Article
Computer Science, Artificial Intelligence
Sahar Rahdar, Reza Ghanbari, Khatere Ghorbani-Moghadam
Summary: The researchers improved the model for the bus terminal location problem and proposed two algorithms to solve this NP-hard problem. They demonstrated the efficiency of their proposed approach through experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Biao Zhang, Chao Lu, Lei-lei Meng, Yu-yan Han, Hong-yan Sang, Xu-chu Jiang
Summary: Inspired by a real-world cellular manufacturing system, this study focuses on a reconfigurable distributed flowshop scheduling problem with grouped jobs. A mixed integer linear programming model is developed for small-scaled instances, and a nested variable neighborhood descent algorithm is proposed for larger instances. The proposed algorithm outperforms other state-of-the-art metaheuristics and the math solver CPLEX.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Dung-Ying Lin, Tzu-Yun Huang
Summary: In this study, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. Empirical results show that this solution strategy outperforms a commonly used commercial optimization package and provides better schedules in a more efficient manner.
Article
Computer Science, Theory & Methods
Xueshi Dong, Hong Zhang, Min Xu, Fanfan Shen
Summary: The paper proposes an optimization model named multiple bottleneck traveling salesmen problem (MBTSP) and investigates multi-scale MBTSP and its solving algorithms, introducing a novel hybrid genetic algorithm (VNSGA) which demonstrates better performance than state-of-the-art algorithms.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Biao Zhang, Quan-Ke Pan, Lei-Lei Meng, Xin-Li Zhang, Ya-Ping Ren, Jun-Qing Li, Xu-Chu Jiang
Summary: This paper introduces the issue of consistent sublots into the hybrid flowshop scheduling problem and develops a mixed integer linear programming (MILP) model and a collaborative variable neighborhood descent algorithm (CVND). The CVND shows excellent performance in local exploitation and global search, with high algorithm efficiency. Results indicate that the CVND has significant advantages in solution quality and relative percentage deviation values.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics
Yuanfei Wei, Zalinda Othman, Kauthar Mohd Daud, Shihong Yin, Qifang Luo, Yongquan Zhou
Summary: In this paper, a hybrid algorithm called EOSMA is proposed to solve the Job Shop Scheduling Problem (JSSP). The algorithm combines the update strategy of Equilibrium Optimizer (EO) with Slime Mould Algorithm (SMA) to achieve a better balance between exploration and exploitation. The addition of Centroid Opposition-based Computation (COBC) improves exploration and exploitation, increases population diversity, enhances convergence speed and accuracy, and prevents falling into local optima. The algorithm also introduces a Sort-Order-Index (SOI)-based coding method and a neighbor search strategy to improve the efficiency of solving JSSP. Experimental results and statistical analysis demonstrate that EOSMA outperforms other competing algorithms.
Article
Green & Sustainable Science & Technology
Fuying Liu, Chen Liu, Qi Zhao, Chenhao He
Summary: This paper investigates a travel route optimization problem and proposes the HTLBO algorithm to minimize total traveling time. By establishing a mathematical programming model and utilizing a hybrid metaheuristic algorithm, the feasibility and effectiveness of the algorithm are verified through a case study and simulation results.
