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A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 104卷 102480-102480页

作者： Bashiri, Mahdi Nikzad, Erfaneh Eberhard, Andrew Hearne, John Oliveira, Fabricio Coventry Univ Sch Strategy & Leadership Fac Business & Law Coventry W Midlands England Shahed Univ Dept Ind Engn Fac Engn Tehran Iran RMIT Univ Sch Sci Melbourne Australia Aalto Univ Sch Sci Dept Math & Syst Anal Espoo Finland

In this paper, a two-stage stochastic programming model is developed for the asset protection routing problem (APRP) to be employed in anticipation of an escaped wildfire. In this model, strategic and tactical decisions are considered in a two-stage setting. The locations of protection depots are determined, taking into account the routing decisions under different possible scenarios. To solve the proposed model, the Frank-Wolfe Progressive Hedging decomposition approach is employed. A realistic case study set in south Hobart, Tasmania, is considered. In this study, the scenarios for uncertain parameters are generated based on real data, considering different sources of uncertainties such as wind direction and speed and total monthly rainfall. Computational experiments have been conducted to demonstrate the solution algorithm's efficiency in solving the asset protection routing problem with a two-stage stochastic framework. The numerical results suggest that more assets with higher values can be protected by considering the proposed two-stage stochastic programming model. The value of the approach is particularly significant where resources are limited, and uncertainty levels are high. Moreover, the model and solution procedure can be applied to other disaster situations in which protection activities occur. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Asset protection Location routing problem stochastic programming Frank-Wolfe Progressive Hedging method Wildfires

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A multi-year two-stage stochastic programming model for optimal design and operation of residential photovoltaic-battery systems

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ENERGY AND BUILDINGS 2021年 239卷 110835-110835页

作者： Zheng, Zhuang Li, Xin Pan, Jia Luo, Xiaowei City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Yeung Acad BldgTat Chee Ave Hong Kong Peoples R China Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

The residential PV-battery system is becoming economically viable due to technological advances. Selecting optimum investment portfolios and operation strategies are still the challenges faced by homeowners. This study proposed an integrated stochastic sizing-operating framework for optimal design and operation of grid-connected residential photovoltaic (PV)-battery systems. The two-stage stochastic programming method is adopted to address the uncertainties from load and PV productions, where 1) the first stage optimizes the investment portfolio of PV and battery, and 2) the second stage optimizes the energy system operation cost. A multi-year financial model is used to calculate the cash flows during the analysis period. The discounted net present values of expected net benefit (NB) are used as the maximization objective function to make optimal decisions for PV-battery investment and operation. Annual battery degradation factors estimate the aging battery effect. To separately evaluate the PV/battery profitability, we are the first to define the added value/cost metrics of PV/battery. Simulations based on realistic load/PV output profiles and estimated technical-economic parameters validated the effectiveness of the proposed framework and the added value/cost metrics of PV/battery components. The impacts of critical factors, including feed-in tariffs (FiT), tariff profiles and levels, and unit costs, were investigated to provide key findings to stakeholders. This framework can guide the homeowners to cost-effectively invest in and operate the distributed residential PV-battery system to facilitate the transition to a reliable, affordable, and clean building energy system. (c) 2021 Elsevier B.V. All rights reserved.

关键词： PV-battery Sizing-operating Multi-year stochastic programming Uncertainty Battery aging

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A two-phase optimization model combining Markov decision process and stochastic programming for advance surgery scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 160卷 107548-107548页

作者： Zhang, Jian Dridi, Mahjoub El Moudni, Abdellah Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands Univ Bourgogne Franche Comte Lab Nanomed Imagerie & Therapeut UTBM Rue Thierry Mieg F-90010 Belfort France

This paper addresses the advance scheduling of elective surgeries in an operating theater composed of operating rooms and a downstream surgical intensive care unit (SICU). The arrivals of new patients in each week, the duration of each surgery, and the length-of-stay of each patient in the SICU are subject to uncertainty. At the end of each week, the surgery planner determines the surgical blocks to open in the next week and assigns a subset of the surgeries on the waiting list to open surgical blocks. The objective is to minimize the patient-related costs incurred by performing and postponing surgeries as well as the hospital-related costs caused by utilization of surgical resources. Considering that the pure mathematical programming models commonly used in the literature mostly focus on the short-term optimization of surgery schedules, we propose a novel two-phase optimization model that combines Markov decision process (MDP) and stochastic programming to improve the long-term performance of surgery schedules. Moreover, in order to solve realistically sized problems efficiently, we develop a novel column-generation-based heuristic (CGBH) algorithm, then combine it with the sample average approximation (SAA) approach. The experimental results indicate that the SAA-CGBH algorithm is considerably more efficient than the conventional SAA approach, and that the optimal surgery schedules of the two-phase optimization model significantly outperform those of a pure stochastic programming model.

