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Multi-objective mathematical programming for optimally sizing and managing battery energy storage for solar photovoltaic system integration of a multi-apartment building

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ENGINEERING OPTIMIZATION 2022年第1期54卷 81-100页

作者： Amorosi, Lavinia Cedola, Luca Dell'Olmo, Paolo Lucchetta, Francesca Sapienza Univ Rome Dept Stat Sci Rome Italy Sapienza Univ Rome Dept Mech & Aerosp Engn Rome Italy

This article presents a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for the solar photovoltaic system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining control over time of the energy sold and bought and the monitoring of the state of batteries while ranging from minimum to maximum levels. This is obtained through a new mathematical programming model with three different objective functions that can be tuned simultaneously to find Pareto optimal solutions to the problem. The computational results in detailed scenarios with real data verify the model's adequacy to deal with the real problem and give a measure of the saving of bought and sold energy.

关键词： Battery storage optimization energy system management mixed integer linear programming

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Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第2期59卷 421-433页

作者： Takubo, Yuji Chen, Hao Ho, Koki Georgia Inst Technol Aerosp Engn Atlanta GA 30332 USA

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.

关键词： Reinforcement Learning Spaceflight In Situ Resource Utilization Space Transportation Interplanetary Mission Space Logistics Optimization mixed integer linear programming Artificial Intelligence Propellant Earth

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Optimal Scheduling of COVID-19 Pandemic Supply Disruptions Under Uncertainty

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IEEE ACCESS 2022年 10卷 114374-114392页

作者： Cao, Wei Wang, Xifu Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

With the outbreak of COVID-19 pandemic, the problem of supply chain emergency scheduling has had a great influence on economic benefits of enterprises. The uncertainty of the COVID-19 pandemic and uncertainty of supply chain have made the problem of emergency scheduling more complicated. In this study, a multiobjective multiperiod mixed-integer programming optimization model was developed, in which two conflicting benefit factors, cost and service level were taken as the optimization target of the model. Cost and service level were normalized, and the weighted sum was taken as the objective function, which transformed the problem into a multiperiod nonlinear one. Two algorithms of Bi-level Modified Hybrid Genetic Algorithm (BMHGA) and Bi-level Hybrid Genetic Algorithm (BHGA) were designed to solve the model. Three emergency strategies have been proposed, including enabling alternative suppliers, repairing failure nodes and enabling internal suppliers' flexibility. The parameter of disruption scenario and its solution method were designed. Finally, the practicability of the proposed model and algorithm was demonstrated through application to a case study of an electronics supply chain. The results indicated that the optimal solution of two algorithms was between 2%-5%, and BMHGA could obtain a better optimal solution.

关键词： Supply chain management COVID-19 Genetic algorithms Pandemics mixed integer linear programming Job shop scheduling Production facilities A multiobjective multiperiod mixed-integer programming optimization model supply chain emergency scheduling uncertain supply disruptions uncertain demand COVID-19 pandemic bi-level modified hybrid genetic algorithm (BMHGA) bi-level hybrid genetic algorithm (BHGA)

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Flexible open-shop problem for minimizing weighted total completion time

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 1353-1366页

作者： Mashizi, Iman Khosravi Kermani, Vahid Momenaei Shahsavari-Pour, Naser IslamicAzad Univ Dept Math Kerman Branch Kerman Iran Vali e Asr Univ Dept Ind Management Rafsanjan Iran

In this article, scheduling flexible open shops with identical machines in each station is studied. A new mathematical model is offered to describe the overall performance of the system. Since the problem enjoys an NP-hard complexity structure, we used two distinct metaheuristic methods to achieve acceptable solutions for minimizing weighted total completion time as the objective function. The first method is customary memetic algorithm (MA). The second one, MPA, is a modified version of memetic algorithm in which the new permutating operation is replaced with the mutation. Furthermore, some predefined feasible solutions were imposed in the initial population of both MA and MPA. According to the results, the latter action caused a remarkable improvement in the performance of algorithms.

关键词： Flexible open shop mixed integer linear programming memetic algorithm permutating operation taguchi method

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On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第1期59卷 33-48页

作者： du Jonchay, Tristan Sarton Chen, Hao Isaji, Masafumi Shimane, Yuri Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. mixedinteger linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.

关键词： Low Thrust Propulsion Propulsion Technology mixed integer linear programming Logistics Operations Bipropellants Geosynchronous Satellite Manufacturing Cost Geosynchronous Equatorial Orbit Propulsion System Earth

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A slack-bus-independent loss sensitivity approach for optimal day-ahead generation scheduling

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ELECTRICAL ENGINEERING 2022年第2期104卷 421-434页

作者： Phuoc, Nguyen Huy Ongsakul, Weerakorn Manjiparambil, Nimal Madhu Sasidharan, Nikhil Asian Inst Technol Sch Environm Resources & Dev Dept Energy Environm & Climate Change Pathum Thani Thailand Amrita Vishwa Vidyapeetham Univ Ctr Excellence Computat Engn & Networking Coimbatore Tamil Nadu India Natl Inst Technol Calicut Dept Elect Engn Kozhikode Kerala India

In a power system, the incurred transmission loss and the associated sensitivity factors are dependent on the selection of the slack bus and the dispatch. In this paper, a fast two-stage, hydro-thermal generation scheduling process, which is also inclusive of the system power loss, is proposed. A novel approach towards estimating power loss sensitivity factors is presented which is independent of the choice/location of the slack bus in the network. In addition to this, the approach identifies the loss incurred by the operation of different market players including the generation and distribution companies for penalizing. mixed integer linear programming is used to model the said optimal day-ahead scheduling problem. The two-stage process employed is such that the first stage is applied only in the planning stage, while second is a fast responsive algorithm suitable for very short-term applications. A real network, Vietnam Power Grid, is used for testing this proposed approach, and the results obtained demonstrated that there is significant reduction in the electricity cost and total transmission loss.

