A robust optimization approach to risk-averse routing of marine crude oil tankers

被引:6
|
作者
Siddiqui, Atiq W. [1 ]
Sarhadi, Hassan [2 ]
Verma, Manish [3 ]
机构
[1] Imam Abdulrahman Bin Faisal Univ, Coll Business Adm, POB 1982, Dammam 31451, Saudi Arabia
[2] Acadia Univ, Fred C Manning Sch Business, Wolfville, NS, Canada
[3] McMaster Univ, DeGroote Sch Business, Hamilton, ON, Canada
关键词
Risk assessment; Conditional value -at -risk; Marine transportation; Crude oil shipments; Robust optimization; VALUE-AT-RISK; HAZARDOUS MATERIAL TRANSPORTATION; MARITIME TRANSPORTATION; PLANNING-MODEL; PETROLEUM-PRODUCTS; DECISION-MAKING; SPILL; COST; METHODOLOGY; SIMULATION;
D O I
10.1016/j.cie.2022.108878
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Marine accidents involving crude oil tankers are rare though such incidents have led to catastrophic socioeco-nomic and environmental consequences. This low probability - high consequence nature of marine oil spills renders the existing expected consequence technique ineffective and calls for the development of a risk-averse approach. We propose a conditional value-at-risk (CVaR) based methodology for selecting the appropriate size of crude oil tankers and then routing them over the intercontinental network such that the weighted sum of transport cost and CVaR based transport risk is minimized. In addition, we also develop a robust formulation of the mixed-integer routing problem, which not only facilitates tiding over imprecision in the risk data but also prepares risk-averse shipment plan. The proposed methodology was used to analyze the realistic marine network of a major oil supplier and to demonstrate its benefits, and to underscore the value of incorporating robustness.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] A robust optimization approach for risk-averse energy transactions in networked microgrids
    Wang, Luhao
    Li, Qiqiang
    Cheng, Xingong
    He, Guixiong
    Li, Guanguan
    Wang, Rui
    INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS, 2019, 158 : 6595 - 6600
  • [2] Risk-averse Distributional Reinforcement Learning: A CVaR Optimization Approach
    Stanko, Silvestr
    Macek, Karel
    IJCCI: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE, 2019, : 412 - 423
  • [3] Design optimization for resilience for risk-averse firms
    Giahi, Ramin
    MacKenzie, Cameron A.
    Hu, Chao
    COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 139
  • [4] Robust multicriteria risk-averse stochastic programming models
    Xiao Liu
    Simge Küçükyavuz
    Nilay Noyan
    Annals of Operations Research, 2017, 259 : 259 - 294
  • [5] Robust multicriteria risk-averse stochastic programming models
    Liu, Xiao
    Kucukyavuz, Simge
    Noyan, Nilay
    ANNALS OF OPERATIONS RESEARCH, 2017, 259 (1-2) : 259 - 294
  • [6] A survey on risk-averse and robust revenue management
    Goensch, Jochen
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2017, 263 (02) : 337 - 348
  • [7] Risk-averse optimization and resilient network flows
    Eshghali, Masoud
    Krokhmal, Pavlo A.
    NETWORKS, 2023, 82 (02) : 129 - 152
  • [8] Multivariate robust second-order stochastic dominance and resulting risk-averse optimization
    Chen, Zhiping
    Mei, Yu
    Liu, Jia
    OPTIMIZATION, 2019, 68 (09) : 1719 - 1747
  • [9] Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand
    Zhong, Shaopeng
    Cheng, Rong
    Jiang, Yu
    Wang, Zhong
    Larsen, Allan
    Nielsen, Otto Anker
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 141
  • [10] Risk-averse Oil-spill Response Planning
    Liu, Z.
    Sarhadi, H.
    2021 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM21), 2021, : 1157 - 1163