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Article
Management
Stefan Wagner et al.
Summary: This paper discusses the order batching problem in manual order picking systems and proposes a variable neighborhood search (VNS) scheme to tackle large-sized problem instances. The VNS scheme, combined with a set partitioning problem formulation, provides high-quality solutions. Computational experiments show that the VNS scheme outperforms existing approaches and that a warehouse setup with heterogeneous pick devices offers significant cost savings.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Review
Management
M. Geurtsen et al.
Summary: This paper provides a comprehensive review on the integration of production, maintenance, and resource scheduling, and offers recommendations for future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Industrial
Mahdi Jemmali et al.
Summary: The main focus of this study is the scheduling problem of minimizing the makespan on two identical parallel machines with mold constraints. The mold constraint is described as a resource-constrained problem. Ten heuristics based on different approaches have been developed and discussed to solve the NP-hard problem. In addition, a new lower bound is proposed and computational results show the effectiveness of the heuristics and the higher performance of the proposed lower bound.
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Andrea Campagner et al.
Summary: In this study, we compared 21 learning and aggregation methods proposed in the fields of ensemble learning, social choice theory, information fusion and uncertainty management, and collective intelligence based on 40 benchmark datasets. The results showed that Bagging-based approaches performed similarly to XGBoost and outperformed other Boosting methods. ExtraTree-based approaches were as accurate as both XGBoost and Decision Tree-based ones while being more computationally efficient. Standard Bagging-based and IF-UM-inspired approaches outperformed CI and SCT-based approaches. IF-UM-inspired approaches had the best performance and the strongest resistance to label noise. Based on our findings, we provide practical insights into the effectiveness of different state-of-the-art ensemble and aggregation methods.
INFORMATION FUSION
(2023)
Article
Engineering, Industrial
Salama Shady et al.
Summary: This paper proposes a feature selection approach based on the Gene Expression Programming (GEP) algorithm to evolve high-quality scheduling rules in simple structures. By restricting the search space and selecting only meaningful features, this approach can speed up the search process and generate rules with high interpretability.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Davut Ari et al.
Summary: The intelligent system applications require automated data-driven modeling tools. The DEC-GEP algorithm suggests using the Differential Evolution (DE) algorithm for the optimization of expression trees and constant terminals, leading to more efficient evolution of GEP expression trees and dependable modeling results.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Ying Liu et al.
Summary: We propose a branch-and-bound algorithm to solve a project scheduling problem involving unit-capacity resources and transfer times. The algorithm combines a branching scheme and a scheduling method to explore the branch-and-bound tree efficiently. Five effective dominance rules are designed to speed up the exploration process. Computational experiments show that our algorithm outperforms existing mathematical models solved by CPLEX or CP Optimizer.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Social Sciences, Interdisciplinary
Hongliang Zhang et al.
Summary: Emergencies such as machine breakdowns and rush orders have a significant impact on manufacturing enterprises, making it crucial to address rescheduling problems. With the rise of intelligent manufacturing, automatic guided vehicles are being utilized in enterprises. To tackle the flexible job shop scheduling problem with automatic guided vehicle transportation, a mixed-integer linear programming model is established. An improved NSGA-II algorithm is utilized to minimize makespan, energy consumption, and machine workload deviation. Solution qualities are enhanced through a local search operator based on a critical path, while an improved crowding distance calculation method reduces computation complexity. The validity, robustness, and superiority of the proposed algorithm are confirmed through comparisons with NSGA, NSGA-II, and SPEA2.
Article
Computer Science, Artificial Intelligence
Bin Cao et al.
Summary: In this article, a novel neural network model is proposed by integrating gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate more expressive fuzzy rules. The network training is treated as a multiobjective optimization problem to consider network precision, explainability, and generalization simultaneously. An enhanced distributed parallel multiobjective evolutionary algorithm is introduced to explore different forms of fuzzy rules and improve precision and convergence.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Engineering, Industrial
Roland Braune et al.
Summary: This paper presents a Genetic Programming approach for solving flexible shop scheduling problems, generating priority rules for job dispatching and minimizing the makespan. Through testing on benchmark problem settings and a special industrial case, along with comparison between single-tree and multi-tree approaches, the study demonstrates consistent performance improvements over existing priority rules.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2022)
Article
Operations Research & Management Science
Haitao Liu et al.
