Suchergebnisse - (automatic OR automatizace) programming general terms algorithms keywords coevolution~

Multi-period fourth-party logistics network design from the viability perspective: a collaborative hyper-heuristic embedded with double-layer Q-learning algorithm.

Autoren: Zhang, Yuxin Huang, Min Gao, Zheming et al.

Quelle: International Journal of Production Research; May2025, Vol. 63 Issue 9, p3300-3330, 31p

Schlagwörter: THIRD-party logistics, NP-hard problems, INTEGER programming, SATISFACTION, FORTIFICATION

A Multi-Objective Artificial Bee Colony Algorithm Incorporating Q-Learning Search for the Flexible Job Shop Scheduling Problems with Multi-Type Automated Guided Vehicles.

Autoren: Ge, Shihong Zhang, Hao Xu, Zhigang et al.

Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 20, p10948, 29p

Schlagwörter: PRODUCTION scheduling, AUTOMATED guided vehicle systems, MULTI-objective optimization, ENERGY consumption, MANUFACTURING industries, MIXED integer linear programming, REINFORCEMENT learning, BEES algorithm

A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming.

Autoren: Zeiträg, Yannik Rui Figueira, José Figueira, Gonçalo

Quelle: International Journal of Production Research; Aug2024, Vol. 62 Issue 16, p5850-5877, 28p

Schlagwörter: ECONOMIC lot size, PRODUCTION scheduling, GENETIC programming, COEVOLUTION, PRODUCTION planning

A cooperative iterated greedy algorithm for the serial distributed permutation flowshop scheduling problem.

Autoren: Han, Biao Pan, Quan-Ke Gao, Liang

Quelle: International Journal of Production Research; Jun2024, Vol. 62 Issue 12, p4245-4272, 28p

Schlagwörter: GREEDY algorithms, DISTRIBUTED algorithms, PERMUTATIONS, LINEAR programming, SCHEDULING

Firma/Körperschaft: NATIONAL Endowment for the Humanities

A novel machine-learning rolling horizon heuristic for dynamic lot-sizing and job shop scheduling problems.

Autoren: Zeiträg, Yannik Figueira, José Rui

Quelle: International Journal of Production Research; Jun2025, Vol. 63 Issue 12, p4563-4589, 27p

Schlagwörter: PRODUCTION planning, GENETIC programming, STOCHASTIC programming, MATHEMATICAL programming, MACHINE learning, ECONOMIC lot size, PRODUCTION scheduling

Heterogeneous Cooperative Coevolution: Strategies of Integration between

Autoren: Leonardo Vanneschi Giancarlo Mauri Andrea Valsecchi et al.

Weitere Verfasser: Leonardo Vanneschi Giancarlo Mauri Andrea Valsecchi et al.

Quelle: http://www.disco.unimib.it/upload/p361-vanneschi.pdf.

Schlagwörter: Categories and Subject Descriptors I.2 [Artificial Intelligence, Automatic Programming General Terms Algorithms Keywords Coevolution, Genetic Algorithms, Genetic Programming

Dateibeschreibung: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.277

Verfügbarkeit: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.277
http://www.disco.unimib.it/upload/p361-vanneschi.pdf

OPTIMIZING ASSEMBLY LINE BALANCING WITH CARBON FOOTPRINT AND SPATIAL CONSTRAINTS: A CUSTOMIZED Q-LEARNING ALGORITHM.

Autoren: Yuchen Li Yuqing Tian

Quelle: International Journal of Industrial Engineering; 2025, Vol. 32 Issue 5, p1204-1222, 19p

Schlagwörter: ASSEMBLY line balancing, SUSTAINABILITY, MIXED integer linear programming, RESOURCE allocation, ECOLOGICAL impact, REINFORCEMENT learning, CONSTRAINT satisfaction, INDUSTRIAL efficiency

A Novel Sorting Route Planning Method for Irregular Sheet Parts in the Shipbuilding Process.

Autoren: Xing, Hongyan Luo, Cheng Song, Jichao et al.

Quelle: Journal of Marine Science & Engineering; Oct2025, Vol. 13 Issue 10, p1871, 20p

Schlagwörter: SHIPBUILDING, AUTOMATION, SORTING (Electronic computers), MATHEMATICAL programming, COMPUTER vision, LASER based sensors, ROUTE choice

TCDE: Differential Evolution for Topographical Contour-Based Prediction to Solve Multimodal Optimization Problems.

Autoren: Cheng, Zhen Zhang, Yun Fan, Caifu et al.

