Article
A New Perspective for Solving Manufacturing Scheduling
Based Problems Respecting New Data Considerations
Mohammed A. Awad
1
and Hend M. Abd-Elaziz
2,
*
Citation: Awad, M.A.; Abd-Elaziz,
H.M. A New Perspective for Solving
Manufacturing Scheduling Based
Problems Respecting New Data
Considerations. Processes 2021, 9,
1700. https://doi.org/10.3390/
pr9101700
Academic Editor: Arkadiusz Gola
Received: 17 August 2021
Accepted: 18 September 2021
Published: 23 September 2021
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4.0/).
1
Design and Production Engineering Department, Ain-Shams University, 1 El Sarayat St., ABBASSEYA,
Al Waili, Cairo 11517, Egypt; Mohamed.Ahmed.Awad@eng.asu.edu.eg
2
Mechatronics Department, Badr University in Cairo, Cairo-Suez Road, Cairo 11829, Egypt
* Correspondence: Hend_m_ae@buc.edu.eg
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.
Keywords:
flexible job shop scheduling; heuristics; optimization; job shop scheduling; industry 4.0;
integrated process planning and scheduling
1. Introduction
Industry 4.0 (I4.0) has accustomed manufacturing to the digital age via Cyber Physical
Systems (CPS) and Digital Twins (DTs). CPS and DTs are two integrated approaches
of several intelligent tools that facilitate the use of data-driven approach in industry. A
system can have the ability to physically interact the environment and collect data to
virtually represent a complete description along system states progress. Such description
causes a state of awareness or smartness that empowers smart manufacturers to take
the place of conventional manufacturers [
1
]. Accordingly, the progress of sensor devices,
communication technologies and enabling intelligent techniques have evolved for the
sake of big data analytics [
2
]. Enabling techniques have been used in industry for more
than three decades. However, what makes a difference is the perspective of data that
empowers the intelligent techniques [
3
]. In a dynamic collaboration, CPS allow information
communication between systems/sub-systems aspects, where the real physical phase and
cyber phase are associated to fulfill the system awareness gap through data analysis [
4
,
5
].
While, DTs support integration between the dual states of data: static state and dynamic
state. Data states are beneficial in creating a virtual model or emphasizing interdependent
instances of included sub-systems, approaching a model-based system [
6
,
7
]. Briefly, both
the CPS and DTs are employed to achieve cyber-physical pervasive integration [5].
The motive behind the three previous industries is still applied in I4.0 but is influenced
by sustainability and adaptability. Sustainability is a requirement for the future, being one
of the pillars that accompanies value added activities [
8
,
9
]. The latter term, adaptability, is
Processes 2021, 9, 1700. https://doi.org/10.3390/pr9101700 https://www.mdpi.com/journal/processes
