Article
Energy & Fuels
Sudlop Ratanakuakangwan, Hiroshi Morita
Summary: The study integrates both stochastic robust optimization and robust optimization into a proposed model to deal with multiple uncertainties effectively. Social acceptance is considered alongside other factors such as system stability and economy. Through case studies in Thailand and Vietnam, the model demonstrates the potential to effectively balance system stability and economic factors in energy planning.
Article
Computer Science, Interdisciplinary Applications
A. Alnaqbi, J. Trochu, F. Dweiri, A. Chaabane
Summary: In this study, we propose a stochastic model for tactical planning of the Crude Oil Supply Chain (COSC) considering cost and demand uncertainties. The model takes into account a multi-echelon supply chain with multi-products and a multi-period planning horizon, as well as inventory and backorder penalties. We use a Sample Average Approximation (SAA) procedure with Multiple Replications Procedure (MRP) to solve the stochastic model and demonstrate its application in supply chain planning. We provide numerical results that illustrate the impact of cost uncertainty on planning decisions and synergy gains, and evaluate the value of modeling uncertainty compared to deterministic planning.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Energy & Fuels
Iegor Riepin, Matthew Schmidt, Luis Baringo, Felix Muesgens
Summary: This paper explores the value of Projects of Common Interest in the context of uncertainties in the European natural gas market, using an adaptive robust optimization framework. It identifies the importance of certain projects and highlights the need for financial support for their realization.
Article
Computer Science, Interdisciplinary Applications
Morteza Mazloumian, M. Fazle Baki, Majid Ahmadi
Summary: This paper addresses the problem of integrated operating room planning and advanced scheduling in a surgery department. By considering both the allocation of surgical specialties to OR blocks and the assignment of patients from waiting lists, the stability of the scheduling process is enhanced. The objective is to maximize patient service levels and hospital efficiency. A multi-objective integer linear programming model is developed and solved using an Lp-metric methodology. The effectiveness of the model is demonstrated through a numerical experiment.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Health Policy & Services
Nazanin Aslani, Onur Kuzgunkaya, Navneet Vidyarthi, Daria Terekhov
Summary: Tactical capacity planning is crucial in healthcare settings, with a focus on utilizing cardinality-constrained robust optimization to create feasible resource allocations under uncertainty. This approach helps clinics save money while meeting demand, and allows for identification of critical time periods for decision-making. Translation of data into an uncertainty set can impact the conservatism of the solution.
HEALTH CARE MANAGEMENT SCIENCE
(2021)
Article
Operations Research & Management Science
Christian Rahlmann, Felix Wagener, Ulrich W. Thonemann
Summary: This study focuses on a tactical freight railway crew scheduling problem, introducing an approach to incorporate uncertain trip demand for robust crew schedules. By developing a column generation solution method, duties in multiple scenarios are covered to create recoverable robust crew schedules, outperforming nominal solutions in terms of robustness.
TRANSPORTATION SCIENCE
(2021)
Article
Management
Antoine Perraudat, Stephane Dauzere-Peres, Philippe Vialletelle
Summary: This paper investigates the impact of qualification management on capacity planning in flexible manufacturing systems, proposing methods to address the issue. Through a study on industrial data, it is shown that considering demand uncertainty in qualification management is critical.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2022)
Article
Economics
Clara Chini Nielsen, David Pisinger
Summary: This study focuses on the tactical planning of a dynamic technician routing and scheduling problem. By partitioning the area into disjoint slices and assigning them to work days, the driving distance can be reduced and service time optimized. The results show that tactical planning can lead to a 10% reduction in driving distance.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Management
Xuan Vinh Doan
Summary: This paper proposes a marginal-based distributionally robust optimization framework for integer stochastic optimization, which is applied to address endogenous uncertainty in retrofitting planning applications. The proposed algorithm demonstrates efficient problem-solving capability in retrofitting planning applications.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Automation & Control Systems
Sunney Fotedar, Ann-Brith Stromberg, Edvin Ablad, Torgny Almgren
Summary: Optimal solutions using nominal or expected parameter values in the presence of uncertainties in the parameters of a mathematical model can be misleading. Robust solutions to optimization problems are desired, but robust multi-objective optimization has received little attention. This study presents a new robust efficiency concept for bi-objective optimization problems and tests it on a real-world resource planning problem.
