Review
Computer Science, Interdisciplinary Applications
Zulfiquar N. Ansari, Sachin D. Daxini
Summary: The study provides a comprehensive review on the applications and trends of meta-heuristics for remanufacturing problems. It identifies production planning and scheduling as the most focused application area, genetic algorithm as the most popular individual meta-heuristic for optimization, and increasing attention on hybrid meta-heuristics. The majority of remanufacturing optimization models considered are single objective.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Management
Jakob Kallestad, Ramin Hasibi, Ahmad Hemmati, Kenneth Soerensen
Summary: In this paper, a selection hyperheuristic framework based on Deep Reinforcement Learning (DRLH) is proposed for solving combinatorial optimization problems. Compared to traditional heuristics, this framework has better generalization capability and performs better in selecting low-level heuristics during the search process. By integrating a Deep RL agent into the ALNS framework, the DRLH framework is shown to outperform ALNS and a Uniform Random Selection (URS) in selecting low-level heuristics.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Bart Vangerven, Dirk Briskorn, Dries R. Goossens, Frits C. R. Spieksma
Summary: Motivated by evidence that parliament seatings are relevant for decision making, we consider the problem to assign seats in a parliament to members of parliament. We prove that the resulting seating assignment problem is strongly NP-hard in several restricted settings. We present a Mixed Integer Programming formulation of the problem, we describe two families of valid inequalities and we discuss symmetry breaking constraints. Further, we design a heuristic. Finally, we compare the outcomes of the Mixed Integer Programming formulation with the outcomes of the heuristic in a computational study.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Tianyu Zhu, Xinli Shi, Xiangping Xu, Jinde Cao
Summary: The application of neural network models in solving combinatorial optimization problems has attracted much attention and shown promising results recently. The neural network can learn solutions based on given problem instances through reinforcement learning or supervised learning. In this paper, a novel end-to-end method called gated cosine-based attention model (GCAM) is proposed to solve routing problems. Extensive experiments on different scale of routing problems demonstrate that the proposed method achieves faster convergence in the training process compared to state-of-the-art deep learning models while obtaining solutions of the same quality.
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz, Ahmed A. Ewees, Nabil Neggaz, Rehab Ali Ibrahim, Mohammed A. A. Al-qaness, Songfeng Lu
Summary: This paper introduces an alternative global optimization meta-heuristics approach inspired by natural selection theory, using competition among six algorithms to generate offspring. The proposed method shows efficiency compared to other well-known meta-heuristics methods. Variants of the method update individuals using different strategies, leading to successful outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Juho Lauri, Sourav Dutta, Marco Grassia, Deepak Ajwani
Summary: This paper proposes a novel framework for scaling up exact combinatorial optimization algorithms using machine learning techniques. Unlike existing approaches, which directly learn the output of the problem, our framework focuses on learning the task of pruning elements to reduce problem size. The framework utilizes interpretable learning models based on local features, providing insights into the optimization problem and instance class for designing better heuristics.
JOURNAL OF HEURISTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Ricardo Gama, Hugo L. Fernandes
Summary: This study explores the use of Pointer Network models trained using reinforcement learning to solve the OPTW problem, proposing a modified architecture to better address problems related with dynamic time-dependent constraints, applicable to modeling the Tourist Trip Design Problem. The experiments demonstrate that the approach has good generalization performance across different tourists visiting different regions and generally outperforms the most commonly used heuristic methods.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Yuan Sun, Xiaodong Li, Andreas Ernst
Summary: This article explores problem reduction techniques for large-scale optimization problems using stochastic sampling and machine learning. Statistical measures and a machine learning approach are used to better predict decision variables belonging to the optimal solution.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Krzysztof Michalak
Summary: This paper studies a graph-based optimization problem related to epidemics control and proposes a classifier-based evolutionary algorithm, which uses a trained classifier to select graph nodes for protection and find better optimization solutions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Qi Wang, Kenneth H. Lai, Chunlei Tang
Summary: This study proposes a novel framework (BDRL) that combines BERT and deep reinforcement learning to solve combinatorial optimization problems over graphs. The transformer encoder of BERT is improved to effectively embed the combinatorial optimization graph, and BERT-like training is extended to reinforcement learning using contrastive objectives to acquire self-attention-consistent representations. Hierarchical reinforcement learning is employed to pre-train and fine-tune the model for specific combinatorial optimization problems. The results demonstrate the generalization ability, efficiency, and effectiveness of the proposed framework in multiple tasks.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Eduardo Mosqueira-Rey, Elena Hernandez-Pereira, David Alonso-Rios, Jose Bobes-Bascaran, Angel Fernandez-Leal
Summary: Researchers are exploring new forms of interaction between humans and machine learning algorithms, including active learning, interactive machine learning, and machine teaching. Besides control, humans can also contribute to the learning process through curriculum learning and explainable AI. This collaboration between AI models and humans goes beyond the learning process and extends to usability and practicality considerations.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Katharina Bieker, Bennet Gebken, Sebastian Peitz
Summary: We present a novel algorithm that provides detailed insight into the effects of sparsity in linear and nonlinear optimization. Sparsity is important in various scientific areas and can be enforced by adding the L1-norm as a penalty term. To address the non-convex multiobjective optimization problem, we propose a continuation method that offers more optimal compromises.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Physics, Multidisciplinary
Hiroshi Yamashita, Ken-ichi Okubo, Suguru Shimomura, Yusuke Ogura, Jun Tanida, Hideyuki Suzuki
Summary: The spatial photonic Ising machine (SPIM) is an optical architecture utilizing spatial light modulation for efficiently solving large-scale combinatorial optimization problems. In this study, a new computing model for the SPIM is proposed, which can handle any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, and it also incorporates the learning ability of Boltzmann machines. Experimental results demonstrate that the proposed model achieves efficient learning, classification, and sampling of MNIST handwritten digit images with low-rank interactions. Therefore, the proposed model shows higher practical applicability to various problems of combinatorial optimization and statistical learning, while maintaining the scalability of the SPIM architecture.
