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Synthesis of carbon capture, utilization, and sequestration processing pathways using state-task network representation

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IFAC-PapersOnLine 2023年第2期56卷 6888-6893页

作者： Wonsuk Chung Ali S. Al-Hunaidy Hasan Imran Jay H. Lee Clean Energy Research Center Korea Institute of Science and Technology (KIST) 02792 Seoul Republic of Korea Carbon Management Division Research & Development Center Saudi Aramco Dhahran 31311 Saudi Arabia Mork Family Department of Chemical Engineering and Material Science University of Southern California VHE310 3651 Watt Way Los Angeles California United States

Synthesis of sustainable processing pathway is an important initial step in deciding investments in carbon capture, utilization, and sequestration (CCUS). For a best decision, it is necessary to analyze a very large number of potential processing pathways at once in terms of their economics and net carbon emission. Such analysis may also reveal which parts of a pathway incur significant portions of the costs and carbon emissions, suggesting hotspots for improvement. Frameworks used should also be flexible enough to accommodate varying feed conditions and market/emission data as they tend to vary according to the sources of CO 2 and geographical locations. The superstructure method along with a state-task network (STN) representation offers such flexibility in analyzing a CCUS system. This work proposes to use a STN representation of a process in a superstructure composed of feeds, processes, and products, to represent and optimize among various options of CCUS pathways through mathematical programming. A case study is conducted to illustrate the utility of the STN representation in the CCUS superstructure optimization.

关键词： Carbon capture carbon utilization superstructure state-task network representation mixed-integer programming techno-economic assessment carbon life cycle assessment

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Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年第1期88卷

作者： Akbari, Leilanaz Kazemi, Ahmad Salari, Majid Ferdowsi Univ Mashhad Dept Ind Engn POB 91775-1111 Mashhad Iran Monash Univ Sch Math Melbourne Australia

Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.

关键词： Emergency services Simulation mixed-integer programming Deployment and relocation

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Vehicle Speed and Gear Position Co-Optimization for Energy-Efficient Connected and Autonomous Vehicles

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2021年第4期29卷 1721-1732页

作者： Shao, Yunli Sun, Zongxuan Univ Minnesota Twin Cities Dept Mech Engn Minneapolis MN 55455 USA Oak Ridge Natl Lab Oak Ridge TN 37932 USA

This work proposes a real-time implementable control strategy to optimize vehicle speed and transmission gear position simultaneously for connected and autonomous vehicles (CAVs). Co-optimization of vehicle speed and transmission gear position has the advantage to maximize the fuel benefits. Drivability is considered during the optimization to satisfy the acceleration requirement and avoid shift busyness. The target vehicle's speed and gear position are controlled intelligently using predicted future traffic conditions based on information enabled by connectivity. The optimal control problem is a hybrid one with both continuous (vehicle speed) and discrete (gear position) control inputs. The problem is formulated and simplified to a mixed-integer programming problem with a convex quadratic objective function and mixed-integer linear constraints. The optimal control solutions are obtained in real time using an efficient numerical solver in the model predictive control (MPC) fashion. Future traffic conditions are anticipated using a traffic prediction method based on a traffic flow model. The traffic prediction method can be applied to scenarios where both connected vehicles and nonconnected vehicles share the road. As a case study, a vehicle platooning scenario on an urban road is evaluated in both simulation and experiment. The target vehicle is at the end of the vehicle platoon and follows the preceding vehicle. The average computational time of the optimization is 0.44 s. By co-optimizing vehicle speed and gear position, the target vehicle can achieve 10.6% fuel benefits compared with the immediate preceding vehicle and 8.9% energy benefits compared with a human-driven vehicle (driven by VISSIM's car-following model). The proposed control strategy can be potentially extended to various CAV applications and traffic scenarios as well.

关键词： Gears Optimal control Engines Optimization Mechanical power transmission Power demand Mathematical model Autonomous vehicle connected vehicle (CV) ecodriving hardware-in-the-loop test mixed-integer programming model predictive control (MPC) optimal control traffic prediction

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Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

Deep Reinforcement Learning-based Task Offloading in Satelli...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Zhu, Dali Liu, Haitao Li, Ting Sun, Jiyan Liang, Jie Zhang, Hangsheng Geng, Liru Liu, Yudong Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728195056

In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.

关键词： Satellite-terrestrial networks Edge computing Deep reinforcement learning Computation offloading mixed-integer programming

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Learning Lyapunov Functions for Hybrid Systems 24

Learning Lyapunov Functions for Hybrid Systems

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24th ACM International Conference on Hybrid Systems Computation and Control (HSCC)

作者： Chen, Shaoru Fazlyab, Mahyar Morari, Manfred Pappas, George J. Preciado, Victor M. Univ Penn Philadelphia PA 19104 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781450383394

We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-time autonomous hybrid systems that admit a mixed-integer representation. Such systems include autonomous piecewise affine systems, closed-loop dynamics of linear systems with model predictive controllers, piecewise affine/linear complementarity/mixed-logical dynamical systems in feedback with a ReLU neural network controller, etc. The proposed method comprises an alternation between a learner and a verifier to search for a Lyapunov function from a family of parameterized Lyapunov function candidates. In each iteration, the learner uses a collection of state samples to select a Lyapunov function candidate through a convex program in the parameter space. The verifier then solves a nonconvex mixed-integer quadratic program in the state space to either validate the proposed Lyapunov function candidate or reject it with a counterexample, i.e., a state where the Lyapunov condition fails. This counterexample is then added to the sample set of the learner to refine the set of Lyapunov function candidates in the next iteration. By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps. We demonstrate our stability analysis method on closed-loop MPC dynamical systems and a ReLU neural network controlled PWA system.

