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Integrated stochastic disassembly line balancing and planning problem with machine specificity

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第5期60卷 1688-1708页

作者： He, Junkai Chu, Feng Dolgui, Alexandre Zheng, Feifeng Liu, Ming Univ Paris Saclay Univ Evry Lab IBISC F-91025 Evry France Donghua Univ Glorious Sun Sch Business & Management Shanghai Peoples R China CNRS IMT Atlantique LS2N Dept Automat Prod & Comp Sci Nantes France Tongji Univ Sch Econ & Management Shanghai Peoples R China

The disassembly is a fundamental basis in converting End-of-Life (EOL) products into useful components. Related research becomes popular recently due to the increasing awareness of environmental protection and energy conservation. Yet, there are many opening questions needed to be investigated, especially the efficient coordination of different-level decisions under uncertainty is a big challenge. In this paper, a novel integrated stochastic disassembly line balancing and planning problem is studied to minimise the system cost, where component yield ratios and demands are assumed to be uncertain. In this work, machine specificities are considered for task processing, such as price, ability, and capacity. For the problem, a two-stage non-linear stochastic programming model is first constructed. Then, it is further transformed into a linear formulation. Based on problem property analysis, a valid inequality is proposed to reduce the search space of optimal solutions. Finally, a sample average approximation (SAA) and an L-shaped algorithm are adopted to solve the problem. Numerical experiments on randomly generated instances demonstrate that the valid inequality can save around 11% of average computation time, and the L-shaped algorithm can save around 64% of average computation time compared with the SAA algorithm without a big sacrifice of the solution quality.

关键词： Integrated stochastic disassembly line balancing and planning machine specificity two-stage stochastic programming valid inequality SAA and L-shaped

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Modeling and solution algorithm for a disaster management problem based on Benders decomposition

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IISE TRANSACTIONS 2022年第12期54卷 1161-1171页

作者： MirHassani, Seyed Ali Garmroudi, Fatemeh Hooshmand, Farnaz Amirkabir Univ Technol Fac Math & Comp Sci Tehran Polytech Tehran Iran

Pre-disaster planning and management activities may have significant effects on reducing post-disaster damages. In this article, a two-stage stochastic programming model is provided to design a resilient rescue network assuming that the demands for relief items and the network functionality after the disaster are affected by uncertainty. Locations and capacities of relief centers, the inventory of relief items, and strengthening vulnerable arcs of the network are among the main decisions that must be taken before the disaster. Servicing the affected points is decided after the disaster, and the risk of not satisfying demands is controlled by using the conditional-value-at-risk measure. Since the direct resolution of the model is intractable and time-consuming over actual large-sized instances, an improved Benders decomposition algorithm based on the problem structure is proposed to overcome this difficulty. Computational results highlight the effectiveness of the proposed method compared to the existing approaches.

关键词： two-stage stochastic programming conditional-value-at-risk disaster management Benders decomposition valid cuts

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Integrating wholesale and retail electricity markets considering financial risks using stochastic programming

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年第PartA期142卷

作者： Bagheri, Abed Jadid, Shahram Iran Univ Sci & Technol IUST Ctr Excellence Power Syst Automat & Operat Sch Elect Engn Tehran Iran

The prevalent paradigm of power system operations has mostly centered on placing all the intelligence in resources that are connected to transmission networks, whereas distribution grids are often not accounted for in-depth and put aside by only approximating some limited behaviors. Nonetheless, proliferation of distributed energy resources, e.g. solar panels and wind turbines, in the distribution networks, combined with a significant amount of load uncertainties in different consumption sectors, necessitates the integration of a significant amount of intelligence at the distribution level of electric power systems. Hence, distribution system operators are becoming more involved in the operation of electric power systems and electricity markets. This paper opens a branch of knowledge for simultaneously clearing wholesale and retail electricity markets relying on (i) a fully-decentralized algorithm able to simultaneously run by all system operators, preserving privacy aspects of all market players and data security over the whole system, (ii) considering inherent uncertainty of electricity generation of renewable units and power consumption of customers using a stochastic programming approach, (iii) modeling and analyzing financial risk of the uncertain parameters and their effects on all parts of the system. Results obtained from software simulations indicate the high ability of the proposed structure in making integrated electricity markets with maximum performance and efficiency considering numerous limitations in the system.