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
(2022)
Article
Operations Research & Management Science
Dominic J. Breuer, Shashank Kapadia, Nadia Lahrichi, James C. Benneyan
Summary: This paper develops two robust optimization models to plan the most effective bed and nurse resource allocation in hospital clinical units, considering uncertainties and resource sharing. The use of uncertainty sets based on least-squares ellipsoidal fitting helps reduce non-admissions and costs.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Information Systems
Shuxia Li, Wanke Han, Liping Liu
Summary: Traditional hub location problems are usually based on deterministic circumstances, but this study takes into account the uncertainty of customer demands and the perishability of fresh agricultural products. By establishing robust optimization models, the study shows that the deterioration rate and uncertainty have significant impacts on the total transportation profit.
Article
Computer Science, Interdisciplinary Applications
Ana Batista, David Pozo, Jorge Vera
Summary: This research addresses the off-line admission planning problem with uncertain length of stay in the inpatient service, considering the uncertainty in patient's length of stay and bed availability. By using a distributionally robust optimization framework, the coordinated decisions of scheduling and allocation aim to maximize the weighted sum of patient's admission benefit while reducing the cost of overstay. The proposed approach outperforms benchmark models in reliability and computational efficiency, providing insights to practitioners and hospital decision-makers on anticipating admission decisions at the tactical-operational level while considering the randomness of the length of stay.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Alexandre Cesar Balbino Barbosa Filho, Sergio Mauro da Silva Neiro
Summary: The paper presents a robust optimization framework that combines various concepts and principles to provide reliable solutions. The framework is applicable to both linear and nonlinear mathematical models, as well as discrete and continuous optimization problems. Numerical simulations validate the high tractability and performance of the framework.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
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
Engineering, Industrial
Claver Diallo, Uday Venkatadri, Abdelhakim Khatab, Zhuojun Liu, El-Houssaine Aghezzaf
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2019)
Article
Engineering, Industrial
Wouter Lefever, Faycal A. Touzout, Khaled Hadj-Hamou, El-Houssaine Aghezzaf
Summary: This paper discusses the time-constrained inventory routing problem (TCIRP) on a network with uncertain arc travel times, proposing a robust optimization approach with a controlled level of conservatism and developing a Benders' decomposition-based heuristic to cope with the resulting robust counterpart's complexity. The proposed method is compared with two standard approaches and shown to find robust solutions that are not too conservative in reasonable time.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Automation & Control Systems
Joao Costa Mateus, Dieter Claeys, Veronique Limere, Johannes Cottyn, El-Houssaine Aghezzaf
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2020)
Article
Engineering, Industrial
Niels De Smet, Stefan Minner, El-Houssaine Aghezzaf, Bram Desmet
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2020)
Article
Management
Mathieu Vandenberghe, Stijn De Vuyst, El-Houssaine Aghezzaf, Herwig Bruneel
Summary: Anticipating the impact of urgent emergency arrivals on operating room schedules is challenging. This study introduces a model for surgery scheduling considering stochastic surgery durations and emergency patient arrivals. Detailed analysis of emergency break-ins can lead to lower total cost, and an efficient heuristic is proposed to estimate the solution value with less computational effort.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Engineering, Environmental
Sarah ElSaid, El Houssaine Aghezzaf
WASTE MANAGEMENT & RESEARCH
(2020)
Article
Engineering, Industrial
Ryan O'Neil, Claver Diallo, Abdelhakim Khatab, El-Houssain Aghezzaf
Summary: This paper proposes a solution method for the multimission selective maintenance problem by combining column-generation and genetic algorithms. By integrating the genetic algorithm within the classical column-generation framework, high-quality solutions can be quickly obtained. The proposed method performs well in solving large-scale systems.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Muhammad Saeed, Thibaut Demasure, Steven Hoedt, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: An execution time estimation model is proposed to accurately estimate the execution time in a robotic assembly workcell. The model utilizes trajectory generation and task specifications to estimate the task time with reasonable accuracy. Possible directions to further improve the estimation accuracy are discussed.