Article
Engineering, Industrial
Hongfei Guo, Linsheng Zhang, Yaping Ren, Yun Li, Zhongwei Zhou, Jianzhao Wu
Summary: This paper studies a stochastic disassembly line balancing problem based on expected profit, considering the risks of task failures. A mathematical model is presented to maximize the expected recovering profit, and a hybrid metaheuristic approach is developed to efficiently solve the model. Experimental results demonstrate the superior solution performance of the proposed approach compared to existing methods.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Weitiao Wu, Wei Zhou, Yue Lin, Yuanqi Xie, Wenzhou Jin
Summary: The study introduces a multi-period location-inventory-routing problem and proposes a mixed integer nonlinear programming model with a two-stage hybrid metaheuristic algorithm. Experimental results show significant contributions of inventory management to total cost savings, with optimized inventory levels showing more shortages for retailers and potential increases or decreases for distribution centers.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Kexin Sun, Debin Zheng, Haohao Song, Zhiwen Cheng, Xudong Lang, Weidong Yuan, Jiquan Wang
Summary: This paper proposes an improved hybrid genetic algorithm with variable neighborhood search (HGA-VNS) for addressing the flexible job shop scheduling problem. Experimental results show that the HGA-VNS algorithm is significantly better than other algorithms in terms of performance and can obtain more efficient and economic solutions in practical applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Optics
Tao Zhao, Yongyi Chen, Jinjin Peng, Yao Mao
Summary: In a sparse aperture imaging system, the piston error affects the co-phase of subapertures and reduces the imaging resolution. The piston error correction is converted into an optimization problem using the image quality evaluation function. A hybrid algorithm combining stochastic parallel gradient descent and Gray Wolf algorithm is proposed for global search. The hybrid algorithm effectively balances exploration and exploitation, achieving high optimization speed and correction accuracy. Simulations with three-aperture and seven-aperture systems validate the performance of the hybrid algorithm.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Christophe Wilbaut, Raca Todosijevic, Said Hanafi, Arnaud Freville
Summary: The discounted {0,1} knapsack problem (D{0-1}KP) is a variant of the well-known knapsack problem where items are partitioned into groups and a discount relationship is introduced among items in each group. In this work, a new variable neighborhood search (VNS) algorithm is proposed to solve the D{0-1}KP, and several greedy heuristics are used to build initial feasible solutions. The performance of VNS is evaluated and compared with state-of-the-art metaheuristics, demonstrating its robustness and competitiveness.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Chengshuai Li, Biao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: This paper focuses on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. It proposes an improved cooperative coevolutionary algorithm (vCCEA) by integrating the variable neighborhood descent (VND) strategy. The algorithm features a two-layer encoding strategy, a novel collaborative model, special neighborhood structures, and a collaborative population restart mechanism. The computational results show that vCCEA outperforms other algorithms in solving each subproblem of HFSP_ECS effectively.
Article
Computer Science, Interdisciplinary Applications
Mehmet Ulas Koyuncuog, Leyla Demir
Summary: This study tackles the buffer allocation problem in order to maximize the profit of unreliable production lines. The formulation considers various costs and throughput, and proposes an adaptive hybrid variable neighborhood search algorithm for solving the problem. Additionally, a new initialization procedure based on buffer location is proposed to reduce search effort. Experimental results show that the algorithm is capable of solving profit maximization problems for large production lines, with the number of machines and reliability parameters being the most influential factors.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Civil
Damir Sedlar, Zeljan Lozina, Ivan Tomac
Summary: This paper explores the application of VNS and its extensions to the optimization of truss structures with discrete cross sections, showcasing their effectiveness through various fixed geometry truss examples.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING
(2022)
Article
Engineering, Industrial
Hamza Heni, S. Arona Diop, Jacques Renaud, Leandro C. Coelho
Summary: This paper proposes and evaluates new fuel consumption estimation models for vehicle routing using real-world data and machine learning methods. The results show that the proposed models outperform classical models in terms of accuracy and provide a more precise estimation for fuel consumption in vehicle routing.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Bruno E. Demantova, Cassius T. Scarpin, Leandro C. Coelho, Maryam Darvish
Summary: This paper develops a complex solution algorithm for dealing with the Inventory-Routing Problem with time windows (IRPTW). By utilizing a combination of tools, the algorithm efficiently solves the optimization problem of inventory and routing decisions. The results of the study show promising performance of the algorithm compared to a competing algorithm and provide an overview of the integration of existing techniques in the literature.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Roberto Cantu-Funes, Leandro C. Coelho
Summary: This article studies a pumping scheduling problem for water distribution systems (WDSs) and proposes a high-performance heuristic algorithm. The physical hydraulic behavior is ensured through hydraulic simulation software, and the present method significantly improves the existing solutions.
ENGINEERING OPTIMIZATION
(2023)
Article
Management
Yantong Li, Jean-Francois Cote, Leandro C. Coelho, Chuang Zhang, Shuai Zhang
Summary: This article investigates the problem of order assignment and scheduling under uncertainties in production and distribution processes. The study focuses on companies using a distributed production model and the need for unified information and resources for supply and demand matching. A two-stage stochastic programming model is used to address the problem, and a sample average approximation method is applied to handle a large number of possible scenarios. The results demonstrate the superiority of the proposed model and the LBBD method over others.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Operations Research & Management Science
Eva Barrena, David Canca, Leandro C. Coelho, Gilbert Laporte
Summary: This article investigates a generalization of the selective traveling salesman problem where the benefit of visiting a location changes over time. Known as the selective traveling salesman problem with time-dependent profits (STSP-TDP), the problem involves maximizing the total profit of a circuit on a graph with time-dependent profits associated with the vertices. The problem finds applications in tourism itineraries, mailbox collection, military surveillance, and water sampling. The article proposes mathematical formulations for both single-visit and multiple-visit cases, and solves them using a general-purpose solver, providing a detailed analysis of the problem and solutions.