关键词： Advance surgery scheduling Two-phase optimization model Markov decision process stochastic programming Column-generation-based heuristic Sample average approximation

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Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: stochastic programming and Benders decomposition

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 155卷 102505-102505页

作者： Cheramin, Meysam Saha, Apurba Kumar Cheng, Jianqiang Paul, Sanjoy Kumar Jin, Hongyue Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Univ Technol Sydney UTS Business Sch Sydney NSW Australia

Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.

关键词： Rare earth magnet Supply chain optimization Reverse logistics stochastic programming Benders decomposition COVID-19 pandemic

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Selecting wood supply contracts under uncertainty using stochastic programming

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INFOR: Information Systems and Operational Research 2021年第2期59卷 191-211页

作者： Ali Rahimi Mikael Rönnqvist Luc LeBel Jean-François Audy a Département de Génie Mécanique Université Laval Québec Canada b Faculté de Foresterie de Géographie et de Géomatique Université Laval Québec Canada c Département de Management Université du Québec à Trois-Rivières Trois-Rivières Canada

A large portion of expenses in the forest industries is associated with wood supply procurement. Numerous suppliers are involved and securing wood supply contracts with competitive prices is a constant challenge for procurement managers. A major difficulty is the procurement exposure to various sourcing risks including the start of the spring thaw, contract breach, or unreliability of suppliers. A procurement plan should anticipate random events and include measures that counter their negative impact. Recourse actions must be planned by considering volume uncertainty and wood price fluctuations. Relying on manual tools is hardly capable of considering all aspects of this problem. A stochastic programming approach is proposed to support the development of a procurement plan. In this model, several types of contracts including fixed, flexible and option contracts with different durations are included. The proposed selection of contracts from a stochastic programming model yields average optimality in the presence of plausible scenarios. The developed two-stage stochastic programming model decides on the selection of the optimal portfolio of contracts to minimize total procurement costs. Based on a case study in Quebec, an average saving of 4% was shown by using stochastic programming compared to the deterministic approach.

关键词： stochastic programming wood supply procurement contract selection portfolio strategy

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A sensitivity analysis of stochastic programming for reverse logistic of herbs agro-industry: a case study of herbs logistic in Indonesia

A sensitivity analysis of stochastic programming for reverse...

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International Conference on Innovation in Technology and Management for Sustainable Agroindustry (ITaMSA)

作者： Rakhmasari, A. A. Djatna, T. Suparno, O. Rusli, M. S. Bogor Agr Univ Post Grad Program Agroind Technol Bogor Indonesia Minist Ind Polytech APP Dept Logist Management Jakarta Indonesia Bogor Agr Univ Dept Agroind Technol Postgrad Program Bogor Indonesia

This paper analyses the sensitivity of reverse logistic formulation of herbs agro-industry based on fuzzy stochastic mixed integer linear programming. A case study from real world problem of herbs logistic in Indonesia is provided in order to respond stochastic challenges in the reverse logistic system. For implementation purpose of this current progress, some related historical and hypothetical data were deployed. The model was then used to test how far this fuzzy quantitative modelling is capable to solve the problem within available data ranges with consideration on possibility in each data occurrence. A GRG non-linear was used as model solution to solve the fuzzy stochastic modelling with implementation using Excel solver. The fuzzy quantitative modelling result with a case study in herbs logistic in Indonesia is concluded with verification and validation on current model formulation for decision making purposes in herbs reverse logistic.

关键词： Herbs agroindustry herbs logistic reverse logistic sensitivity analysis stochastic programming

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The optimal management of the prosumer's resources via stochastic programming

The optimal management of the prosumer's resources via stoch...

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6th International Conference on Energy and Environment Research (ICEER) - Energy and Environment - Challenges Towards Circular Economy

作者： Beraldi, Patrizia Violi, Antonio Carrozzino, Gianluca Univ Calabria Dept Mech Energy & Management Engn Via P Bucci 41-C I-87036 Arcavacata Di Rende CS Italy Univ Sannio Dept Law Econ Management & Quantitat Methods Piazza Arechi 2 I-82100 Benevento BN Italy

This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving. (C) 2019 Published by Elsevier Ltd.