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Lauren Van De Ginste, Alexander De Cock, Axl Van Alboom, Stijn Huysentruyt, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: This paper presents a multidimensional formal skill model that can be used to describe the needs and capacities in an assembly system, connecting resources with processes and products. It discusses the evaluation of a reconfigurable assembly system and highlights the benefits of a skill-based modeling approach.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Matthias Schamp, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: This paper proposes a workflow that utilizes a 3D digital model to provide additional support to automation engineers, allowing for earlier verification of control logic and reducing real commissioning time. The approach records all occurring states and transitions, visualizes the state graph, and highlights unexpected behavior. Validation on a test case confirms the effectiveness of the proposed approach, with future research aimed at validation on real industrial cases.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Engineering, Industrial
Jaakko Peltokorpi, Steven Hoedt, Thomas Colman, Kim Rutten, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: Cognitive assistance systems help individuals with learning disabilities to improve their skills and increase their employment opportunities. This study focused on the impact of different forms of instruction and types of disability in a manual assembly task. The results showed that projection instructions enhanced the initial assembly cycle and challenging operators with filtered content improved their independence and task understanding. However, adaptive instructions posed barriers for operators who relied heavily on mentor support. The form of instruction should be carefully considered based on each operator's adaptation and needs. These findings have implications for the human-centric and socially sustainable production agenda of Industry 5.0 and highlight future research priorities.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Alp Darendeliler, Dieter Claeys, El-Houssaine Aghezzaf
Summary: This paper tackles the problem of integrated lot-sizing and maintenance decision making in the context of multiple products and stochastic demand. The authors propose a Markov decision process formulation and employ the classic Q-learning algorithm along with a decomposition-based approximate Q-value heuristic to find near-optimal solutions in an efficient manner. To speed up convergence, they introduce a hybrid Q-learning method that initializes Q-values using the output of the approximate Q-value heuristic. The numerical experiments reveal the superiority of the hybrid method and the scalability limitations of tabular methods, leading to the development of three state aggregation schemes that significantly reduce computational time while maintaining good performance.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Engineering, Multidisciplinary
Alp Darendeliler, Dieter Claeys, El-Houssaine Aghezzaf
Summary: This paper investigates the problem of integrated lot-sizing and maintenance decision making with multiple products and stochastic demand. It proposes a combination of Q-learning algorithm and approximate Q-value heuristic to achieve near-optimal solutions. The experiments show that the hybrid Q-learning method outperforms the classic Q-learning algorithm and approximate Q-value heuristic in terms of accuracy and convergence speed. Moreover, to tackle large-scale problems, three state aggregation schemes are developed and applied, with the third scheme achieving similar performance as the hybrid Q-learning method while significantly reducing computational time.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Proceedings Paper
Computer Science, Information Systems
Lauren Van De Ginste, Alexander De Cock, Axl Van Alboom, Yogang Singh, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: In the Industry 4.0 era, the demand for highly customized products in small batch sizes has increased, leading to the need for flexible assembly workstations. A skill-centered model is introduced to describe resource activities and flexibility impacts, allowing for versatile formal models to convert flexibility needs into design and operational decisions. This model offers a framework that combines abstract and executable skills, allowing for easy adaptation and application in various settings.
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, PT V
(2021)
Proceedings Paper
Computer Science, Information Systems
Cheshmeh Chamani, El-Houssaine Aghezzaf, Abdelhakim Khatab, Birger Raa, Yogang Singh, Johannes Cottyn
Summary: The research considers a two-stage supply chain with two collaborating production plants in industrial symbiosis. Two different formulations are proposed: one using the SAA method to solve the natural model and the other based on a plant location reformulation. Experimental analysis indicates that the plant location reformulation significantly improves optimality gaps and computational times compared to the natural model.
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT II
(2021)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)