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A Novel Metaheuristic Hybrid Parthenogenetic Algorithm for Job Shop Scheduling Problems: Applying Optimization Model
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3278372© Copyright 2023 The Authors. Metaheuristics are primarily developed to explore optimization techniques in many practice areas. Metaheuristics refer to computational procedures leading to finding optimal solutions to optimization problems. Due to the increasing number of optimization problems with large-scale data, there is an ongoing demand for metaheuristic algorithms and the development of new algorithms with more efficiencies and improved convergence speed implemented by a mathematical model. One of the most popular optimization problems is job shop scheduling problems. This paper develops a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) to optimize flexible job shop scheduling problems for single-machine and multi-machine job shops and a furnace model. This method is based on the principles of genetic algorithms, underlying the combinations of different types of selections, proposed ethnic GA, and hybrid parthenogenetic algorithm. In this paper, a parthenogenetic algorithm combined with ethnic selection GA is tested; the parthenogenetic algorithm version includes parthenogenetic operators: swap, reverse, and insert. The ethnic selection uses different selection operators such as stochastic, roulette, sexual, and aging; then, top individuals are selected from each procedure and combined to generate an ethnic population. The ethnic selection procedure is tested with the PGA types on a furnace model, single-machine job shops, and multi-machines with tardiness, earliness, and due date penalties. A comparison of obtained results of the established algorithm with other selection procedures indicated that the NMHPGA is achieving better objective functions with faster convergence speed.10.13039/501100007914-Brunel University Londo
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Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems
Data Availability Statement: The data that support the findings of this study are available from the corresponding author, M.A., upon reasonable request.Copyright © 2023 by the authors. Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the variations in production requirements. In this study, novel reinforcement learning (RL) models, including advanced Q-learning (QRL) and RL-based state–action–reward–state–action (SARSA) models, are proposed to enhance the scheduling performance of FJSPs, in order to reduce the total makespan. To more accurately depict the problem realities, two categories of simulated single-machine job shops and multi-machine job shops, as well as the scheduling of a furnace model, are used to compare the learning impact and performance of the novel RL models to other algorithms. FJSPs are challenging to resolve and are considered non-deterministic polynomial-time hardness (NP-hard) problems. Numerous algorithms have been used previously to solve FJSPs. However, because their key parameters cannot be effectively changed dynamically throughout the computation process, the effectiveness and quality of the solutions fail to meet production standards. Consequently, in this research, developed RL models are presented. The efficacy and benefits of the suggested SARSA method for solving FJSPs are shown by extensive computer testing and comparisons. As a result, this can be a competitive algorithm for FJSPs.There is not funding for this research. The APC was funded by Brunel University London