A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Abstract

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

“…Over time, variants of the JSSP have emerged, including the basic JSSP, the Dynamic Job Shop Scheduling Problem (DJSSP), and the Flexible Job Shop Scheduling Problem (FJSSP) [2]. The FJSSP introduces complexity by incorporating machine assignment as an additional decision level, thereby making it an NP-hard problem that necessitates challenging combinatorial optimization [2]- [4]. DJSSP involves dynamically adjusting the schedule plan in case of occurrence of a disruptive event.…”

“…Recent advancements have introduced specific measures of stability and robustness tailored to the FJSSP [1], [6]- [8]. The concept of real-time dynamic scheduling involves utilizing current data to make scheduling decisions [2]- [4]. Figure 1 illustrates the architecture and information flow of a generic real-time dynamic scheduler system within an Industry 4.0 based MES (MES 4.0).…”