关键词： Hybrid system stability verification piecewise-affine systems Lyapunov function mixed-integer programming mixed-integer formulations counterexample guided synthesis

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Exact and heuristic approaches to Truck-Drone Delivery Problems

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2023年 12卷

作者： Freitas, Julia C. Penna, Puca Huachi V. Toffolo, Tulio A. M. Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil

Collaborative delivery employing drones in last-mile delivery has been an extensively studied topic in recent years. In this paper, it is studied Truck-Drone Delivery Problems (TDDPs) in which a traditional delivery truck is gathered with a drone to cut delivery times and costs. The vehicles work together in a hybrid operation involving one drone launching from a larger vehicle that operates as a mobile depot and a recharging platform. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel mixed integer programming (MIP) formulation and a heuristic approach to address the problem. The proposed MIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.

关键词： Unmanned Aerial Vehicle Traveling Salesman Problem Drone delivery mixed-integer programming General Variable Neighborhood Search

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A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2023年 26卷

作者： Als, Matthias Villads Hinsch Madsen, Mathias Bejlegaard Jensen, Rune Moller IT Univ Copenhagen Rued Langgaards Vej 7 DK-2300 Copenhagen S Denmark

Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To our knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. Our results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.

关键词： Multi-objective optimisation Energy-efficient train timetabling Matheuristic mixed-integer programming Genetic algorithm Decision support

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Modeling social, economic, and health perspectives for optimal pandemic policy decision-making

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年 86卷 101472页

作者： Soltanisehat, Leili Gonzalez, Andres Barker, Kash Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented e-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Paretooptimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

关键词： Pandemic SIRD model Maximum network flow problem mixed-integer programming

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Using multiple reference vectors and objective scaling in the Feasibility Pump

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2023年第1期11卷

作者： Mexi, Gioni Berthold, Timo Salvagnin, Domenico Zuse Inst Berlin Takustr 7 D-14195 Berlin Germany Fair Isaac Deutschland GmbH Welle 10 D-60322 Frankfurt Germany Univ Padua Via Gradenigo 6-B I-35131 Padua Italy

The Feasibility Pump (FP) is one of the best-known primal heuristics for mixed-integer programming (MIP): more than 15 papers suggested various modifications of all of its steps. So far, no variant considered information across multiple iterations, but all instead maintained the principle to optimize towards a single reference integer point. In this paper, we evaluate the usage of multiple reference vectors in all stages of the FP algorithm. In particular, we use LP-feasible vectors obtained during the main loop to tighten the variable domains before entering the computationally expensive enumeration stage, a procedure we refer to as mRENS. Moreover, we consider multiple integer reference vectors to explore further optimizing directions and introduce alternative objective scaling terms to balance the contributions of the distance functions and the original MIP objective. Our computational experiments demonstrate that the new method can improve performance on general MIP test sets. In detail, our modifications provide a 29.3% solution quality improvement and 4.0% running time improvement in an embedded setting, needing 16.0% fewer iterations over a large test set of MIP instances. In addition, the method's success rate increases considerably within the first few iterations. In a standalone setting, we also observe a moderate performance improvement, which makes our version of FP suitable for the two main use-cases of the algorithm.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY-NC-ND license (http:// creativecommons .org /licenses /by -nc -nd /4 .0/).

关键词： mixed-integer programming Primal heuristics

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A simulation-optimization model for liquefied natural gas transportation considering product variety

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2021年第4期16卷 279-289页

作者： Shafipour-Omrani, Bahareh Komijan, Alireza Rashidi Ghasemi, Peiman Ghasemzadeh, Ebrahim Babaeinesami, Abdollah Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Firoozkooh Branch Firoozkooh Iran

In this paper, the ports of origin and destination and transportation of the LNG supply chain are focused, and a model is presented to minimize the cost of sending natural gas from the primary port to the destination port, taking into account product diversity. Therefore the main research questions are as follows: What is the allocation of a loading port to the group of unloading ports? What are the optimal transportation costs for liquefied natural gas ships? This research holds innovation in two aspects, one is considering the product variety and the way of presenting the constraint (which differs from other studies in the field of liquefied natural gas), and the other is applying the actual events in the simulation-optimization model. To solve the proposed model, the branch and bound method is used and then different scenarios are designed and implemented for it with the help of simulation. The results show that the proposed solutions are appropriate and reduce transportation costs by about 11%. To clarify the computational capability and operating conditions of the model, the gas platforms of Iran and London were studied. This study shows that if the ships capacity increases and the product variation decreases, the number of times the ship leaves port are 30, the transportation cost is reduced by $ 221,661.50 at the same time, the power of responding to demands will increase.

关键词： Supply chain maritime transport liquefied natural gas mixed-integer programming

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