-Cabeza, Ascension Bordons, Carlos Univ Seville Sch Engn Syst Engn & Automat Dept Seville 41092 Spain Univ Seville ENGREEN Lab Engn Energy & Environm Sustainabil Seville 41092 Spain

In this article, a stochastic programming method is used to control a residential microgrid where possible disturbance realizations over a horizon are shaped into a tree structure with a given probability. This tree is used by model predictive controllers within a hierarchical and distributed scheme. The upper layer is characterized by a smart contract deployed in the blockchain, acting as a fully distributed coordinator that performs control tasks and collects and distributes relevant data. The bottom layer consists of individual agents that solve locally a reduced tree-based model predictive control (TBMPC) problem and interact with the smart contract to iteratively optimize the overall problem in a distributed fashion. The performance of the approach is demonstrated through several simulations in which various power-trading configurations are assessed.

关键词： Blockchain distributed control energy trading interconnected microgrids model predictive control (MPC) stochastic programming

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