Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

Abstract

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

Integrated Model of Production and Engineering Chains in Smart Manufacturing Technologies in Industry 4.0

“…Thus, a model can be defined as information created by someone for someone who will use that information for some purpose. According to some authors [15], a model has three main characteristics: it is based on an original, it reflects only a specific part of the original, and it must be repeatedly used instead of the original to achieve a certain cognitive goal.…”

Towards a Fuzzy Approach for Optimizing Single Machine Common Due Date Scheduling Problem under Uncertainty

“…Alharbi and El-Wahed Khalifa [26] introduced a flow shop scheduling problem with a pentagonal processing time of jobs to minimize the rental cost of the machine in compliance with the rental policy. Recently, an enormous number of researchers studied scheduling problems in different environments [27][28][29][30][31][32].…”

Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

“…An alternative approach is based on numerous meta-heuristic methods, such as genetic algorithms, evolution strategies, swarm optimization, and simulated annealing. The investigators leading this study possess extensive expertise in artificial intelligence algorithms, particularly emphasizing swarm intelligence and nature-inspired techniques, optimization methodologies, control systems, and fuzzy numbers [24][25][26][27][28][29][30][31][32][33][34][35]. This paper delves into the exploration of applying the bat algorithm to identify the optimal camera and motion sensor placement within a complex industrial environment.…”