Article
Management
Guilherme O. Chagas, Leandro C. Coelho, Maryam Darvish, Jacques Renaud
Summary: Bio-based waste valorization is a current trend in municipal waste management that reduces waste and promotes circular economy in cities. However, modeling and optimizing biorefinery plant operations pose challenges and require innovative approaches and solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Editorial Material
Computer Science, Interdisciplinary Applications
Yue Guo, Lean Yu, Yichuan Ding, Leandro C. Coelho
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2022)
Article
Engineering, Industrial
Allyson Silva, Kees Jan Roodbergen, Leandro C. Coelho, Maryam Darvish
Summary: The ABC storage is a popular policy for storage location assignment in warehouses. However, using arbitrary zone sizes can result in efficiency losses. This study proposes a new methodology using machine learning models to predict optimal zone sizes considering factors such as warehouse layout, demand characteristics, and storage policies.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2022)
Article
Economics
M. Amine Masmoudi, Leandro C. Coelho, Emrah Demir
Summary: This paper investigates the problem of designing refuse vehicle routes for commercial waste collection and proposes a Hybrid Threshold Acceptance algorithm to solve the problem. Extensive computational experiments confirm the good performance of the proposed algorithm and demonstrate the benefits of using hybrid electric refuse vehicles in terms of operational costs and total distance traveled.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Computer Science, Interdisciplinary Applications
Farouk Hammami, Monia Rekik, Leandro C. Coelho
Summary: We address a new variant of the Bid Construction Problem (BCP) with stochastic clearing prices in combinatorial auctions for truckload transportation service procurement. Our proposed exact method generates multiple nonoverlapping bids and ensures non-negative profit for the carrier regardless of auction outcomes and materialization of contracts. Experimental results demonstrate the good performance of our approach and reveal potential profit gains compared to a standard BCP.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
Carise E. Schmidt, Arinei C. L. Silva, Maryam Darvish, Leandro C. Coelho
Summary: This paper introduces an extension of classical distribution problems, the time-dependent fleet size and mix multi-depot vehicle routing problem, which is crucial for urban logistics and service designs. The authors propose a mathematical model and a powerful matheuristic to solve this challenging problem, demonstrating that the solution can help improve traffic and congestion issues for distribution companies. The computational results show that the proposed approaches outperform the exact method in terms of solution quality and computational time, and highlight the importance of considering congestion information in algorithm design.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Management
Thiago A. Guimaraes, Cleder M. Schenekemberg, Leandro C. Coelho, Cassius T. Scarpin, Jose E. Pecora
Summary: In this article, a new modular mechanism is proposed to improve the feasibility and quality of solutions for inventory-routing problems. The mechanism can be integrated into different optimization algorithms and achieves better results compared to other approaches. Experimental results demonstrate the effectiveness of the proposed method, achieving optimal solutions for various instances.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Computer Science, Information Systems
Rabie Jaballah, Rodrigo Ramalho, Jacques Renaud, Leandro C. Coelho
Summary: Traffic and congestion significantly impact transportation systems. Travel time models are necessary to calculate trip durations and arrival times when traffic information is available. The challenge is efficiently using the precise data captured from onboard devices to solve routing problems. This article analyzes the impact of time aggregation on travel time models and concludes that they share similar performance with large intervals.
Article
Operations Research & Management Science
Cleder Marcos Schenekemberg, Thiago Andre Guimaraes, Antonio Augusto Chaves, Leandro C. Coelho
Summary: This paper investigates the production and inventory routing problems in a single-product supply chain under a vendor-managed inventory system. The authors propose two-and three-index formulations and apply branch-and-cut algorithm as well as a local search matheuristic-based algorithm to solve the problem. They design a parallel framework called 3FP-B&C to integrate all three fronts of the algorithms. The experimental results show that their method outperforms other approaches from the literature in both the inventory routing problem and the production routing problem.
TRANSPORTATION SCIENCE
(2023)
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)