关键词： Decentralized optimization Electricity market Risk management two-stage stochastic programming Uncertainty

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A binary decision diagram based algorithm for solving a class of binary two-stage stochastic programs

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MATHEMATICAL programming 2022年第1期191卷 381-404页

作者： Lozano, Leonardo Smith, J. Cole Clemson Univ Dept Ind Engn 278 Freeman Hall Clemson SC 29634 USA Clemson Univ Dept Ind Engn 100 Freeman Hall Clemson SC 29634 USA

We consider a special class of two-stage stochastic integer programming problems with binary variables appearing in both stages. The class of problems we consider constrains the second-stage variables to belong to the intersection of sets corresponding to first-stage binary variables that equal one. Our approach seeks to uncover strong dual formulations to the second-stage problems by transforming them into dynamic programming (DP) problems parameterized by first-stage variables. We demonstrate how these DPs can be formed by use of binary decision diagrams, which then yield traditional Benders inequalities that can be strengthened based on observations regarding the structure of the underlying DPs. We demonstrate the efficacy of our approach on a set of stochastic traveling salesman problems.

关键词： two-stage stochastic programming Integer programming Benders' decomposition Binary decision diagrams

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A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty

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HEALTH CARE MANAGEMENT SCIENCE 2023年第2期26卷 238-260页

作者： Lalmazloumian, Morteza Baki, M. Fazle Ahmadi, Majid Univ Windsor Dept Ind & Mfg Syst Engn Windsor ON N9B 3P4 Canada Univ Windsor Odette Sch Business 401 Sunset Ave Windsor ON N9B 3P4 Canada Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

Surgery demand is an uncertain parameter in addressing the problem of surgery block allocations, and its typical variability should be considered to ensure the feasibility of surgical planning. We develop two models, a stochastic recourse programming model and a two-stage stochastic optimization (SO) model with incorporated risk measure terms in the objective functions to determine a planning decision that is made to allocate surgical specialties to operating rooms (ORs). Our aim is to minimize the costs associated with postponements and unscheduled demands as well as the inefficient use of OR capacity. The results of these models are compared using a case of a real-life hospital to determine which model better copes with uncertainty. We propose a novel framework to transform the SO model based on its deterministic counterpart. Three SO models are proposed with respect to the variability and infeasibility of the measures of the objective function to encode the construction of the SO framework. The analysis of the experimental results demonstrates that the SO model offers better performance under a highly volatile demand environment than the recourse model. The originality of this work lies in its use of SO transformation framework and its development of stochastic models to address the problem of surgery capacity allocation based on a real case.

关键词： stochastic optimization two-stage stochastic programming Operating room planning Surgery capacity allocation Demand uncertainty Operations research Operations management Scheduling

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A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios

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INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2022年第1期20卷 75-89页

作者： Li, Xiaoming Gao, Jie Wang, Chun Huang, Xiao Concordia Univ Montreal Informat Syst & Engn Montreal PQ Canada Concordia Univ Montreal John Molson Sch Business Montreal PQ Canada

To reduce the vehicle relocation rate considering relieving disequilibrium of the supply-demand ratios across regions for car-sharing systems, in this paper, we propose a data-driven optimization framework by integrating the non-parametric learning algorithm and two-stage stochastic programming modeling technique to address the one-way station-based car-sharing relocation problem. In contrast with the most existing work that deals with demand uncertainty using predefined probability distributions, the learning-based framework is capable of handling demand uncertainty by learning the intrinsic pattern from large-scale historical data and computing high quality solutions. To validate the performance of our proposed approach, we conduct a group of numerical experiments based on New York taxicab trip record data set. The experimental results show that our proposed data-driven approach outperforms the parametric approaches and deterministic model in terms of business profit, relocation rate, and value of stochastic solution (VSS). Most significantly, compared with the deterministic approach, the vehicle relocation rates are reduced by approximate 80%, 70% and 40% under small fleet size, medium fleet size and large fleet size, respectively. In addition, the VSS of our approach is more than 3 times higher than the one of Poisson distribution by average.

关键词： Data-driven optimization two-stage stochastic programming Non-parametric density estimation Car-sharing

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two-stage stochastic programming problems involving interval discrete random variables

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OPSEARCH 2012年第3期49卷 280-298页

作者： Barik, Suresh Biswal, Mahendra Chakravarty, Debashish Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India Indian Inst Technol Dept Min Engn Kharagpur 721302 W Bengal India

In this paper, we propose a two-stage stochastic linear programming problem considering some of the left hand side and right hand side of linear constraints parameters as interval discrete random variables with known probability distribution and rest of the parameters are precisely known. Both the randomness and discrete intervals are simultaneously considered for the model parameters. Further, the concepts of best optimum and worst optimum solution are studied in two-stage stochastic programming. To solve the stated problem, first we remove the randomness from the problem and formulate an equivalent deterministic linear programming model with interval coefficients. Then the deterministic model is solved using the solution procedure of linear programming with interval coefficients. We obtain the upper bound and lower bound of the objective function as the best and the worst value respectively. It highlights the possible risk involved in the decision making process. A numerical example is presented to demonstrate the usefulness of the proposed methodology.

