Review of job shop scheduling research and its new perspectives under Industry 4.0

Abstract

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem – Analysis of Test Results

“…The classical [44] job shop scheduling problem is formulated as follows: Let us have a set of n jobs J1, ..., Jn, which have to be performed on m machines M1, ... , Mm.…”

Evaluation of Loading Bay Restrictions for the Installation of Offshore Wind Farms Using a Combination of Mixed-Integer Linear Programming and Model Predictive Control

“…As a result, a multitude of solution algorithms has been proposed, ranging from exact mathematical models to heuristic or metaheuristic approaches. Please refer to Zhang et al [28] for a concurrent review of solution approaches. Within the literature, there exist several classifications of sub-types of the JSSP, e.g., [28][29][30].…”

“…Please refer to Zhang et al [28] for a concurrent review of solution approaches. Within the literature, there exist several classifications of sub-types of the JSSP, e.g., [28][29][30]. Nevertheless, besides giving a broad list of possible classifications, Zhang et al propose four predominant sub-types for JSSP [28]:…”

“…If also considering transport operations by heavy-lift or feeder vessels, overlaps of performable operations exist, resulting in a so-called Partial Flexible JSSP. Please refer to Zhang et al [28] for more information on Partial and Complete Flexible JSSPs and a more detailed definition of the different sub-types.…”

Innovation Potentials and Pathways Merging AI, CPS, and IoT

“…Innovation advances in the areas of Cyber-Physical Systems (CPS) and Industry 4.0 in the production area [1][2][3][4][5], Physical Internet (PI) and Internet of Things (IoT) in the logistics and transportation area [6][7][8][9][10] as well as Artificial Intelligence (AI) in the informatics field [11][12][13][14][15][16] have brought about a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen, but when-sooner than later-and in which specific way.…”