Article
Computer Science, Information Systems
Zhi Lu, Jin-Kao Hao, Una Benlic, David Lesaint
Summary: This paper introduces an iterated multilevel simulated annealing algorithm for large-scale graph conductance minimization, demonstrating high performance on very large real-world sparse graphs, with publicly available source code.
INFORMATION SCIENCES
(2021)
Article
Management
Mhand Hifi, Shohre Sadeghsa
Summary: This study investigates a variant of completion problems in logistics by designing a hybrid algorithm to solve it, and evaluates its performance by comparing the results with the best method available in the literature.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Mathematics, Applied
E. Duman
Summary: The Turkish Cashier Problem (TCP) is a special case of the traveling salesman problem, focusing on finding the optimal route to minimize transportation cost for cashiers. To address this problem, a heuristic algorithm was developed, along with a tight lower bound, and it was demonstrated that the heuristic algorithm performs well for practical instances of the problem.
APPLIED AND COMPUTATIONAL MATHEMATICS
(2022)
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
Management
Pengfei He, Jin-Kao Hao
Summary: This article presents a unified algorithm to solve the minmax multiple traveling salesman problem with single or multiple depots. The algorithm uses crossover operation to generate new solutions, variable neighborhood descent for local optimization, and post-optimization for further improvement. Experimental results show that the algorithm performs well compared to other leading algorithms.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Yuji Zou, Jin-Kao Hao, Qinghua Wu
Summary: This article presents an effective heuristic algorithm for the traveling salesman problem with job-times. The algorithm uses a breakout local search method to find high-quality local optimal solutions and incorporates a perturbation procedure to escape local optimum traps. Computational results show that the algorithm outperforms previous methods on benchmark instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Levi R. Abreu, Bruno A. Prata, Jose M. Framinan, Marcelo S. Nagano
Summary: This paper explores the Open Shop Scheduling Problem and proposes efficient constructive heuristics to solve it. Extensive computational tests demonstrate the excellent performance of the proposed algorithms, making them the best heuristics for this problem so far.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Eduardo G. Carrano, Leticia D. Cruz, Douglas Baptista, Daniel Camargo, Ricardo H. C. Takahashi
Summary: This paper presents a heuristic-based reel management method for the manufacturing process of oil extraction pipes. The method aims to optimize the reel movement plan to improve production efficiency, and performs well in practical applications.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Emilio Singh, Nelishia Pillay
Summary: Research in the applicability of ant-based optimization techniques for hyper-heuristics is limited. This paper presents a novel ant-based generation constructive hyper heuristic and investigates the impact of different pheromone maps on its performance. The study finds that different pheromone maps have varied effects on the performance of the hyper-heuristic for different types of optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(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
Zequn Wei, Jin-Kao Hao, Jintong Ren, Fred Glover
Summary: This paper presents a responsive strategic oscillation algorithm for the NP-hard disjunctively constrained knapsack problem, which achieves high-quality solutions by employing feasible local search and strategic oscillation search. The algorithm also uses a frequency-based perturbation to escape from local optimal traps. Extensive evaluations on benchmark instances and real-world instances demonstrate the effectiveness of the algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Sergio Cavero, Eduardo G. Pardo, Abraham Duarte
Summary: This paper presents an efficient heuristic algorithm based on the Iterated Greedy framework hybridized with a new local search strategy to solve the Two-Dimensional Bandwidth Minimization Problem (2DBMP). Extensive experimentation demonstrates the superior performance of the proposed approach compared to state-of-the-art methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Information Systems
Orachun Udomkasemsub, Booncharoen Sirinaovakul, Tiranee Achalakul
Summary: Hyper-heuristics are highly versatile and adaptable, capable of effectively solving a wide range of complex optimization problems. This paper proposes a framework that utilizes policy-based reinforcement learning to enhance the performance of hyper-heuristics. The framework trains hyper-heuristic agents to select the best generalized constructive low-level heuristics for solving combinatorial optimization problems.
Article
Quantum Science & Technology
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, Mattia Fiorentini
Summary: In this study, four variational quantum heuristics were applied to the job shop scheduling problem on IBM's superconducting quantum processors. The results showed that the filtering variational quantum eigensolver (F-VQE) outperformed other algorithms in terms of convergence speed and sampling the global optimum.
