Article
Chemistry, Analytical
Soi Jeon, Dae-Hyun Choi
Summary: This paper presents an optimization framework to dispatch a mobile charging station to reduce waiting time of electric vehicles at a fixed charging station. The framework considers active/reactive power flow and consumption, reactive power capability of the charging station, and voltage quality in power distribution systems, resulting in optimal operation scheduling and reduction of waiting EVs within allowable voltage range.
Article
Computer Science, Artificial Intelligence
Ying Zhang, Yunpeng Hua, Ao Kang, Jiyuan He, Meng Jia, Yao-Yi Chiang
Summary: The deployment of charging infrastructure for electric vehicles is crucial in extending their range. However, using the Mixed Integer Linear Programming (MILP) method to optimize the deployment becomes impractical as the computational time and memory requirements increase exponentially. This paper proves that the Planning of Electric Vehicle Charging Stations (PEVCS) is an NP-complete combinatorial optimization problem and demonstrates the effectiveness of two efficient methods utilizing submodularity. The proposed methods provide a provable guarantee for performance and are effective in realistic large-scale situations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Energy & Fuels
Jin Zhang, Zhenpo Wang, Eric J. Miller, Dingsong Cui, Peng Liu, Zhaosheng Zhang, Zhenyu Sun
Summary: This paper evaluates the multi-period and multi-scenario spatiotemporal distribution of charging demands based on real-world operation data in Beijing. It proposes a three-period charging stations planning model to meet dynamic multi-period charging demands. The model takes into account high-resolution spatiotemporal charging demands distribution and capacity design to determine the locations and quantities of charging piles reasonably. Suggestions are given on the construction locations and configurations of charging stations.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Computer Science, Information Systems
Giulio Ferro, Riccardo Minciardi, Luca Parodi, Michela Robba
Summary: This article discusses an optimization problem in planning service stations for electric buses, considering the selection and sizing of sites, assignment of lines, and fleet sizing. The problem takes into account the non-linear charging process of the batteries and aims to achieve a minimum cost design while ensuring a minimum quality level. Commercial software tools can efficiently solve moderate-sized problems, as demonstrated in the presented application.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Civil
Canqi Yao, Shibo Chen, Zaiyue Yang
Summary: This paper proposes an efficient two-stage algorithm that decomposes the original MIP problem into two LP problems, achieving near-optimal solution in polynomial time by exploiting the exactness of LP relaxation and eliminating the coupled term. Additionally, a variant algorithm based on the two-stage one is introduced to further improve the quality of the solution. Extensive simulations validate the effectiveness of the proposed algorithm compared to state-of-the-art methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Energy & Fuels
Meysam Saeedirad, Esmaeel Rokrok, Mahmood Joorabian
Summary: Electric vehicle charging is a common technique for techno-economic energy management in the distribution system. This method, if implemented properly, can bring benefits such as peak load shaving and cost reduction. Unlike traditional charging methods, smart charging charges vehicles based on their specific demand, resulting in shorter charging time and balanced energy consumption. To implement this method, vehicle owners need to provide information about their next trips, and a charging management system will plan the charging process considering various parameters. The aim is to minimize charging costs while taking technical and economic constraints into account.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Environmental Studies
Tugce Uslu, Onur Kaya
Summary: This research presents a mixed integer-linear mathematical model to optimize the location and capacity decisions of electric bus charging stations for ensuring the connectivity of road networks in a region. The study highlights the importance of driving ranges in the efficient use of electric buses, and the significant impact of charging durations, number of trips, and service rates on the capacities of charging stations.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Energy & Fuels
Shiqu Q. Xiao, Xia Lei, Tao Huang, Xiang Wang
Summary: This paper presents a stochastic bi-level model for the optimal allocation of fast charging stations (FCSs) and distribution network expansion planning (DNEP). The allocation of FCSs on the transportation network is optimized using a sequential capacitated flow-capturing location-allocation model (SCFCLM), and the uncertainties of loads are addressed using the chance constrained method in the economic model for DNEP. Numerical experiments are conducted to validate the proposed planning method and analyze the influences of flow capturing sequence and relaxed confidence level.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Luyun Wang, Bo Zhou
Summary: This study investigates the planning problem of fast-charging stations for electric vehicles with uncertain charging demands. It establishes a chance-constrained programming model based on the multicommodity flow model to determine the locations of fast-charging stations and the number of charging piles. The programming model is reformulated using a scenario-based approach and a big-M coefficients generation algorithm, and the Dantzig-Wolfe decomposition method is utilized to find the optimal solution. A numerical experiment in a 25-node network is conducted to assess the efficiency of the proposed model and solution approach.
