Effective problem solving in logistics with new framework for connecting problems and metaheuristics
Vol 9, Issue 2, 2025
VIEWS - 644 (Abstract)
Abstract
Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.
Keywords
Full Text:
PDFReferences
- Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic Algorithms: A Comprehensive Review. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications.
- Abdel-Basset, M., Mohamed, R., Hezam, I. M., et al. (2024). An improved nutcracker optimization algorithm for discrete and continuous optimization problems: Design, comprehensive analysis, and engineering applications. Heliyon, 10(17), e36678. https://doi.org/10.1016/j.heliyon.2024.e36678
- Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408
- Afrasyabi, P., Mesgari, M. S., El-kenawy, E. M., et al. (2023). A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems. Decision Analytics Journal, 9, 100356. https://doi.org/10.1016/j.dajour.2023.100356
- Alvarez, P. P., Espinoza, A., Maturana, S., et al. (2020). Improving consistency in hierarchical tactical and operational planning using Robust Optimization. Computers & Industrial Engineering, 139, 106112. https://doi.org/10.1016/j.cie.2019.106112
- Badejo, O., & Ierapetritou, M. (2022). Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis. Computers & Chemical Engineering, 161, 107759. https://doi.org/10.1016/j.compchemeng.2022.107759
- Blocho, M. (2020). Heuristics, metaheuristics, and hyperheuristics for rich vehicle routing problems. Smart Delivery Systems.
- Chaharsooghi, S. K., & Meimand Kermani, A. H. (2008). An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP). Applied Mathematics and Computation, 200(1), 167–177. https://doi.org/10.1016/j.amc.2007.09.070
- Cui, J., Wu, L., Huang, X., et al. (2024). Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning. Knowledge-Based Systems, 288, 111459. https://doi.org/10.1016/j.knosys.2024.111459
- Dagdia, Z. C., & Mirchev, M. (2020). When Evolutionary Computing Meets Astro- and Geoinformatics. Knowledge Discovery in Big Data from Astronomy and Earth Observation.
- Darvishpoor, S., Darvishpour, A., Escarcega, M., et al. (2023). Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones. Drones, 7(7), 427. https://doi.org/10.3390/drones7070427
- Diab, A. A. Z., Tolba, M. A., El-Rifaie, A. M., et al. (2022). Photovoltaic parameter estimation using honey badger algorithm and African vulture optimization algorithm. Energy Reports, 8, 384–393. https://doi.org/10.1016/j.egyr.2022.05.168
- Esmaelian, M., Tavana, M., Santos-Arteaga, F. J., et al. (2018). A novel genetic algorithm based method for solving continuous nonlinear optimization problems through subdividing and labeling. Measurement, 115, 27–38. https://doi.org/10.1016/j.measurement.2017.09.034
- Ezugwu, A. E., Adeleke, O. J., Akinyelu, A. A., et al. (2019). A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems. Neural Computing and Applications, 32(10), 6207–6251. https://doi.org/10.1007/s00521-019-04132-w
- Ezugwu, A. E., Shukla, A. K., Nath, R., et al. (2021). Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artificial Intelligence Review, 54(6), 4237–4316. https://doi.org/10.1007/s10462-020-09952-0
- Ghafari, R., & Mansouri, N. (2023). E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing. Sustainable Computing: Informatics and Systems, 40, 100918. https://doi.org/10.1016/j.suscom.2023.100918
- Gil-Rios, M. A., Cruz-Aceves, I., Cervantes-Sanchez, F., et al. (2021). Automatic enhancement of coronary arteries using convolutional gray-level templates and path-based metaheuristics. Recent Trends in Computational Intelligence Enabled Research.
- Gritsch, M. (2001). The role of logistics strategy in corporate competitiveness: challenges and opportunities for Hungarian companies [PhD thesis] (Hungarian). Budapest University of Economics and Public Administration.
- Hladík, M. (2022). Discrete and Continuous Optimization. Available online: https://kam.mff.cuni.cz/~hladik/DSO/text_dso_en.pdf (accessed on 2 November 2024).
