Article
Computer Science, Artificial Intelligence
Christos Valouxis, Christos Gogos, Angelos Dimitsas, Petros Potikas, Anastasios Vittas
Summary: Machine scheduling is a challenging problem with various real-life applications. By using constraint programming models and a powerful solver, the problem can be effectively solved, and a hybrid approach can further improve the solution quality.
Article
Computer Science, Artificial Intelligence
Shuo Qin, Dechang Pi, Zhongshi Shao
Summary: With the rapid development of cloud computing, scheduling complex scientific workflows on the cloud has become a challenging problem. This paper proposes a novel adaptive iterated local search framework (AILS) to meet budget constraints and demonstrates its effectiveness through comparison.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Mateusz Polnik, Annalisa Riccardi, Kerem Akartunali
Summary: The proposed multistage algorithm, based on constraint programming formulation, can effectively solve the vehicle routing problem with time windows and synchronised visits, and has shown outstanding performance in solving real scheduling problems.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Engineering, Civil
Xinyu Wang, Shuai Shao, Jiafu Tang
Summary: This study introduces mathematical WVRP models and proposes an efficient heuristic method RI-ILS to solve the problem. RI-ILS method performs well in computational experiments, producing good results for both traditional vehicle routing problems and WVRPs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Ala-Eddine Yahiaoui, Sohaib Afifi, Hamid Allaoui
Summary: This paper focuses on the Technician Routing and Scheduling Problem and proposes an enhanced iterated local search approach to solve it. Experimental results show that the proposed method outperforms existing methods in terms of overall gap and computational times.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Nasser R. Sabar, Say Leng Goh, Ayad Turky, Graham Kendall
Summary: This article addresses the dynamic vehicle routing problem (DVRP) and proposes an effective population-based approach to incorporate new customers into the schedule while minimizing the cost of serving all customers without violating problem constraints. Experimental results demonstrate that the integrated components significantly improve search performance and produce new best results for several instances.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Hanyu Gu, Yefei Zhang, Yakov Zinder
Summary: This paper addresses the Workforce Scheduling and Routing Problem and proposes an optimization procedure that combines iterated local search and Lagrangian relaxation. The proposed procedure has been tested on benchmark problems and outperforms a previously published implementation of iterated local search.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Civil
Jiaxi Wang, Yinan Zhao, Manfred Gronalt, Boliang Lin, Jianping Wu
Summary: An integer linear programming (ILP) model is proposed in this paper for the integrated optimization problem of depot service scheduling, train parking, and train routing. A real-world case study of the Shanghai South Depot is conducted to further examine the effectiveness and efficiency of the proposed methodology. The computational results show that the method is able to generate optimized shunting plans for the linearized INLP within reasonable solution time, outperforming the manual method in terms of solution quality and computation speed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Operations Research & Management Science
Mohammed Bazirha, Abdeslam Kadrani, Rachid Benmansour
Summary: Home health care provides a range of medical services for patients at home. This study presents a stochastic programming model with recourse to address the uncertainties and synchronization of services. The objective is to minimize costs and expected values. The deterministic model is solved using various algorithms, while the SPR model is solved using Monte Carlo simulation. Results highlight the complexity of the SPR model compared to the deterministic model.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Vahid Riahi, M. A. Hakim Newton, Abdul Sattar
Summary: The PFSP-SDST problem with sequence-dependent setup times is NP-hard and has practical applications in industries such as cider and print. The proposed CBLS algorithm transforms constraints into an auxiliary objective function to guide the search towards the optimal value of the actual objective function. Experimental results show that the CBLS algorithm outperforms existing state-of-the-art algorithms and obtains new upper bounds for a significant number of problem instances.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Gen-Han Wu, Chen -Yang Cheng, Pourya Pourhejazy, Bai-Lyn Fang
Summary: Major corporations compete over the strengths of their supply chains. This study proposes a new method based on mixed-integer linear programming to solve the production scheduling and routing problem by integrating machine scheduling with vehicle routing. Extensive numerical experiments show that the developed hybrid metaheuristics are effective in solving this problem.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Sarmad Riazi, Bengt Lennartson
Summary: This article presents an improved method for solving conflict-free scheduling and routing of automated guided vehicles, with promising results. The method includes reformulating the mathematical model of the problem, improvements to an existing Benders decomposition method, and a new heuristic that quickly yields high-quality solutions. Additionally, a real-large-scale industrial instance is solved using open-source and commercial solvers, both of which effectively solve the proposed models.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Management
Maximilian Pohl, Christian Artigues, Rainer Kolisch
Summary: This study addresses the problem of runway scheduling during winter operations. A comprehensive optimization model is proposed to handle snow removal, runway assignment, and aircraft scheduling simultaneously. The study formulates the problem using clique inequalities and constraint programming, and proposes an exact solution methodology. The algorithm is applied to real instances from a large international airport and demonstrates good performance in terms of reducing model size and computational time, outperforming alternative approaches.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Felix Winter, Nysret Musliu
Summary: In this study, novel constraint programming models are proposed for real-life paint shop scheduling problems in the automotive supply industry. Experimentally evaluating and comparing our models using real-life instances, we demonstrate that the decision variant of the paint shop scheduling problem is NP-complete, indicating the challenging nature of solving this issue.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Sheng-Long Jiang, Weigang Li, Xuejun Zhang, Chuanpei Xu
Summary: This study considers the temporal and technical constraints of hot rolling production and proposes a solution based on the Pareto local search algorithm, which can effectively solve multi-objective hot rolling scheduling problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Management
Mohammad Jeihoonian, Masoumeh Kazemi Zanjani, Michel Gendreau
Summary: Motivated by the recovery of modular-structured products, this study proposes a flexible design model for a reverse supply chain (RSC) that considers the uncertain behavior of product returns. The model is decomposed into smaller scenario cluster submodels and coordinated using a Lagrangian-progressive hedging-based method. Computational results based on a realistic case demonstrate the superiority of the proposed model and the efficiency of the solution approach.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Adrien Rimele, Michel Gamache, Michel Gendreau, Philippe Grangier, Louis-Martin Rousseau
Summary: RMFS is a type of automated warehouse system recently deployed in e-commerce, consisting of a fleet of small robots tasked with retrieving and storing items in the warehouse. Due to the nature of the e-commerce market and the flexibility of RMFS, there are opportunities to improve warehouse productivity by optimizing operational decisions. Researchers have proposed a mathematical framework to model operational decisions in RMFS as a stochastic dynamic program, aiming to formalize optimization opportunities for developing more advanced methods in a well-defined environment.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Management
Xiangyi Zhang, Lu Chen, Michel Gendreau, Andre Langevin
Summary: In this study, a branch-and-cut algorithm is developed for the vehicle routing problem with two-dimensional loading constraints. The algorithm is shown to be competitive through experimental results, and extensive computational analysis is conducted to investigate the impact of different factors on the algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
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
Operations Research & Management Science
Fabian Torres, Michel Gendreau, Walter Rei
Summary: The growth of e-commerce has led to increased demand for last-mile deliveries, causing congestion in urban areas. Crowdsourcing deliveries can help meet this demand in a cost-effective way. This study introduces a crowd-shipping platform that sells heterogeneous products and details a two-stage stochastic model to address the complexities of delivery requirements and vehicle supply.