Summary: The study focuses on an integrated production and delivery scheduling problem with non-stationary demand in a two-stage supply chain, offering an ADP solution. By introducing the SPTm/FCFD principle and constraints, the objective of minimizing order waiting time is achieved. Experimental results confirm the superior performance of the ADP policy and the impact of demand features on policy effectiveness.
NAVAL RESEARCH LOGISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zilong Zhuang et al.
Summary: This study develops a network-based dynamic dispatching rule generation mechanism utilizing complex network theory and entropy weighting method to extract and automatically generate low-level heuristics from the perspective of system optimization to address real-time production scheduling issues.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Information Systems
Jeevanantham Balusamy et al.
Summary: The article discusses the use of artificial immune system, randomized gossip algorithm, and particle swarm optimization for task scheduling in the cloud environment to efficiently allocate tasks and achieve uniform distribution.
DISTRIBUTED AND PARALLEL DATABASES
(2022)
Article
Computer Science, Interdisciplinary Applications
Mehdy Morady Gohareh et al.
Summary: This research focuses on the job shop scheduling problem with stochastic process times and weighted earliness-tardiness objective function. It aims to develop a solution method that delivers dynamic and global dispatching rules based on information from the entire shop floor. The problem is converted into a near-Markov decision process, and a database of states is generated using simulation and ant colony. The results show significant cost reduction and improved performance in congested scheduling systems.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Hegen Xiong et al.
Summary: This paper provides a comprehensive review of the types and models of job shop scheduling problem (JSSP) and analyzes and classifies its entities, attributes, assumptions, and performance measures. Through extensive statistics and analysis of published papers, promising research directions are identified.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
Salama Shady et al.
Summary: Thanks to advances in computational power and machine learning algorithms, Genetic Programming (GP) can be used to automatically design scheduling rules for dynamic job shop scheduling problems. However, the computational costs and interpretability of the rules remain significant limitations. In this paper, a new representation of GP rules and an adaptive feature selection mechanism are proposed to improve solution quality by limiting the search space and generating more interpretable rules.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Yu Xue et al.
Summary: This paper proposes a self-adaptive gradient descent search algorithm (SaGDSA) for parameter search in fully-connected neural networks. The algorithm combines the advantages of evolutionary computation and gradient descent, providing both global search and local search capabilities.
Article
Multidisciplinary Sciences
Lingxuan Liu et al.
Summary: This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage, considering incompatible job families and limited buffer size. The automatic design of efficient heuristics based on genetic programming method successfully generates scheduling rules to minimize total completion time. The proposed genetic programming with cooperative co-evolution approach outperforms both constructive heuristic and meta-heuristic algorithms in producing high-quality schedules within seconds.
Article
Computer Science, Artificial Intelligence
Yannik Zeitrag et al.
Summary: This article introduces a new approach of reducing the computational costs of simulation-based fitness evaluation by utilizing surrogate models, and demonstrates the effectiveness and performance advantages of this method in solving dynamic job shop scheduling problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jianwei Zhao et al.
Summary: This article studies the scheduling strategy of multiple mobile sinks in wireless sensor networks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed, which can balance multiple objectives and complete complex scheduling tasks.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Weishi Shao et al.
Summary: This paper investigates the distributed flow shop scheduling problem in heterogeneous multi-factories and proposes a mixed-integer linear programming model and a multi-local search algorithm to solve it. The effectiveness and efficiency of the proposed methods are demonstrated through experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Deming Lei et al.
Summary: An improved variable neighborhood search (IVNS) is proposed in this study to optimize the parallel drone scheduling traveling salesman problem (PDSTSP) in the truck-drone hybrid delivery system. Experimental results show that the IVNS method performs competitively on the PDSTSP problem and obtains state-of-the-art solutions for 12 instances.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Guohui Zhang et al.
Summary: This research proposes an effective two-stage algorithm based on convolutional neural network for solving the flexible job shop scheduling problem. The algorithm is used to train the prediction model and evaluate the robustness of scheduling, with the evaluation done through the proposed RMn metric.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Cristiane Ferreira et al.
