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multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

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NETWORKS 2025年第1期85卷 3-37页

作者： Bhattarai, Sudhan Song, Yongjia Clemson Univ Dept Ind Engn Clemson SC 29634 USA

In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre-positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two-stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto-regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out-of-sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.

关键词： disaster relief logistics network optimization hurricane evacuation Markov chain multistage stochastic programming

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A multistage stochastic programming approach for drone-supported last-mile humanitarian logistics system planning

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Jin, Zhongyi Ng, Kam K. H. Zhang, Chenliang Chan, Y. Y. Qin, Yichen Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Hong Kong Peoples R China Shanghai Maritime Univ Sch Econ & Management Shanghai Peoples R China

Drone-supported last-mile humanitarian logistics systems playa crucial role inefficiently delivering essential relief items during disasters. In contrast to conventional truck-based transportation methods, drones provide a versatile and rapid transportation alternative. They are capable of navigating challenging terrain and bypassing damaged infrastructure. However, establishing an effective drone-supported last-mile humanitarian logistics system faces various challenges. This study introduces a novel approach to address these challenges by proposing a drone-supported last-mile humanitarian logistics system planning (DLHLSP) problem. The DLHLSP problem involves decision-making for both pre-disaster and post-disaster phases, taking into account the unique characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, decisions include determining drone-supported relief facility locations, drone deployment strategies, and drone visit schedules to disaster sites. Post-disaster decisions focus on inventory management, relief item procurement, and drone-based delivery operations. To capture the demand uncertainty in chaotic disaster environment, we establish a multistage stochastic programming model incorporating nonanticipativity constraints to make decisions at each stage without knowledge of the demand information in future time periods. Next, we employ the Benders decomposition algorithm to obtain exact solutions. Furthermore, we perform numerical experiments to verify the exact algorithm using randomly generated numerical instances. The results show that the algorithm significantly outperforms the Gurobi solver and could solve the problem of practical scale. Finally, the study validates the proposed model based on a case study of the Lushan earthquake in China and provides several managerial implications and insights. Overall, this research contributes to the field of humanitarian logistics by offering a comprehensive framework

关键词： logistics system multistage stochastic programming Nonanticipativity constraints Benders decomposition

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multistage stochastic programming approach for joint optimization of job scheduling and material ordering under endogenous uncertainties

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期290卷 886-900页

作者： Sha, Yue Zhang, Junlong Cao, Hui Tsinghua Univ Dept Ind Engn Beijing Peoples R China Suning Com Artificial Intelligent Res Ctr Nanjing Peoples R China

Job scheduling incorporated with material ordering can better meet practical needs and lead to overall cost reduction. In this paper, we present a stochastic approach for this joint optimization problem, considering uncertainties in job processing times and resource consumptions. We formulate this integrated problem as a multistage stochastic mixed-integer program involving endogenous uncertainties. Several theoretical properties that can reduce the model size are studied. Based on this, a branch-and-bound exact algorithm and a sampling-based approximate method are designed as solution algorithms. The effectiveness of the integrated scheduling approaches and the efficiency of the proposed solution algorithms are evaluated via numerical experiments. It is shown that our approach can greatly reduce the overall cost compared with the traditional separate production planning approach, especially when production resources are not very restricted. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Job scheduling Material ordering multistage stochastic programming Endogenous uncertainties

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multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2014年 124卷 180-197页

作者： Gupta, Vijay Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water-oil ratio and gas-oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach. (c) 2014 Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming endogenous uncertainties non-anticipativity constraints Lagrangean decomposition oil & gas exploration production sharing agreements

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multistage stochastic programming Model of Portfolio Selection for Life Insurance Companies in China

Multistage Stochastic Programming Model of Portfolio Selecti...

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2nd International Conference on Business Intelligence and Financial Engineering

作者： Qu, Ran Qu, Zhenting Jilin Univ Sch Management Changchun 130023 Peoples R China Chinalife Insurance Co Branch of JiLin Changchun Peoples R China

ISBN: (纸本)9780769537054

In order to help Chinese life insurance companies effectively make their portfolio selection. A portfolio selection model was established by using the method of multistage stochastic programming in this paper. In this model the management and supervision reality of Chinese life insurance industry were transferred into constraints. The quarterly return rate data of Chinalife' investment and that of the assets in Chinese financial markets were gathered from 2004 to the first two quarters in 2008. A scenario tree was established by using these data. Benders Decomposition Algorithm was chosen to solve this model. Then the model was used to construct a series of portfolio for China life in 2009. The portfolio showed that China life should maintain over 70% of risk-free assets and less than 30% risk assets to achieve its objective of benefit and security. Also its insurance business should be concerned.

