Article
Management
Nishit Bhavsar, Manish Verma
Summary: This study investigates the use of subsidies as a risk mitigation tool for hazardous materials transportation by rail. By developing an optimization model and a customized solution technique, the study shows the significance of subsidies in reducing risk and ensuring fair distribution of risk.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Atiq W. Siddiqui, Hassan Sarhadi, Manish Verma
Summary: This study proposes a conditional value-at-risk (CVaR) based methodology for selecting the appropriate size of crude oil tankers and routing them over the intercontinental network to minimize the weighted sum of transport cost and CVaR based transport risk. Additionally, a robust formulation of the mixed-integer routing problem is developed to handle imprecision in the risk data and prepare risk-averse shipment plans. The benefits of the proposed methodology are demonstrated through the analysis of a major oil supplier's realistic marine network, highlighting the value of incorporating robustness.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Liang Zheng, Ji Bao, Zhenyu Mei
Summary: This study proposes a passive countermeasure for cybersecurity in urban traffic signal control systems by designing robust signal control plans. The proposed approach is evaluated on an urban road network in Changsha, China, and the results show that the developed robust signal control plans can withstand attacks and perform better than the counterpart ones. The proposed bi-level modeling framework and solution algorithm can be used to build robust traffic signal control systems that are resilient to cyberattacks.
INFORMATION SCIENCES
(2023)
Article
Energy & Fuels
Reza Lotfi, Nooshin Mardani, Gerhard-Wilhelm Weber
Summary: This study utilizes robust bi-level programming technique and game theory to locate renewable energy sites, demonstrating that the incorporation of uncertainties can enhance energy generation and supplier's profit. Sensitivity analysis shows that increasing uncertainty decreases generated energy but increases supplier's profit; raising the discounting rate gradually diminishes supplier's profit; as the scale of problems increases, both generated energy and supplier's profit are boosted.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Engineering, Industrial
Jinpei Wang, Xuejie Bai, Yankui Liu
Summary: Hazardous materials transportation poses great risks due to their inflammable, explosive, and corrosive properties. In order to address the challenges of the hierarchical relationship and ambiguous accident probability, we propose a bilevel optimization method and construct uncertainty sets. Our robust model improves transportation safety and can be transformed into a computationally tractable linear programming model. We apply the model to a real transport case and compare it with traditional optimization methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Chemical
Wanke Han, Tijun Fan, Shuxia Li, Liping Liu
Summary: This study proposes a new multimodal network model that considers a detour strategy and uncertainty in hazmat transportation. The study demonstrates through a case study using simulated data that changes in demand uncertainty and transit discount factor affect the total cost and risk of the multimodal hub network, thus influencing carrier's location and route decisions. The new model can effectively control and mitigate risk, making it more suitable for hazmat transportation.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2023)
Article
Thermodynamics
Boyu Chen, Yanbo Che, Zhihao Zheng, Shuaijun Zhao
Summary: This paper develops an optimal bidding strategy for a data center operator (DCO) participating in the day-ahead electricity market. The DCO is regarded as a price maker managing several data centers (DCs) located in different buses of the transmission network (TN) with local renewable power generators.
Article
Computer Science, Information Systems
Li Wang, Yuanli Cai, Baocang Ding
Summary: A robust model predictive control with bi-level optimization is proposed for a nonlinear boiler-turbine system. The method effectively improves the economic performance of the system by optimizing control moves and control policy under different operating conditions. Simulation results have demonstrated the effectiveness of the proposed method.
Article
Engineering, Electrical & Electronic
Zeeshan Akhtar, Amrit Singh Bedi, Srujan Teja Thomdapu, Ketan Rajawat
Summary: This work introduces the first SBFW algorithm to solve stochastic bi-level optimization problems in a projection-free manner. It also proposes the SCFW algorithm for stochastic compositional optimization problems. Extensive numerical tests demonstrate the usefulness and flexibility of SBFW and SCFW algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Naga Yasasvi Puvvada, Abheejeet Mohapatra, Suresh Chandra Srivastava
Summary: This paper proposes a novel nonlinear dual-based bi-level approach for robust AC Transmission Expansion Planning (TEP) with uncertainties in RES generations and loads. It introduces a convex relaxation and utilizes Benders Decomposition (BD) to solve the problem. The approach is tested on various systems, demonstrating its robustness and efficacy.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Omid Homaee, Arsalan Najafi, Michal Jasinski, Georgios Tsaousoglou, Zbigniew Leonowicz
Summary: This paper discusses the coordination problem of dispatch actions and energy exchange between two interconnected ADNs. Bilateral energy trading allows neighboring ADNs to benefit from locational marginal price differences. The problem is formulated as a robust bi-level program and solved using the KKT conditions and ADMM. Simulation results show the convergence behavior of the proposed method and quantify the value of DSO-DSO coordination in the presence of an interconnecting line between the ADNs.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Economics
Xin Wang, Yong-Hong Kuo, Houcai Shen, Lianmin Zhang
Summary: The study proposes a target-oriented framework for a multi-period location-transportation problem, addressing issues arising from estimating the weights of different objectives in multi-objective optimization approach.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Engineering, Electrical & Electronic
Ceyao Zhang, Tianjian Zhang, Feng Yin, Abdelhak M. Zoubir
Summary: M-estimators are commonly used in robust regression for handling heavy-tailed data corrupted by outliers. This paper proposes a data-driven approach to determine optimal tuning parameters based on the dataset itself. It formulates the tuning problem as a novel bi-level optimization framework, solves the regression model parameters and tuning parameters jointly, and employs an online approximation strategy for efficient optimization. The proposed framework is generic and applicable to differentiable loss functions of any parametric regression model and M-estimator. Positive simulation results are presented compared to various benchmarks.
Article
Engineering, Electrical & Electronic
Chi Zhang, Guohong Zeng, Jian Wu, Shaoyuan Wei, Weige Zhang, Bingxiang Sun
Summary: This paper addresses the coupled optimization problem of driving strategy and energy management for diesel-electric hybrid trains (DEHT) equipped with battery systems. A bi-level optimization method is proposed, with the top level using particle swarm optimization to obtain the optimal driving strategy, and the bottom level solving a convex optimization problem to allocate power for the diesel generator set and battery. Results show that the introduced optimization method can achieve a maximum fuel consumption reduction of 20.0% and a minimum reduction of 2.7%.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Operations Research & Management Science
Pulak Swain, Akshay Kumar Ojha
Summary: This study focuses on robust portfolio optimization under uncertain conditions, converting uncertain problems into bi-level optimization models and deriving robust solutions. Researchers have shifted their interest towards robust optimization techniques for uncertain mean-variance problems, aiming to achieve stable results in uncertain conditions.
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