Quelle: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7557, 26p

Schlagwörter: DIFFERENTIAL evolution, OPTIMIZATION algorithms, BENCHMARK problems (Computer science), CONTOURS (Cartography), HEURISTIC, MATHEMATICAL optimization, MATHEMATICAL programming

A graph partitioning based cooperative coevolution for the batching problem in steelmaking production.

Autoren: Wang, Gongshu Guo, Qingxin Xu, Wenjie et al.

Quelle: International Journal of Production Research; Oct2022, Vol. 60 Issue 19, p5876-5891, 16p, 4 Diagrams, 4 Charts

Schlagwörter: DIFFERENTIAL evolution, COEVOLUTION, STEELMAKING furnaces, STEEL manufacture, MASS production, CLASSIFICATION algorithms, MANUFACTURING processes

Applications, classifications, and challenges: a comprehensive evaluation of recently developed metaheuristics for search and analysis.

Autoren: Shaikh, Muhammad Suhail Raj, Saurav Zheng, Gengzhong et al.

Quelle: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-110, 110p

Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features.

Autoren: Akyol, Erhan Alatas, Bilal Ozgen, Inanc

Quelle: Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2207, 22p

Schlagwörter: EVOLUTIONARY algorithms, PEST control, ARTIFICIAL intelligence, AGRICULTURAL industries, INTEGRATED pest control, FRUIT quality, DATA mining

Symmetry-Driven Two-Population Collaborative Differential Evolution for Parallel Machine Scheduling in Lace Dyeing with Probabilistic Re-Dyeing Operations.

Autoren: Wang, Jing Lian, Jingsheng Deng, Youpeng et al.

Quelle: Symmetry (20738994); Aug2025, Vol. 17 Issue 8, p1243, 29p

Schlagwörter: COMPUTER scheduling, DIFFERENTIAL evolution, STOCHASTIC programming, TEXTILE factories, DYES & dyeing, SYMMETRY, SCHEDULING

Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning: Learn to optimise for job shop scheduling: a survey...: M. Xu et al.

Autoren: Xu, Meng Mei, Yi Zhang, Fangfang et al.

Quelle: Artificial Intelligence Review; Jun2025, Vol. 58 Issue 6, p1-53, 53p

Schlagwörter: PRODUCTION scheduling, GENETIC programming, MANUFACTURING processes, SCHEDULING

Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming.

Autoren: Lopes, Rui L. Figueira, Gonçalo Amorim, Pedro et al.

Quelle: International Journal of Production Research; Jan2020, Vol. 58 Issue 2, p509-525, 17p, 2 Diagrams, 13 Charts, 2 Graphs

Schlagwörter: INVENTORY control, INVENTORY management systems, GENETIC programming, COEVOLUTION, CLOSED loop systems, OVERHEAD costs, COST structure, ORDER management systems, MAGNITUDE (Mathematics)

The Scientific Landscape of Hyper-Heuristics: A Bibliometric Analysis Based on Scopus.

Autoren: Peñate-Rodríguez, Helen C. Rivera, Gilberto Sánchez-Solís, J. Patricia et al.

Quelle: Algorithms; May2025, Vol. 18 Issue 5, p294, 25p

Schlagwörter: BIBLIOMETRICS, HEURISTIC, DATABASES, RESEARCH personnel, PROBLEM solving

Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions.

Autoren: Chen, YiFan Chan, Weng Howe Su, Eileen Lee Ming et al.

Quelle: PeerJ Computer Science; Jul2025, p1-47, 47p

Schlagwörter: MULTI-objective optimization, SMART cities, URBANIZATION, ALGORITHMS, SUSTAINABILITY, BENCHMARK problems (Computer science), MACHINE learning, CRITICAL analysis

An Adaptive Genetic Algorithm with Optimal Recombination for Scheduling Problems with Energy Resource.

Autoren: Sakhno, M. Y.

Quelle: Numerical Analysis & Applications; Sep2025, Vol. 18 Issue 3, p268-282, 15p

A novel auto-pruned ensemble clustering via SOCP.

Autoren: Üçüncü, Duygu Akyüz, Süreyya Gül, Erdal

Quelle: Central European Journal of Operations Research; Sep2024, Vol. 32 Issue 3, p819-841, 23p

Schlagwörter: OPERATIONS research, CONVEX programming, INTEGER programming, DECISION making, ALGORITHMS

Constrained multimodal multi-objective optimization based on cooperative evolution and neural network.

Autoren: Cao, Jie Wang, Yiyuan Zhang, Jianlin et al.

Quelle: International Journal of Machine Learning & Cybernetics; Nov2025, Vol. 16 Issue 11, p9535-9553, 19p