关键词： Optimal day-ahead generation scheduling Independent slack bus Transmission loss allocation Loss sensitivity factor Wholesale electricity market mixed integer linear programming

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Resiliency constraint proactive scheduling against hurricane in multiple energy carrier microgrid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第20期16卷 4096-4114页

作者： Ghasemi, Ali Akbar Gitizadeh, Mohsen Kamwa, Innocent Ghareghani, Omid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Laval Elect Engn Dept Quebec City PQ Canada

Hurricane, as one of the most frequent natural events, causes damage to the infrastructure of the upstream networks of multiple energy carrier microgrids (MECMs). Thus, the supply continuity of critical loads is threatened due to sudden islanding of MECMs from the upstream networks. Therefore, providing a framework to enhance the resilience of MECMs against this threat is necessary. In response to this issue, this paper presents a proactive optimal operation scheduling approach that is constrained to the feasible islanding of MECM from the upstream networks as well as the continuous suppling of the critical loads until the return of the upstream networks. Proposed resiliency constraint proactive scheduling is formulated as a mixed integer linear programming (MILP) that considers the interdependence between all three electric, gas and heat networks. The demand responsibility of all three electric, thermal and gas loads is used to consider the satisfaction of the consumer beside economic and resilient operation. Furthermore, both normal operation and contingency-based uncertainties are taken into account and captured using a robust optimization method. Then in order to evaluate the resilience of the MECM, feasible islanding and preparedness indices are proposed. Finally, the effectiveness of proposed approach is illustrated using an MECM test.

关键词： electric loads natural events multiple energy carrier microgrid gas loads upstream networks optimization method integer programming mixed integer linear programming heat networks resilient operation MILP economic operation power generation economics feasible islanding distributed power generation thermal loads resiliency constraint proactive scheduling hurricane power generation scheduling proactive optimal operation scheduling approach electric networks MECM

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A computational study of the influence of the parameter big-M in a time-indexed MILP for assembly flowshop

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IEEE LATIN AMERICA TRANSACTIONS 2022年第5期20卷 848-854页

作者： Santana, Jose R. Fuchigami, Helio Y. Univ Fed Goias UFG Fac Ciencia & Tecnol Aparecida Goias Goiania Go Brazil Univ Fed Sao Carlos UFSCar Sao Carlos SP Brazil

In this paper, a mixed integer linear programming model was proposed to solve the assembly flowshop scheduling problem. The objective of the study was to evaluate the impact on computational time (CPU) from the variation of the value of the big-M parameter considering three different dimensions. The performance of the mathematical model was evaluated from the application of a commercial solver that has different strategies in search of the optimal solution. 240 test-problems were solved on the optimality in an acceptable computational time, with the number of jobs ranging from 4 to 8, the number of products from 2 to 4 and the number of machines from 1 to 4. The performance profiles of Dolan and More (2002) were employed for a more accurate statistical analysis of the results, which indicated the most efficient value for the big-M parameter in the proposed mathematical formulation.

关键词： Computational modeling Mathematical models Visualization IEEE transactions Adaptation models Statistical analysis Software mixed integer linear programming Scheduling Assembly Flowshop big-M influence

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Autonomous Intersection Management for Connected and Automated Vehicles: A Lane-Based Method

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15091-15106页

作者： Wu, Wei Liu, Yang Liu, Wei Zhang, Fangni Dixit, Vinayak Waller, S. Travis Changsha Univ Sci & Tectmol Key Lab Highway Engn Minist Educ Changsha 410114 Hunan Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Univ New South Wales Sch Civil & Environm Engn Res Ctr Integrated Transport Innovat Sydney NSW 2052 Australia

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes all-direction lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time.

关键词： Delays Autonomous vehicles Resource management Intelligent transportation systems Servers Numerical analysis mixed integer linear programming Autonomous intersection connected and autonomous vehicles lane-based method sliding time window

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Balancing cybersecurity in a supply chain under direct and indirect cyber risks

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第2期60卷 766-782页

作者： Sawik, Tadeusz Reykjavik Univ Dept Engn IS-101 Reykjavik Iceland AGH Univ Sci & Technol Dept Operat Res Krakow Poland

Cybersecurity is an essential requirement for the sustainability of global supply chains. In this paper, a stochastic programming formulation is presented for optimisation of cybersecurity investment and selection of security controls to mitigate and balance the impact of direct and indirect (propagated) cyber risks in a multi-tier supply chain. Using a network transformation combined with the first-order Taylor series approximation of natural logarithm to linearise the nonlinear constraints, a nonlinear stochastic combinatorial optimisation model is approximated by its linear equivalent. The problem objective is to determine an optimal cybersecurity investment under limited budget and portfolio of security controls for each supply chain node to balance the cybersecurity over the entire supply chain. The minmax objective functions are applied to minimise either the maximum breach probability or the maximum loss of supply chain nodes. Alternatively, maxmin objectives are used to maximise either the minimum non-breach probability or the minimum saving of loss. The proposed integrated modelling approach is illustrated with results of computational study and a comparison of approximated and exact solution values is presented. The decision-making insights are provided and discussed.

关键词： Cyber risk management cybersecurity investment portfolio of security controls mixed integer linear programming supply chain cybersecurity

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