Article
Management
Denis Lebedev, Paul Goulart, Kostas Margellos
Summary: This study focuses on revenue management in attended home delivery using dynamic programming. The unique fixed point of the underlying Bellman operator is proven, with a closed-form expression and continuous extension provided. Monotonicity of prices with respect to the number of orders placed is shown, offering a pathway for scalable implementations.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Management
Simon Laumer, Christiane Barz
Summary: This paper proposes a novel choice of non-separable basis functions for an approximate linear programming approach to the network revenue management problem. The consideration of non-separability is particularly important in situations with large interdependencies between resources. The authors suggest grouping resources into non-separable subnetworks and extending existing reductions of approximate linear programs with a more general choice of basis functions. The work contributes to a better understanding of the impact of assuming separability in network revenue management and provides a method for estimating added average revenue resulting from incorporating interactions between resources.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Jin Woo Bae, Kwang-Ki K. Kim
Summary: An energy-efficient supervisory control method for parallel hybrid electric vehicles is proposed to improve fuel economy and reduce emissions, utilizing dynamic programming and Gaussian process regression to optimize power management. The method achieved significant fuel efficiency improvements in real-world driving conditions, showcasing the potential for practical application in the automotive industry.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Ziyu Lin, Jingliang Duan, Shengbo Eben Li, Haitong Ma, Jie Li, Jianyu Chen, Bo Cheng, Jun Ma
Summary: The research addresses the challenge of solving the finite-horizon HJB equation, proposes a new algorithm, and validates its effectiveness through simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Syeda Sakira Hassan, Simo Sarkka
Summary: This article proposes a novel computational method for solving nonlinear optimal control problems. The method uses Fourier-Hermite series to approximate the action-value function in dynamic programming and uses sigma-point methods to numerically compute the coefficients of the series. The method is proven to have quadratic convergence and its performance is tested experimentally.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Management
Feng Zhu, Shaoxuan Liu, Rowan Wang, Zizhuo Wangd
Summary: This study addresses the revenue management problem in selling high-speed train tickets in China, where a unique feature is the assign-to-seat restriction. The authors propose a modified network revenue management model and develop efficient approximation algorithms to solve the problem. They also introduce a bid-price control policy based on a maximal sequence principle, which improves the accuracy of the value function approximation. Numerical experiments demonstrate the effectiveness of the proposed policies in resource-allocation efficiency.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2023)
Article
Automation & Control Systems
Antonio Sala, Leopoldo Armesto
Summary: This study introduces a new criterion for adaptive meshing in polyhedral partitions to interpolate value functions, employing an initial condition probability density function, uncertainty propagation, and temporal-difference error to determine the addition of new points. A collection of lemmas justifies the algorithmic proposal, with comparative analysis highlighting the advantages of this proposal over other options in literature. The developed methods are applied in simulation examples and an experimental robotic setup.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Jonathan Lock, Tomas McKelvey
Summary: This paper presents a numerical method for generating state-feedback control policies for general undiscounted, constant-setpoint, infinite-horizon, nonlinear optimal control problems with continuous state variables. The method extends existing termination criteria by requiring both the control policy and resulting system state to converge, allowing for use with undiscounted cost functions that are bounded and continuous. MATLAB implementation and example codes are freely available for validation.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Denis Lebedev, Kostas Margellos, Paul Goulart
Summary: This study addresses the revenue management problem of finding profit-maximizing prices for delivery time slots in attended home delivery. By analyzing three approximate DP algorithms, it is found that the gradient-bounded DP algorithm outperforms others in terms of computation time and profit-generation capabilities. Real-world data is used for analysis and stress-testing the robustness of pricing policies to errors in model parameter estimates.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Xuyang Luo, Chunyue Song
Summary: Temporary borrowing is used by mutual fund managers as a liquidity risk management tool to meet investor redemption demands. In this study, a new Markov decision process model is developed to describe the temporary borrowing process, considering multiple lending channels and uncertainties. The proposed approximate dynamic programming algorithm provides cost-minimizing solutions and the value function updating formula DMAX improves estimation accuracy. Numerical experiments demonstrate the effectiveness and robustness of the algorithm in both deterministic and stochastic cases.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Xiao Han, Lei Liu, Huijin Fan, Zhongtao Cheng
Summary: This paper investigates the robust approximate optimal control problem for air-breathing hypersonic vehicle (AHV) and proposed a robust approximate optimal controller based on adaptive dynamic programming (ADP). By input-output linearization and introducing auxiliary variables, the high-order nonlinear AHV dynamics are transformed to a second-order feedback decoupling model. The proposed controller considers both robustness and input cost, optimizes control actions, and avoids chattering. The use of a single critic network design ensures system stability and simplifies implementation. Simulation results demonstrate the effectiveness of the proposed scheme.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Cong Li, Yongchao Wang, Fangzhou Liu, Qingchen Liu, Martin Buss