Article
Transportation Science & Technology
Carlo Filippi, Gianfranco Guastaroba, Lorenzo Peirano, M. Grazia Speranza
Summary: This paper presents an optimization model for locating charging stations that considers the specific features of the problem, such as charging technologies, service time, and time-dependent demand. It also compares this model with a simpler one that does not include the temporal dimension, and shows that ignoring time can result in high levels of unsatisfied demand.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Automation & Control Systems
Tianyi Zhao, Na Li, Nan Kong, Xiaoqing Xie
Summary: This paper addresses the optimal electric vehicle charging station location problem with two types of customers. A bi-level location optimization model is formulated, and an adaptive large neighbourhood search algorithm and a construct-improve heuristic are designed to solve the problem. Numerical experiments demonstrate the efficiency of the solution method, and a need-inspired case study provides practical insights.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Pasqual Marti, Jaume Jordan, Javier Palanca, Vicente Julian
Summary: Current traffic congestion and carbon emissions are major threats to the sustainability of modern cities. The challenges faced by cities today involve optimizing traffic flow and transitioning to electric vehicles. Agent-based simulation (ABS), with its ability to model the complexity of urban mobility, has shown great potential in addressing these challenges. This paper introduces two configurable generators that automate the creation of ABS experiments.
Article
Engineering, Electrical & Electronic
Zhonghao Zhao, Carman K. M. Lee
Summary: This article proposes a new dynamic pricing framework for EV charging stations that offers multiple charging options to customers and aims to maximize the quality of service. It employs a customized deep reinforcement learning approach to solve the problem and demonstrates its effectiveness through simulation results.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Operations Research & Management Science
Aurelien Froger, Ola Jabali, Jorge E. Mendoza, Gilbert Laporte
Summary: The paper explores the Electric vehicle routing problems (E-VRPs) with capacity limitations at the charging stations, introducing the E-VRP-NL-C problem. It presents a continuous-time model formulation and an algorithmic framework to effectively handle the issue.
TRANSPORTATION SCIENCE
(2022)
Article
Transportation Science & Technology
Ehsan Mahyari, Nickolas Freeman, Mesut Yavuz
Summary: The past decade has seen a growing interest in transportation electrification from academia, government, and industry. Fleet operators are currently facing a challenge in charge scheduling due to fleet size, heterogeneity, and uncertainty, leading to the emergence of the Charging-as-a-Service (CaaS) industry. This paper addresses the CaaS providers' electric vehicle fleet (EVF) charge scheduling problem and proposes an optimization approach that outperforms industry benchmarks in terms of charging costs and energy consumption.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Operations Research & Management Science
Adham I. Tammam, Miguel F. Anjos, Michel Gendreau
ANNALS OF OPERATIONS RESEARCH
(2020)
Article
Management
Thibault Barbier, Miguel F. Anjos, Fabien Cirinei, Gilles Savard
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Management
Jesus A. Rodriguez, Miguel F. Anjos, Pascal Cote, Guy Desaulniers
Summary: The maintenance scheduling problem for hydroelectric generators involves uncertainty in water flows and nonlinearity in hydroelectric production, solved using a two-stage stochastic program and parallelized Benders decomposition algorithm. By approximating hydroelectric production with linear inequalities and indicator variables, tailoring and testing various acceleration techniques successfully sped up the algorithm fourfold. Industrial results confirm high scalability of the parallelized Benders implementation in various scenarios.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
Miguel F. Anjos, Manuel V. C. Vieira
Summary: This paper focuses on the facility layout problem, proposing a new MILP formulation and introducing a two-stage optimization algorithm to solve large instances. The algorithm is the first to report solutions for instances with up to 100 departments in the literature.
OPTIMIZATION LETTERS
(2021)
Article
Operations Research & Management Science
M. F. Anjos, Y. Emine, A. Lodi, Z. Sun
Summary: This study explores several polynomial optimization formulations for the maximum independent set problem and the use of the Lasserre hierarchy. Computational experiments show that the choice of formulation can significantly impact the resulting bounds, with theoretical justifications provided for the observed behavior.
