Jul 2026· 2026 3rd World Conference on Computer and Information Security (WCCIS)· pp. 24-28· 0 citations· 33 references
Abstract
The Flexible Job Shop Scheduling Problem (FJSP) is a critical combinatorial optimization problem in modern manufacturing systems, characterized by multiple constraints, dynamic requirements, and NP-hard complexity. This paper presents an improved Ant Colony Optimization (ACO) algorithm specifically designed to address the FJSP by minimizing makespan. The proposed algorithm incorporates enhanced pheromone update mechanisms, dual-level solution encoding for operation sequencing and machine assignment, and adaptive search strategies. Experimental results on a benchmark problem with 4 jobs, 3 processes per job, and 6 machines demonstrate that the improved ACO achieves superior performance compared to traditional metaheuristics, reducing makespan by approximately 8.7% compared to genetic algorithms. The convergence analysis shows stable optimization behavior within 40 iterations, validating the effectiveness and practical applicability of the proposed approach.
Experimental results show that the improved algorithm achieves an optimal Makespan value of 190 in dynamic disturbance scenarios and exhibits strong robustness, providing an efficient and feasible solution for job shop scheduling in complex production environments.
Jianguo Du, Chengkun Li, Zijie Tang· ITM Web of Conferences· 0 citations
These findings demonstrate the effectiveness and adaptability of the proposed framework for energy-aware flexible job shop scheduling and show that the BLDMA framework achieves better non-dominated solution sets on the tested instances.
Jia-Yu Liu, Juin-Han Chen, Hai-Yang Liu et al.· International Journal of Ind...· 0 citations
A Adaptive Genetic Algorithm (AGA) is designed to solve the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions.
Ming-Yue Li, Li-Na Wang, Jun Wang et al.· Journal of Engineering, Proj...· 0 citations
The findings demonstrate how mathematical optimization and advanced metaheuristics can be combined with problem-specific structural insights to improve solution quality and computational efficiency in production and operations research.
This study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm, and proves that the hybridisation of metaheuristics would improve the solution quality.
M. K. Marichelvam, M. Geetha· Computers· 0 citations
An intelligent scheduling methodology for Reconfigurable Manufacturing Systems (RMS) that integrates Petri Net (PN) modeling with heuristic and metaheuristic optimization techniques that achieves both efficient scheduling and practical industrial applicability is proposed.
Salah Hammedi· 0 citations
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