Summary: The emergence of Industry 4.0 is making production systems more flexible and dynamic, requiring real-time scheduling adaptation. Machine learning methods have been developed to improve scheduling rules, but they often lack interpretability and generalization. This paper proposes a novel approach that combines machine learning with domain problem reasoning to guide the empirical search for effective and interpretable dispatching rules. The experimental results show that the proposed approach outperforms existing literature in various scenarios, indicating its potential as a new paradigm for applying machine learning to dynamic optimization problems.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2022)
Article
Automation & Control Systems
Chunjiang Zhang et al.
Summary: This paper proposes a dynamic scheduling framework based on an improved gene expression programming algorithm to address the dynamic flexible job shop scheduling problem considering setup time and random job arrival. Experimental results demonstrate that the improved gene expression programming outperforms standard gene expression programming, genetic programming, and scheduling rules.
MEASUREMENT & CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Julian Lee et al.
Summary: The paper introduces a new clustering algorithm, SAGMDE, based on simulated annealing with two perturbation methods for large and small disturbances. Experimental results on various datasets show that SAGMDE performs more consistently in terms of cluster quality compared to existing clustering algorithms. Visual comparisons using generative art are used to evaluate different clustering algorithms.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Interdisciplinary Applications
Huali Fan et al.
Summary: This paper introduces a mathematical programming model for dynamic job shop scheduling problem with extended technical precedence constraints (ETPC) and utilizes a constructive heuristic to solve large-scale problems. It demonstrates the effectiveness of genetic programming-based hyper-heuristic approach in generating problem-specific dispatching rules.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Shih-Wei Lin et al.
Summary: Scheduling problems are crucial in modern manufacturing, and an improved meta-heuristic algorithm, MTSA, has been proposed for Permutation Flowshop Scheduling Problem with Mixed-Blocking Constraints, outperforming existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
J. Behnamian et al.
Summary: This study addresses a bi-objective flexible open shop scheduling problem, aiming to optimize the schedule by balancing workload and reducing job delivery delays from the customers' perspective. The proposed algorithm, which includes a scatter search algorithm, outperforms the NSGA-II in terms of efficiency and solution quality after comparisons using multiple evaluation metrics.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Automation & Control Systems
Fangfang Zhang et al.
Summary: A novel two-stage GPHH framework with feature selection is designed in this article to automatically evolve scheduling heuristics in DFJSS, and individual adaptation strategies are proposed to utilize information. Results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes, and reach comparable scheduling heuristic quality with much shorter training time than traditional algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Amir Ghasemi et al.
Summary: This research advances evolutionary SO methods literature by exploring the use of metamodeling within these techniques. The proposed ELBSO method utilizes a Machine Learning based simulation metamodel created using Genetic Programming to optimize the Stochastic Job Shop Scheduling Problem, outperforming other algorithms in solution quality and computation time.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Aydin Teymourifar et al.
COGNITIVE COMPUTATION
(2020)
Article
Computer Science, Interdisciplinary Applications
M. K. Marichelvam et al.
COMPUTERS & OPERATIONS RESEARCH
(2020)
Article
Computer Science, Artificial Intelligence
Emmanuel Kieffer et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)
Article
Computer Science, Theory & Methods
Na Yi et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
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Ronghua Chen et al.
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Su Nguyen et al.
EVOLUTIONARY COMPUTATION
(2019)
Article
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Gurkan Ozturk et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2019)
Article
Computer Science, Artificial Intelligence
John Park et al.
APPLIED SOFT COMPUTING
(2018)
Article
Thermodynamics
Liping Zhang et al.
Article
Computer Science, Artificial Intelligence
Yi Mei et al.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Yi Mei et al.
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2016)
Article
Computer Science, Artificial Intelligence
Juergen Branke et al.
EVOLUTIONARY COMPUTATION
(2015)
Article
Computer Science, Interdisciplinary Applications
Li Nie et al.
COMPUTERS & INDUSTRIAL ENGINEERING
(2013)
Article
Management
Edmund K. Burke et al.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2013)
Article
Computer Science, Artificial Intelligence
Edmund K. Burke et al.
EVOLUTIONARY COMPUTATION
(2012)
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Li Nie et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2010)
Article
Computer Science, Artificial Intelligence
Kaikuo Xu et al.
APPLIED SOFT COMPUTING
(2009)