关键词： stochastic programming two-stage stochastic programming Interval linear programming Interval discrete random variables

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Design and operation of renewable energy microgrids under uncertainty towards green deal and minimum carbon emissions

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SUSTAINABLE ENERGY GRIDS & NEtwoRKS 2024年 37卷

作者： Tatar, Su Meyra Aydin, Erdal Koc Univ Dept Chem & Biol Engn TR-34457 Istanbul Turkiye Bogazici Univ Dept Chem Engn TR-34342 Bebek Istanbul Turkiye Koc Univ Koc Univ TUPRAS Energy Ctr KUTEM TR-34450 Istanbul Turkiye

The regulations regarding the Paris Agreement are inevitable to keep the global temperature rise within 2 degrees C. Moreover, integrating renewable energy-based equipment and adopting new ways of producing energy re-sources, for example Power to Gas (PtG) technology, becomes essential because of the current environmental, economic, and political concerns. It is vital to supply the growing energy demand with the increasing population. Uncertainty must be considered in the transition phase since parameters regarding the electricity demand, carbon tax policies, and intermittency of renewable energy-based equipment have intermittent nature. A multi-period two-stage stochastic MILP model is proposed in this work where the wind speed, solar irradiance, tem-perature, power demand, carbon emission trading (CET) price, and CO2 emission limit are considered uncertain parameters. Three stochastic case studies with scenarios including different combinations of the aforementioned uncertain parameters are investigated. Results show that more renewable energy-based equipment with higher rated power values is chosen as the sanctions get stricter. In addition, the optimality of PtG technology is also investigated for a specific location. Implementing the CO2 emission limit as an uncertain parameter instead of including CET price as uncertain results in lower annual CO2 emission rates and higher net present cost values.

关键词： Optimal renewable energy integration Power-to-gas two-stage stochastic programming Carbon trade Carbon price Green hydrogen

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Risk-aware two-stage stochastic short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station and end-user cooperation

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JOURNAL OF ENERGY STORAGE 2024年 78卷

作者： Shaillan, Haider Muaelou Tohidi, Sajjad Hagh, Mehrdad Tarafdar Tabar, Vahid Sohrabi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

The interconnection between different energy networks considerably improves their technical and economical aspects;however, it increases the complexity of decisions. This paper optimizes the short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station composed of charging facilities, battery swapping slots, parking lots and hydrogen and gasoline refueling parts. In order to achieve the optimal operation in such a complex system and consider the influence of day-ahead and real-time decisions under uncertainties, a risk-aware two-stage stochastic programming is suggested in the presented study. In this regard, fluctuations of wind and solar units and different types of electrical and thermal loads and markets are modeled using various scenarios and their risk is measured by the downside risk method. In addition, the impact of end-user cooperation is analyzed and a sensitivity analysis is implemented. The simulations demonstrate that the proposed model can successfully specify the behavior of all components under a risk-averse strategy with maximum robustness, where the profit is decreased by 8.4 % to achieve an expected risk in profit equal to 0 $. The results also validate that although the all-in-one station can act as a bulk storage, a multi-directional energy injection is required to provide its high demand.

关键词： All-in-one vehicle station Flexible loads Multi-microgrid Risk analysis Renewable resources two-stage stochastic programming

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Minimizing the Expected Renewable Resource Costs in a Project with stochastic Resource Availability

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2024年第1期41卷 2350008-2350008页

作者： Moghaddaszadeh, Hossein Ranjbar, Mohammad Jamili, Negin Ferdowsi Univ Mashhad Fac Engn Dept Ind Engn Mashhad Iran Erasmus Univ Rotterdam Sch Management Rotterdam Netherlands

In this paper, we study the well-known resource availability cost problem with stochastic resource availability. The objective is to determine the initial levels of all renewable resources and establish a schedule corresponding to each scenario such that the expected resource availability cost is minimized. We assume that resource shortfalls can be compensated externally but at a noticeable higher cost. We formulate the problem as a two-stage stochastic programming model (TSSPM). We also develop an exact decomposition-based algorithm (DBA) for the particular case of the problem with at most two resources, which also functions as a heuristic for the original problem. Since the number of scenarios influences the performance of the developed solution approaches, we utilize a fast scenario reduction method to reduce the number of scenarios. Computational results indicate that the DBA outperforms the TSSPM formulation in solution quality and CPU runtime.

关键词： Project scheduling resource availability cost problem two-stage stochastic programming

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