OPERATIONS RESEARCH LETTERS
(2021)
Article
Economics
Elizaveta Kuznetsova, Miguel F. Anjos
Summary: This paper analyzes the impact of billing policy on the profitability of investment in prosumer schemes in Ontario, Canada. It concludes that recent advancements in commercial storage technologies have enabled prosumers to achieve full self-sufficiency. Off-grid becomes a more attractive option in sensitive locations due to a high share of fixed costs in the total electricity bill.
Article
Engineering, Electrical & Electronic
Julie Sliwak, Erling D. Andersen, Miguel F. Anjos, Lucas Letocart, Emiliano Traversi
Summary: In this paper, a new strategy for computing efficient clique decompositions for the SDP problem is proposed, using a clique merging heuristic based on two different estimates of the computational burden. Comparisons with other algorithms on MATPOWER instances show a significant decrease in solver time.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Maissa Daadaa, Sara Seguin, Kenjy Demeester, Miguel F. Anjos
Summary: This paper introduces a linear mixed-integer formulation to address the short-term hydropower unit commitment problem by maximizing total energy production through optimizing water discharge and power points of turbine combinations. The algorithm penalizes turbine start-ups and imposes constraints on the maximum number of turbine changes to find a feasible solution. Computational results are presented for instances in the Saguenay-Lac-St-Jean region of Quebec in Canada.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Paula Fermin Cueto, Ivona Gjeroska, Albert Sola Vilalta, Miguel F. Anjos
Summary: The solution approach divides the multi-trip vehicle routing problem with time windows into strategic decisions and operational planning, addressing depot allocation, fleet size determination, and daily vehicle route optimization. By combining a branch-and-cut algorithm with a heuristic method, the method is efficient in both strategic decisions and operational planning.
Article
Thermodynamics
Miguel F. Anjos, Luce Brotcorne, Juan A. Gomez-Herrera
Summary: This paper introduces a novel price setting optimization problem for an electricity supplier in the smart grid, aiming to maximize profit in a demand response context. Computational experiments show that implementing TLOU can increase the supplier's economic benefit by up to 10% and save consumers up to 6% in costs.
Article
Operations Research & Management Science
Mathieu Besancon, Miguel F. Anjos, Luce Brotcorne
Summary: The complexity of near-optimal robust multilevel problems is analyzed, showing that near-optimal robust versions of multilevel problems remain in the same complexity class as the original problems under general conditions.
OPTIMIZATION LETTERS
(2021)
Article
Energy & Fuels
Barbara Rodrigues, Miguel F. Anjos, Valerie Provost
Summary: The article introduces an innovative business model that aggregates residential storage systems and compensates participants for using their energy storage. A realistic case study in Texas confirms the profitability of the model, highlighting the importance of setting appropriate compensation for successful implementation.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Construction & Building Technology
Ilaria Salerno, Miguel F. Anjos, Kenneth McKinnon, Juan A. Gomez-Herrera
Summary: This paper introduces a novel adaptable energy management system (EMS) for smart buildings, which minimizes energy consumption of individual living units and enhances grid demand flexibility. Computational experiments demonstrated significant reductions in electricity costs and energy consumption with the proposed approach.
JOURNAL OF BUILDING ENGINEERING
(2021)
Proceedings Paper
Construction & Building Technology
I Salerno, M. F. Anjos, K. McKinnon, E. S. Mazzucchelli
Summary: The proposed model, Optimal Refurbishment Design (ORD), is an innovative tool to assist architects in refurbishing existing buildings or designing new ones, addressing the demand for refurbishment, navigating low-energy construction solutions, and analyzing buildings as complex systems. Utilizing mathematical optimization, the ORD simultaneously determines optimal thermal mass, insulation, and user needs without preset limitations on materials or strategies.
CARBON-NEUTRAL CITIES - ENERGY EFFICIENCY AND RENEWABLES IN THE DIGITAL ERA (CISBAT 2021)
(2021)
Article
Computer Science, Software Engineering
Mathieu Tanneau, Miguel F. Anjos, Andrea Lodi
Summary: This paper introduces the algorithmic design and implementation of Tulip, an open-source interior-point solver for linear optimization. Tulip is competitive with open-source interior-point solvers, and is flexible in handling unbounded and infeasible problems. It also demonstrates a tenfold speedup in structured master problems over state-of-the-art commercial interior point method solvers. Additionally, Tulip's ability to use different levels of arithmetic precision is illustrated through problems solved in extended precision.
MATHEMATICAL PROGRAMMING COMPUTATION
(2021)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