关键词： Home energy management systems Optimal scheduling Renewable energy stochastic programming Storage device

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A stochastic programming Approach for Configuration Optimization of Reconfigurable Manufacturing Systems

A Stochastic Programming Approach for Configuration Optimiza...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Cui, Feng Geng, Na Jiang, Zhibin Zhou, Xin Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China Shanghai Jiao Tong Univ Sino US Global Logist Inst Shanghai Peoples R China

ISBN: (纸本)9781538672204

As a new manufacturing paradigm, Reconfigurable Manufacturing System (RMS) has a modular structure, which can greatly improve the responsiveness and flexibility by adding or removing the modules of the reconfigurable machine tools (RMTs). However, the uncertain demand makes it difficult and challenging to configure the RMS. To deal with this problem, we propose a multi-period stochastic programming model to optimize the configuration of RMS in each period by considering the uncertain demand. The objective is to maximize total costs. Production planning related decisions are considered to better handle the unpredictable demand fluctuation. Numerical experiments compare the solutions by solving the proposed stochastic programming model and those by solving the deterministic linear model without considering production planning decisions. The results show that our model can provide a better solution in terms of the configuration and reconfiguration cost and total costs under the consideration of uncertain demand.

关键词： Reconfigurable Manufacturing System reconfigurable machine tools stochastic programming reconfiguration

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Asset Liability Management for the Parliamentary Pension Scheme of Uganda by stochastic programming

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Afrika Statistika 2021年第2期16卷 2689-2715页

作者： Herbert MUKALAZI Torbjörn LARSSON Juma KASOZI Fred MAYAMBALA

We develop a model for asset liability management of pension funds, which is solved by stochastic programming techniques. Using data provided by the Parliamentary Pension Scheme of Uganda, we obtain the optimal investment *** sampled scenario trees using the mean, and covariance structure of the return distribution are used for generating the coefficients of the stochastic program. Liabilities are modelled by remaining years of life expectancy and guaranteed period for monthly *** obtain the funding situation of the scheme at each stage under three different asset investment limits. Nous développons un modèle de gestion actif-passif des fonds de pension, qui est résolu par des techniques de programmation stochastiques. En utilisant les données fournies par le Régime de retraite parlementaire de l'Ouganda, nous obtenons les politiques d'investissement optimales. Des arbres de scénario échantillonnés de façon aléatoire à l'aide de la moyenne et de la structure de covariance de la distribution de retour sont utilisés pour générer les coefficients du programme stochastique. Les passifs sont modélisés en se limitant aux années restantes d'espérance de vie et à la période garantie de la pension mensuelle. Nous obtenons la situation de financement du régime à chaque étape sous trois limites d'investissement d'actifs différentes.

关键词： 62P05 90C15 asset liability management finance scenario generation stochastic programming

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Global-local Metamodel-assisted stochastic programming via Simulation

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2021年第1期31卷 2-2页

作者： Xie, Wei Yi, Yuan Zheng, Hua Northeastern Univ Boston MA 02115 USA Rensselaer Polytech Inst Troy NY 12181 USA 360 Huntington Ave 334 SN Boston MA 02115 USA 9605 Gold Coast Dr San Diego CA 92126 USA

To integrate strategic, tactical, and operational decisions, stochastic programming has been widely used to guide dynamic decision-making. In this article, we consider complex systems and introduce the global-local metamodel-assisted stochastic programming via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically, at each visited first-stage decision, we develop a local metamodel to simultaneously solve a set of scenario-based second-stage optimization problems, which also allows us to estimate the optimality gap. Then, we construct a global metamodel accounting for the errors induced by: (1) using a finite number of scenarios to approximate the expected future cost occurring in the planning horizon, (2) second-stage optimality gap, and (3) finite visited first-stage decisions. Assisted by the global-local metamodel, we propose a new simulation optimization approach that can efficiently and iteratively search for the optimal first- and second-stage decisions. Our framework can guarantee the convergence of optimal solution for the discrete two-stage optimization with unknown objective, and the empirical study indicates that it achieves substantial efficiency and accuracy.

关键词： Simulation optimization stochastic programming two-stage optimization Gaussian process metamodel dynamic decision-making

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