EPJ QUANTUM TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Zequn Wei, Jin-Kao Hao
Summary: The algorithm introduced in this study aims to solve the Set-union Knapsack Problem efficiently through its original kernel-based search components and an effective local search procedure. Extensive computational assessments demonstrate its high performance on benchmark instances. Providing access to the algorithm's code aims to facilitate its practical use.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
Christof Defryn, Kenneth Sorensen, Wout Dullaert
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2019)
Article
Computer Science, Interdisciplinary Applications
Florian Arnold, Kenneth Sorensen
COMPUTERS & OPERATIONS RESEARCH
(2019)
Article
Management
Margaretha Gansterer, Richard F. Hartl, Kenneth Sorensen
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2020)
Article
Management
J. Corstjens, B. Depaire, A. Caris, K. Sorensen
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2020)
Article
Management
Nicholas Vergeylen, Kenneth Sorensen, Pieter Vansteenwegen
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2020)
Editorial Material
Operations Research & Management Science
Kenneth Sorensen
Article
Computer Science, Interdisciplinary Applications
Florian Arnold, Kenneth Soerensen
Summary: This study introduces an efficient and effective heuristic for the LRP, which reduces the solution space by estimating an upper bound for the number of open depots and iteratively applying routing heuristic to each remaining depot configuration. The progressive filtering framework quickly detects unpromising configurations, and a good design combining coarse and fine filters outperforms existing heuristics on various instances.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Renata Turkes, Kenneth Sorensen, Daniel Palhazi Cuervo
Summary: The paper presents a matheuristic approach to solve the stochastic facility location problem, optimizing the storage facility configuration to minimize unmet demand and response time. Numerical experiments show the effectiveness and efficiency of the method, particularly for tackling larger instances.
JOURNAL OF HEURISTICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Babiche Aerts, Trijntje Cornelissens, Kenneth Soerensen
Summary: The research focuses on the joint order batching and picker routing problem (JOBPRP) in a warehouse environment, using a two-level variable neighborhood search (2level-VNS) metaheuristic algorithm. Comparing different batching criteria, it is concluded that the minimum aisles criterion is more suitable for JOBPRP in warehouse contexts.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Management
Renata Turkes, Kenneth Sorensen, Lars Magnus Hvattum
Summary: This paper promotes meta-analysis as a more suitable way to gain problem- and implementation-independent insights on metaheuristics. The research shows that adding an adaptive layer in adaptive large neighborhood search algorithms can improve objective function value slightly, but it also adds complexity and should therefore be recommended in specific situations only.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Transportation Science & Technology
Bryan David Galarza Montenegro, Kenneth Sorensen, Pieter Vansteenwegen
Summary: Feeder services are discussed in two forms: on-demand service and traditional service. Experimental results show that demand-responsive feeder service demonstrates higher service quality compared to traditional service.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Multidisciplinary Sciences
Renata Turkes, Kenneth Sorensen, Lars Magnus Hvattum, Eva Barrena, Hayet Chentli, Leandro C. Coelho, Iman Dayarian, Axel Grimault, Anders N. Gullhav, Cagatay Iris, Merve Keskin, Alexander Kiefer, Richard Martin Lusby, Geraldo Regis Mauri, Marcela Monroy-Licht, Sophie N. Parragh, Juan-Pablo Riquelme-Rodriguez, Alberto Santini, Vinicius Gandra Martins Santos, Charles Thomas
Article
Operations Research & Management Science
Renata Turkes, Daniel Palhazi Cuervo, Kenneth Sorensen
ANNALS OF OPERATIONS RESEARCH
(2019)
Article
Management
Renata Turkes, Kenneth Sorensen
JOURNAL OF HUMANITARIAN LOGISTICS AND SUPPLY CHAIN MANAGEMENT
(2019)
Article
Management
Daniel Nicola
Summary: This paper presents frameworks for auction-based and posted price mechanisms for exchanging requests between carriers operating in the same geographical areas. Results show that individual auction-based mechanisms provide similar results to centralized auction-based mechanisms, both outperforming posted price mechanisms.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2024)
