Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm

Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe. By combining domain knowledge of the specific optimization problem, the search efficiency and quality of meta-heuristic algorithms can be significantly improved, making it crucial to identify and summarize domain knowledge within the problem. In this paper, we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem (JSP). Firstly, this paper delves into the importance of domain knowledge in optimization algorithm design. After that, the development of different methods for the JSP are reviewed, and the domain knowledge in it for meta-heuristic algorithms is summarized and classified. Applications of this domain knowledge are analyzed, showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms. Finally, this paper analyzes the relationship among domain knowledge, optimization problems, and optimization algorithms, and points out the shortcomings of the existing research and puts forward research prospects. This paper comprehensively summarizes the domain knowledge in the JSP, and discusses the relationship between the optimization problems, optimization algorithms and domain knowledge, which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

Section: Applications Of Domain Knowledge In Metaheuristic Algorithmsmentioning

confidence: 80%

Section: Development Of Methods For Solving the Job-shop Scheduling P...mentioning

confidence: 99%

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Gui,

Li,

Zhang

et al. 2024

“…To improve the overall performance of the algorithm for its normalization process, this paper employs a normalization treatment for an SDR, the priority value of a SDR is unified in the range of 0-1 to facilitate the calculation of the algorithm, and it is applicable to large, medium, and small three scale models, it is shown in Eq. (7).…”

Section: Igpamentioning

confidence: 99%

“…Meng et al [6] addressed the Autonomous Guided Vehicles (AGV) constraint problem in the FJSP and propose an improved Genetic Algorithm (GA) to solve it. Long et al [7] combined the Q-learning algorithm with the artificial swarm algorithm to create a self-learning artificial swarm algorithm for the insertion of new jobs in the FJSP and demonstrate the effectiveness of the algorithm through example analysis. Li et al [8] applied a Hybrid Chemical Reaction Optimization (HCRO) algorithm to address the distributed FJSP, they develop a novel encoding-decoding method for Flexible Manufacturing Unit (FMU) and design an improved method of critical-FMU to improve the global and local search ability of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

Li,

Zhao,

Tang

et al. 2024

To obtain a suitable scheduling scheme in an effective time range, the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems (FJSP) with different scales, and Composite Dispatching Rules (CDRs) are applied to generate feasible solutions. Firstly, the binary tree coding method is adopted, and the constructed function set is normalized. Secondly, a CDR mining approach based on an Improved Genetic Programming Algorithm (IGPA) is designed. Two population initialization methods are introduced to enrich the initial population, and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm. At the same time, two individual mutation methods are introduced to improve the algorithm's local search ability, to achieve the balance between global search and local search. In addition, the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis. Finally, Deep Reinforcement Learning (DRL) is employed to solve the FJSP by incorporating the CDRs as the action set, the selection times are counted to further verify the superiority of CDRs.

“…For JSP with machine flexibility, Xing et al [12] introduced a knowledge-based Ant Colony Optimization (ACO) algorithm to effectively integrate population information with problem-specific knowledge, enabling a highly efficient spatial search for problem-solving compared to traditional algorithms. The particle swarm optimization, chemical reaction optimization, iterative greedy, and migrating birds optimization algorithms have exhibited good results for JSP and its variants [13][14][15][16] . Additionally, a combination of the reinforcement learning method and evolutionary algorithm has yielded exceptional outcomes.…”

Section: Introductionmentioning

confidence: 99%

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

Li,

Yan,

Huang

2024

With the advancement of the manufacturing industry, the investigation of the shop floor scheduling problem has gained increasing importance. The Job shop Scheduling Problem (JSP), as a fundamental scheduling problem, holds considerable theoretical research value. However, finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP. A co-operative-guided ant colony optimization algorithm with knowledge learning (namely KLCACO) is proposed to address this difficulty. This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge. A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions. A pheromone guidance mechanism, which is based on a collaborative machine strategy, is used to simplify information learning and the problem space by collaborating with different machine processing orders. Additionally, the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution, expanding the search space of the algorithm and accelerating its convergence. The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets, comprising 48 benchmark test cases in total. The effectiveness of the proposed algorithm in addressing JSPs is validated, demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.