A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

Abstract

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

“…Only one paper addressed learning tasks and, thus, reinforcement learning. From the literature review, it appears that papers dealing with reinforcement learning applications are more frequently developed for scheduling problems, robot collaboration, or supply chain problems [121][122][123]. However, as these papers neglect the aspect of resource efficiency, they are beyond the scope of this literature review and were not further analyzed.…”

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

“…The knowledgeable manufacturing system is an in-depth, intelligent manufacturing system with the main characteristics of self-adaptation, self-learning, self-evolution, selfreconstruction, self-training, and self-maintenance, emphasizing the mining, processing, and utilization of the production knowledge contained in the manufacturing system [15][16][17]. Knowledge-based production scheduling has gradually become the focus of research.…”

A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

“…Consequently, production mode of multi-variety and small-batch in manufacture has received widespread attention recently by most manufacturing firms. Meanwhile, flexible manufacturing 1,2 is developed to meet such challenges. As an application of group technology, cellular manufacturing system (CMS) 3,4 can handle production control in each cell independently, which outperforms traditional system in reducing production cost, production time and wip inventory.…”

“…Meanwhile, since FJSP is a more complicated NP-hard problem than job shop scheduling, this brings new challenge to FJCSP optimization. 45 The main contribution of this paper can be summarized as: (1) to better react to green manufacturing and requirement of production mode, a novel mathematical model is proposed for FJCSP by taking makespan, total cost, and total energy consumption into consideration; (2) to enhance local search ability of NSGA-II for solving complex FJCSP, a HVNS is designed to amend performance of algorithm; (3) to verify the validity of proposed algorithm, comparisons with six heuristic methods based on benchmark instances and another kind of method from literature is analyzed.…”