Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

Abstract

In the context of Industry 4.0, the matrix production developed by KUKA robotics represents a revolutionary solution for flexible manufacturing systems. Because of the adaptable and flexible manufacturing and material handling solutions, the design and control of these processes require new models and methods, especially from a real-time control point of view. Within the frame of this article, a new real-time optimization algorithm for in-plant material supply of smart manufacturing is proposed. After a systematic literature review, this paper describes a possible structure of the in-plant supply in matrix production environment. The mathematical model of the mentioned matrix production system is defined. The optimization problem of the described model is an integrated routing and scheduling problem, which is an NP-hard problem. The integrated routing and scheduling problem are solved with a hybrid multi-phase black hole and flower pollination-based metaheuristic algorithm. The computational results focusing on clustering and routing problems validate the model and evaluate its performance. The case studies show that matrix production is a suitable solution for smart manufacturing.

Migration-Based Moth-Flame Optimization Algorithm

“…}, which is a finite set of unlucky moths, such that OM i (t) > OM i (t − 1). Hence, in this operator, the position of M i changes by considering a randomly generated moth (M r ) and M i represents the parents in the crossover formulated in Equations ( 8) and (9), where α is a random number in [0, 1]. The crossover produces two offspring, and the one with better fitness is selected and compared with other offspring to choose the best one, and the position of OM i (t + 1) is added to the guiding archive if it can dominate the OM i (t).…”

“…The guiding archive is still immature) Generating the next position M i (t + 1) using RM operator and Equations ( 8) and (9).…”

“…During past decades, optimization techniques have been developed widely to solve complex problems that emerged in different fields of science, such as engineering [1][2][3][4][5][6][7][8][9], clustering [10][11][12][13][14][15][16][17][18], feature selection [19][20][21][22][23][24][25][26][27][28], and task scheduling [29][30][31][32]. Such optimization problems mainly involve characteristics such as linear/non-linear constraints, nondifferentiable functions, and a substantial number of decision variables.…”

Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight

“…M ij ∩ M gz denotes the machine set that can process operation O ij and operation O gz at the same time. (6) There is a priority relationship between the various operations of the same job.…”

“…At the same time, affected by the deterioration of the climate and the greenhouse effect, society and the country have also put forward higher and higher requirements for the green production of enterprises [3,4]. The manufacturing industry, which accounts for half of the total carbon emissions, has also attracted the attention of society and governments at all levels [5,6]. Therefore, ensuring a high-efficiency production rhythm in production and processing while seeking to improve energy efficiency and reduce total carbon emissions has become the direction of the study efforts of experts and scholars [7,8].…”