Optimization of the Job Shop Scheduling Problem in Industry 4.0

Abstract

The Industry 4.0 trend has brought significant transformation in the manufacturing process through digitalization. In Intelligent Manufacturing Systems (IMS) there is an increase in the complexity of scheduling jobs on machines. The scheduling aims to collect data through the support of novel and emerging technologies such as: inclusion of machine sensors, cloud computing, artificial intelligence, big data analytics, scheduling software, etc. In this paper, we present an example of open-source job shop scheduling software (LEKIN) to solve real-time engineering problems in a manufacturing company. With using optimization algorithms and scheduling software there is likely to be a reduction of production costs and minimization of the total order completion time (make span). A test problem is running to evaluate the difference between the implementation of Shifting Bottleneck Heuristic (SBH) and some dispatching rules, such as Earliest Due Date (EDD), First Come First Served (FCFS), and Shortest Processing Time (SPT). The evaluation criteria used were the make span and the total weighted tardiness. The results have shown that the SBH outstripped the dispatching rules.