关键词： portfolio life insrance multistage stochastic programming scenario generation financial market

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multistage stochastic programming in strategic telecommunication network planning

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COMPUTATIONAL MANAGEMENT SCIENCE 2012年第3期9卷 303-321页

作者： Eisenblatter, Andreas Schweiger, Jonas Atesio GmbH D-12161 Berlin Germany Zuse Inst Berlin Dept Optimizat Takustr 7 D-14195 Berlin Germany

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.

关键词： UMTS Network evolution multistage stochastic programming

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multistage stochastic programming and its Applications in Energy Systems Modeling and Optimization

Multistage Stochastic Programming and its Applications in En...

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作者： Golari, Mehdi The University of Arizona

学位级别：Ph.D.

Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue co

关键词： multistage stochastic programming Renewable Energy Integration Systems & Industrial Engineering Energy systems optimization

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multistage stochastic programming model and solution algorithm for the capacity expansion of railway network

Multistage stochastic programming model and solution algorit...

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International Symposium on Scheduling (ISS)

作者： Shiina, Takayuki Takaichi, Tomoaki Li, Yige Morito, Susumu Imaizumi, Jun Waseda Univ Sch Creat Sci & Engn Shinjuku Ku 3-4-1 Okubo Tokyo 1698555 Japan Toyo Univ Fuculty Business Adm Bunkyo Ku 5-28-20 Hakusan Tokyo 1128606 Japan

We consider a capacity expansion problem for a railway network under uncertainty. In our approach, integer and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. Given the distribution of the number of customers in a scenario, the problem of minimizing the expected value of the total investment cost is considered. The problem is reformulated as a problem with first stage integer variables and continuous second stage variables. An L-shaped algorithm is proposed to solve this problem.

关键词： Railway planning Capacity expansion Optimization multistage stochastic programming L-shaped method

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multistage stochastic programming: Algorithms, Modelling and Applications

Multistage Stochastic Programming: Algorithms, Modelling and...

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作者： Siddig, Murwan Clemson University

学位级别：Ph.D., Doctor of Philosophy

This dissertation comprises four different topics related to multistage stochastic programming (MSP) algorithms, modeling, and applications. First, we extend the adaptive partition-based approach for solving two-stage stochastic programs with a fixed recourse matrix and a fixed cost vector to the MSP setting, where the stochastic process is assumed to be stage-wise independent. The proposed algorithms integrate the adaptive partition-based strategy with a popular approach for solving multistage stochastic programs, the stochastic dual dynamic programming (SDDP) algorithm, according to two main strategies. These two strategies are distinct from each other in the manner by which they refine the partitions during the solution process. In particular, we propose a refinement outside SDDP strategy whereby we iteratively solve a coarse scenario tree induced by the partitions and refine the partitions in a separate step outside of SDDP, only when necessary. We also propose a refinement within SDDP strategy where the partitions are refined in conjunction with the machinery of the SDDP algorithm. We then use, within the two different refinement schemes, different tree-traversal strategies, which allow us to have some control over the size of the partitions. Our numerical experiments on a hydro-thermal power generation planning problem show the effectiveness of the proposed algorithms in comparison to the standard SDDP algorithm. Moreover, the results show that the algorithms with the refinement outside SDDP strategy outperform the ones with the refinement within SDDP strategy. Second, we study an important question related to solving MSP problems with a large number of stages. A common approach to tackle MSP problems with a large number of stages is a rolling-horizon (RH) procedure, where one solves a sequence of MSP problems with a smaller number of stages. This leads to a delicate issue of how many stages to include in the smaller problems used in the RH procedure. We study

关键词： multistage stochastic programming stochastic Dual Dynamic programming Algorithms Hydrothermal power generation Logistics costs Decision making Planning

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A multistage stochastic programming formulation to evaluate feedstock/process development for the chemical process industry

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CHEMICAL ENGINEERING SCIENCE 2018年 187卷 223-244页

作者： Christian, Brianna Cremaschi, Selen Auburn Univ Dept Chem Engn Samuel Ginn Coll Engn 345 W Magnolia Ave Auburn AL 36849 USA

Industry trends suggest that the feedstock and product portfolios along with utilized technologies of chemical process industry (CPI) may grow and be quite different compared to today's in the near future. Incorporation of new feedstocks and technologies into the existing CPI infrastructure may require significant amounts of investments. Determining the investment decisions, i.e., how much to invest, which technologies to invest in, and when to invest in each technology for research and development and for capacity expansion, is a challenging task, because there are often many emerging technologies and the future performances of these technologies are uncertain. Here, we present a multistage stochastic programming (MSSP) formulation accounting for both endogenous and exogenous uncertain parameters associated with the new technology investment planning (NTIP) problem. The MSSP formulation is used to determine investment decisions for four case studies, including a biomass to ethylene production network. The solution for the biomass to ethylene case study suggests that, given the current market conditions, the investment in biomass technologies is not financially advantageous. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： multistage stochastic programming Endogenous uncertainty Exogenous uncertainty Investment planning Mixed integer non-linear programming

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