- Jiang, J., Jiang, R., Meng, X., et al. (2020). SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems. Expert Systems with Applications, 144, 113118. https://doi.org/10.1016/j.eswa.2019.113118
- Kóczy, L. T., Földesi, P., & Tüű-Szabó, B. (2018). Enhanced discrete bacterial memetic evolutionary algorithm - An efficacious metaheuristic for the traveling salesman optimization. Information Sciences, 460–461, 389–400. https://doi.org/10.1016/j.ins.2017.09.069
- Liu, J., Zhao, F., Li, Y., et al. (2023). A new global sine cosine algorithm for solving economic emission dispatch problem. Information Sciences, 648, 119569. https://doi.org/10.1016/j.ins.2023.119569
- Lu, Z., Feng, T., & Li, X. (2013). Low-carbon emission/economic power dispatch using the multi-objective bacterial colony chemotaxis optimization algorithm considering carbon capture power plant. International Journal of Electrical Power & Energy Systems, 53, 106–112. https://doi.org/10.1016/j.ijepes.2013.03.040
- Lyu, P., Luo, Q., Wang, T., et al. (2023). Railway gravity retaining wall design using the flower pollination algorithm. Transportation Geotechnics, 42, 101065. https://doi.org/10.1016/j.trgeo.2023.101065
- Ma, Z., Wu, G., Suganthan, P. N., et al. (2023). Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms. Swarm and Evolutionary Computation, 77, 101248. https://doi.org/10.1016/j.swevo.2023.101248
- Makhmudov, F., Kilichev, D., & Cho, Y. I. (2024). An application for solving minimization problems using the Harmony search algorithm. SoftwareX, 27, 101783. https://doi.org/10.1016/j.softx.2024.101783
- Mohammadi, A., & Sheikholeslam, F. (2023). Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022). Engineering Applications of Artificial Intelligence, 126, 106959. https://doi.org/10.1016/j.engappai.2023.106959
- Nohair, L., Adraoui, A. E., & Namir, A. (2024). An Improved Hybrid Metaheuristic for Active Job-Shop Scheduling Problems. Procedia Computer Science, 231, 56–62. https://doi.org/10.1016/j.procs.2023.12.164
- Osaba, E., Villar-Rodriguez, E., Del Ser, J., et al. (2021). A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm and Evolutionary Computation, 64, 100888. https://doi.org/10.1016/j.swevo.2021.100888
- Pérez, C., Climent, L., Nicoló, G., et al. (2023). A hybrid metaheuristic with learning for a real supply chain scheduling problem. Engineering Applications of Artificial Intelligence, 126, 107188. https://doi.org/10.1016/j.engappai.2023.107188
- Rajaguru, V., & Annapoorani, K. I. (2023). Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using African vulture optimization algorithm. Journal of Energy Storage, 65, 107343. https://doi.org/10.1016/j.est.2023.107343
- Roghanian, E., & Pazhoheshfar, P. (2014). An optimization model for reverse logistics network under stochastic environment by using genetic algorithm. Journal of Manufacturing Systems, 33(3), 348–356. https://doi.org/10.1016/j.jmsy.2014.02.007
- Sadollah, A., Eskandar, H., Bahreininejad, A., et al. (2015). Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Computers & Structures, 149, 1–16. https://doi.org/10.1016/j.compstruc.2014.12.003
- Schmidt, B., Al-Fuqaha, A., Gupta, A., et al. (2017). Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification. Applied Soft Computing, 54, 1–22. https://doi.org/10.1016/j.asoc.2017.01.016
- Sharma, S., Bhattacharjee, S., & Bhattacharya, A. (2016). Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network. International Journal of Electrical Power & Energy Systems, 74, 348–373. https://doi.org/10.1016/j.ijepes.2015.07.034
- Silveira, C. L. B., Tabares, A., Faria, L. T., et al. (2021). Mathematical optimization versus Metaheuristic techniques: A performance comparison for reconfiguration of distribution systems. Electric Power Systems Research, 196, 107272. https://doi.org/10.1016/j.epsr.2021.107272
- Skinderowicz, R. (2022). Improving Ant Colony Optimization efficiency for solving large TSP instances. Applied Soft Computing, 120, 108653. https://doi.org/10.1016/j.asoc.2022.108653
- Sörensen, K., Glover, F. (2010). Metaheuristics. In: Encyclopedia of Operations Research and Management Science. Springer, New York.
- SteadieSeifi, M. (2011). Logistics Strategic Decisions. Logistics Operations and Management.
- Swan, J., Adriaensen, S., Brownlee, A. E. I., et al. (2022). Metaheuristics “In the Large.” European Journal of Operational Research, 297(2), 393–406. https://doi.org/10.1016/j.ejor.2021.05.042
- Talbi, E.-G. (2024). Metaheuristics for variable-size mixed optimization problems: A unified taxonomy and survey. Swarm and Evolutionary Computation, 89, 101642. https://doi.org/10.1016/j.swevo.2024.101642
- Turkoglu, B., Uymaz, S. A., & Kaya, E. (2023). Chaos theory in metaheuristics. Comprehensive Metaheuristics.
- Wang, Y., & Han, Z. (2021). Ant colony optimization for traveling salesman problem based on parameters optimization. Applied Soft Computing, 107, 107439. https://doi.org/10.1016/j.asoc.2021.107439
- Worawattawechai, T., Intiyot, B., Jeenanunta, C., et al. (2022). A learning enhanced golden ball algorithm for the vehicle routing problem with backhauls and time windows. Computers & Industrial Engineering, 168, 108044. https://doi.org/10.1016/j.cie.2022.108044
- Wu, H., & Gao, Y. (2023). An ant colony optimization based on local search for the vehicle routing problem with simultaneous pickup–delivery and time window. Applied Soft Computing, 139, 110203. https://doi.org/10.1016/j.asoc.2023.110203
- Xiao, X., Li, C., Jiang, B., et al. (2022). Adaptive search strategy based chemical reaction optimization scheme for task scheduling in discrete multiphysical coupling applications. Applied Soft Computing, 121, 108748. https://doi.org/10.1016/j.asoc.2022.108748
- Xue, Z., Yi, X., Feng, W., et al. (2024). Prediction and mapping of soil thickness in alpine canyon regions based on whale optimization algorithm optimized random forest: A case study of Baihetan Reservoir area in China. Computers & Geosciences, 191, 105667. https://doi.org/10.1016/j.cageo.2024.105667
- Zhang, H., Zhang, Y., Niu, Y., et al. (2024). A Grey wolf optimizer combined with Artificial fish swarm algorithm for engineering design problems. Ain Shams Engineering Journal, 15(7), 102797. https://doi.org/10.1016/j.asej.2024.102797
- Zhao, R., Wang, Y., Liu, C., et al. (2020). Selfish herd optimizer with levy-flight distribution strategy for global optimization problem. Physica A: Statistical Mechanics and Its Applications, 538, 122687. https://doi.org/10.1016/j.physa.2019.122687
DOI: https://doi.org/10.24294/jipd10934
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Author(s)
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.