PHYSICAL REVIEW LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Olivier Goudet, Beatrice Duval, Jin-Kao Hao
Summary: This work introduces a general population-based weight learning framework for solving graph coloring problems, utilizing a continuous weight tensor optimization problem solving approach and taking advantage of a gradient descent method computed in parallel on graphics processing units.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mourad Lassouaoui, Dalila Boughaci, Belaid Benhamou
Summary: Feature selection is crucial in high-dimensional data classification. This article introduces a novel approach called the synergy Thompson sampling hyper-heuristic, which uses a probabilistic selection strategy to enhance classification accuracy. Experimental results show that this method outperforms existing approaches.
COMPUTATIONAL INTELLIGENCE
(2022)
Article
Operations Research & Management Science
Milad Dehghan, Seyed Reza Hejazi, Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Amir Pirayesh
Summary: This study introduces a new model to address a location-routing problem with simultaneous pickup and delivery, taking into account the risk of disruptions. By incorporating disruptions into the model, the impact of disasters can be reduced, making the network more resistant to disruptions. The objective function considers the location cost of depots, routing cost of vehicles, and cost of unmet customer demand.
RAIRO-OPERATIONS RESEARCH
(2021)
Review
Management
Seyed Sina Mohri, Mehrdad Mohammadi, Michel Gendreau, Amir Pirayesh, Ali Ghasemaghaei, Vahid Salehi
Summary: This paper provides a comprehensive review of hazardous material transportation from an Operational Research perspective, with a focus on hazmat routing, routing-scheduling, and network design problems. The paper reviews the assumptions, objectives, constraints, and solutions of the models, along with case studies. It also highlights the challenges and features of designing models for different transportation modes, and identifies research gaps and future directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Geochemistry & Geophysics
Son Duy Dao, Antoine Mallegol, Patrick Meyer, Mehrdad Mohammadi, Sophie Loyer
Summary: Hydrographic surveying is crucial in the maritime community for various purposes, and careful survey routing planning is essential for efficiency. This article introduces a hybrid iterated greedy algorithm to solve the hydrographic survey routing problem, which consists of three stages and has been validated through real case studies in France.
Article
Management
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Bastien Pasdeloup, Patrick Meyer
Summary: This paper aims to integrate machine learning techniques into meta-heuristics for solving combinatorial optimization problems. It develops a novel efficient iterated greedy algorithm based on reinforcement learning, and evaluates its performance through experiments on the permutation flowshop scheduling problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Son Duy Dao, Antoine Mallegol, Patrick Meyer, Mehrdad Mohammadi, Sophie Loyer
Summary: This paper defines the spatial area determination problem and proposes a solution method based on a Memetic Algorithm. By introducing innovative approaches, the algorithm is able to handle constraints and enhance robustness. Experimental results demonstrate that the proposed algorithm outperforms other optimization algorithms in terms of solution quality.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Arsalan Yousefloo, Reza Babazadeh, Mehrdad Mohammadi, Amir Pirayesh, Alexandre Dolgui
Summary: This paper proposes a multi-objective scenario-based robust stochastic optimization model for designing a sustainable Municipal Solid Waste (MSW) management network. The model considers the dynamic factors influencing MSW management network and integrates sustainability indicators to achieve a balance between quantitative and qualitative evaluation. Additionally, the model investigates waste treatment technologies and emphasizes the importance of fuel consumption on transportation costs and CO2 emissions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Management
Behnam Vahdani, Mehrdad Mohammadi, Simon Thevenin, Michel Gendreau, Alexandre Dolgui, Patrick Meyer
Summary: This paper proposes a new model for vaccine distribution, addressing various concerns such as prioritizing age groups, fair distribution, multi-dose injection, and dynamic demand. The proposed solution approach, which includes a Benders decomposition algorithm, is faster and provides better-quality solutions compared to existing solvers. Numerical experiments on the vaccination campaign in France demonstrate the applicability and performance of the model.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Behnam Vahdani, Mehrdad Mohammadi, Simon Thevenin, Patrick Meyer, Alexandre Dolgui
Summary: This paper addresses a multi-period production-inventory-sharing problem to overcome the challenges caused by the rapid spread of the COVID-19 virus. By introducing a new formulation and utilizing a bespoke epidemiological model and control policy, as well as an accelerated Benders decomposition-based algorithm, the authors successfully solve large-sized test problems efficiently. The proposed sharing mechanism significantly reduces the total cost of the system and unmet demand.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Mathematics
Antoine Mallegol, Arwa Khannoussi, Mehrdad Mohammadi, Bruno Lacarriere, Patrick Meyer
Summary: This paper proposes an improved piecewise linearization method for optimizing the design and operation of multi-energy systems (MESs). It models an MES as a multi-objective mixed-integer linear program and solves the optimization problem over a year with hourly resolution. The method uses fewer linear pieces to approximate non-linear functions, resulting in lower complexity while preserving accuracy.
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)