TRANSPORTATION SCIENCE
(2022)
Article
Operations Research & Management Science
Xiangyi Zhang, Lu Chen, Michel Gendreau, Andre Langevin
Summary: This study proposes a method for solving the vehicle routing problem with two-dimensional loading constraints. By improving the data structure and dominance rule, and strengthening the linear relaxation, the method outperforms existing methods in solving instances with large rectangular items and achieves optimal solutions for 14 instances for the first time.
TRANSPORTATION SCIENCE
(2022)
Article
Engineering, Industrial
Milad Ghorbani, Mustapha Nourelfath, Michel Gendreau
Summary: This paper presents a stochastic programming approach for determining an optimal maintenance plan for multicomponent systems. The approach takes into account the uncertainty of future operating conditions and balances the trade-off between cost minimization and probability maximization of mission accomplishment.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Transportation Science & Technology
Fabian Torres, Michel Gendreau, Walter Rei
Summary: E-commerce is growing globally as people prefer to stay at home rather than going to physical retail stores, and this trend has been further accelerated by COVID-19. At the same time, crowd-shipping has gained popularity due to the increasing e-commerce demands and the current pressures brought by the pandemic. This study presents a setting where a crowd shipping platform utilizes both professional drivers and occasional drivers to fulfill various delivery requests from a central depot. By dividing the delivery requests into sectors and modeling route duration constraints, the participation and acceptance probabilities of the occasional drivers are motivated. The research findings demonstrate that occasional drivers with destinations far from the depot can reduce costs by over 30%, while those with destinations near the depot can achieve a 20% cost reduction. The study also highlights the importance of considering both route duration constraints and capacity constraints to optimize the occasional driver routes for cost reductions.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
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
Jonathan De La Vega, Michel Gendreau, Reinaldo Morabito, Pedro Munari, Fernando Ordonez
Summary: This paper tackles the vehicle routing problem with time windows and stochastic demands (VRPTWSD). It presents a two-stage stochastic program with recourse for modeling the problem, where routes are planned in the first stage and executed in the second stage. The paper proposes an Integer L-shaped algorithm that considers different recourse actions such as reactive trips, preventive trips, and additional actions to handle violated time windows. Experimental results using benchmark instances demonstrate the effectiveness of the proposed algorithm, particularly when using the fixed rule-based policy.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Alexandre M. Florio, Michel Gendreau, Richard F. Hartl, Stefan Minner, Thibaut Vidal
Summary: This paper examines the stochastic variant of the Vehicle Routing Problem (VRP) called VRPSD, where demands are only revealed upon vehicle arrival at each customer. The paper summarizes recent progress in VRPSD research and introduces two major contributions: a branch-price-and-cut algorithm for optimal restocking and a demand model for correlated customer demands. Computational results demonstrate the effectiveness of the new algorithm and the potential cost savings of over 10% when considering demand correlation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(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
Operations Research & Management Science
Tommaso Schettini, Michel Gendreau, Ola Jabali, Federico Malucelli
Summary: Metro lines are a crucial part of urban public transport in many cities, offering a greener and more efficient alternative to private transportation. However, these lines are often resource constrained, making it difficult to expand capacity. To make better use of existing resources, researchers and operators are exploring ways to adapt timetables to forecasted demand and limited vehicle capacities.
TRANSPORTATION SCIENCE
(2023)
Article
Engineering, Industrial
Milad Ghorbani, Mustapha Nourelfath, Michel Gendreau
Summary: This study investigates selective maintenance for multi-component systems undergoing consecutive missions, using a two-stage stochastic programming approach to address uncertainties and enhance the likelihood of mission success.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiangyi Zhang, Lu Chen, Michel Gendreau, Andre Langevin
Summary: This study addresses a capacitated vehicle routing problem with two-dimensional loading constraints, presenting an exact branch-and-price algorithm and an approximate counterpart to solve the challenging combination of two NP-hard problems. By introducing a supervised learning model in the new column generation mechanism, the algorithm demonstrates significant improvements in efficiency in terms of CPU time and feasibility checker calls.
INFORMS JOURNAL ON COMPUTING
(2022)
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)