Summary: This article presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems using an incremental adaptive dynamic programming (IADP) approach. It leverages time delay estimation (TDE) technique and measured input-state data to achieve incremental stabilization under uncertainties, disturbances, and saturation. Numerical simulations validate its effectiveness and superiority in reducing energy expenditure and enhancing robustness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Management
Arne Strauss, Nalan Gulpinar, Yijun Zheng
Summary: The study proposes the introduction of flexible delivery time slots in e-commerce to exploit customer flexibility, offer reduced delivery charges and highlight environmental benefits. A tractable linear programming formulation is developed to link demand management and routing cost implications, enabling the development of a dynamic pricing policy.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Automation & Control Systems
Shangwei Zhao, Jingcheng Wang, Haotian Xu, Hongyuan Wang
Summary: In this paper, an approximate dynamic programming approach is proposed for handling the robust optimal tracking control problem in switched systems with uncertainties. A neural network based identifier is used to estimate the unknown system dynamics, and actor-critic neural networks are constructed to approximate the optimal control input and performance index. The convergence of the proposed approach is proved, and numerical simulations are conducted to validate its effectiveness.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Xiong Yang, Mengmeng Xu, Qinglai Wei
Summary: We study the dynamic event-driven Hop constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we transform the H-8 constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton-Jacobi-Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mechanism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN's weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN's weight estimation error via Lyapunov's method. Finally, we separately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Management
Laura Turrini, Joern Meissner
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Management
Xinan Yang, Arne K. Strauss
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2017)
Article
Management
Joern Meissner, Olga V. Senicheva
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Review
Management
Arne K. Strauss, Robert Klein, Claudius Steinhardt
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Review
Management
Robert Klein, Sebastian Koch, Claudius Steinhardt, Arne K. Strauss
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Management
Laura Turrini, Maria Besiou, Dominik Papies, Joern Meissner
JOURNAL OF OPERATIONS MANAGEMENT
(2020)
Article
Operations Research & Management Science
Stefano Starita, Arne K. Strauss, Xin Fei, Radosav Jovanovic, Nikola Ivanov, Goran Pavlovic, Frank Fichert
TRANSPORTATION SCIENCE
(2020)
Review
Management
Cerag Pince, Laura Turrini, Joern Meissner
Summary: Forecasting spare parts demand has been a challenging issue for many companies, and has received considerable attention over the past fifty years. This paper provides a critical review and quantitative analysis of current literature on spare parts demand forecasting methods, offering detailed insights into when and why particular forecasting methods should be preferred.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Economics
Jan-Rasmus Kuennen, Arne K. Strauss
Summary: This paper develops a modeling framework to assess the impact of the future role of the network manager (NM) on key performance indicators in European air traffic management. The study focuses on the pre-tactical stage of planning air traffic for a future departure day and introduces dynamically priced trajectory products to allow airspace users to choose their preferred route while minimizing overall costs.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Business
Jonas Schwamberger, Moritz Fleischmann, Arne Strauss
Summary: The proactive contacting of customers is an important concept in demand management for the e-grocery industry during the COVID-19 pandemic. By developing decision policies to allocate delivery capacity to high-priority customers, it effectively addresses the issue of overwhelming demand exceeding delivery capacity. Subdividing the delivery area, selecting promising subareas, and determining which customers to contact improves problem-solving efficiency.
Article
Operations Research & Management Science
Jan-Rasmus Kuennen, Arne K. Strauss, Nikola Ivanov, Radosav Jovanovi, Frank Fichert, Stefano Starita
Summary: In European air traffic management, deciding how much capacity to provide for each airspace is crucial. We propose a capacity sharing scheme where a portion of overall capacities can be flexibly deployed in any of the airspaces of the same alliance. Through simulation and optimization, we find that our stochastic approach significantly reduces network costs while using fewer computational resources.
TRANSPORTATION SCIENCE
(2023)
Article
Management
Merve Keskin, Juergen Branke, Vladimir Deineko, Arne K. Strauss
Summary: This paper presents a dynamic multi-period vehicle routing problem with touting as a demand management technique. It proposes several strategies to decide which customers to tout and when, taking into account the characteristics of the customers and the current plan. These strategies are embedded in a rolling-time horizon vehicle routing algorithm to address the multi-period nature of the problem. Empirical comparison in a simulation based on a real-world waste collection problem shows that touting can significantly reduce travel distance in a dynamic vehicle routing problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Business, Finance
Nikolaos Kourentzes, Dong Lie, Arne K. Strauss
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2019)
Article
Transportation
Nikola Ivanov, Radosav Jovanovic, Frank Fichert, Arne Strauss, Stefano Starita, Obrad Babic, Goran Pavlovic
JOURNAL OF AIR TRANSPORT MANAGEMENT
(2019)
Article
Business, Finance
Christine S. M. Currie, Trivikram Dokka, John Harvey, Arne K. Strauss
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2018)
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)
