Identifying the promising production planning and scheduling method for manufacturing in Industry 4.0: a literature review

Figures & data

Table 1. The keywords used in this research.

		Search 1	Search 2
Production planning and scheduling construct	TITLE	‘production planning’ OR ‘manufacturing planning’ OR ‘job scheduling’ OR ‘job shop scheduling’	‘production planning and scheduling’ OR ‘manufacturing operation*’
		AND
Integration construct	TITLE	‘integrat*’ OR ‘hierarchical’ OR ‘aggregat*’ OR ‘multiperiod’ OR ‘stage’
		AND
Uncertainty construct	All fields	‘uncertain*’ OR ‘real*time’
			AND
Industry 4.0 construct	All fields		‘smart’ OR ‘Industry 4.0’ OR ‘fourth industrial revolution’ OR ‘intelligent’ OR ‘cyber*physical’

Figure 1. The review processes.

Table 2. Summary of papers about production planning and scheduling integration.

Authors	Integration		Iteration		Models			Solving methods				Software
	HPP	MPP	Rolling	Feedback	MP	Fuzzy	Simulation	Algorithm	Heuristics	Metaheuristics	Other
(Ghazanfari & Murtagh, 2002)	√				GP							GAMS/MINOS
(Susara et al., 2003)		√	√		MINLP				LR			DICOPT
(Venkateswaran & Son, 2005)	√			√			√					AMPL/MINOS
(Dan & Ierapetritou, 2007)		√	√		MILP				LP relaxation
(Luo & Rong, 2009)		√	√		MILP		√					LINGO
(Rafiei & Rabbani, 2009)	√					√					ANP
(Kang & Choi, 2010)		√									MAS	EXASL
(Zukui & Ierapetritou, 2009)		√			MILP				LR			GAMS/CPLEX
(Monostori et al., 2010)		√			MILP		√	Branch and cut				XpressMP/CPLEX
(Torabi et al., 2010)	√				LP	√						LINGO
(Zanjani et al., 2010)		√			MSP							CPLEX/OPL
(Aghezzaf et al., 2011)	√			√	MILP							CPLEX/OPL
(Alfieri et al., 2012)		√			MSP							CPLEX
(Shah & Ierapetritou, 2012)		√			MILP				ALO-DQA			GAMS/CPLEX
(Rafiei et al., 2013)	√				MINLP					GA, SA, PSO		Microsoft Excel
(O’Reilly et al., 2015)	√
(Yan et al., 2015)		√		√	MINLP					GA		Matlab
(Altendorfer et al., 2016)	√				MILP		√					CPLEX, AnyLogic
(Menezes et al., 2016)	√				LP				Relax and fix, GRASP, RCH, maximal clique			CPLEX
(Dong et al., 2016)	√						√					Arena, Simio, Anylogic, ESPRIT
(Gyulai et al., 2017)		√			MIP		√				Regression	FICO Xpress
(Vogel et al., 2017)	√	√	√									CPLEX
(Wen et al., 2017)		√			CCP		√			GA	ANN	Matlab
(Aguirre & Papageorgiou, 2018)		√			MILP		√					CPLEX
(Arias-Bolzmann et al., 2018)	√					√						Microsoft Excel
(Asmussen et al., 2018)	√	√			MILP
(Zhang et al., 2019)		√	√		CCP	√	√					LINGO
(Han et al., 2020)		√		√		√				PSO		Matlab
(Santander et al., 2020)	√			√	LP, QP		√					Matlab, IPOPT, GAMS
(Matthias et al., 2020)		√					√					Simio
(Zhi-Min et al., 2021)		√			MILP					GA		C#
(Jiang & Yan, 2022)		√		√	MSP					GA, SA		Matlab

HPP: hierarchical production planning, MPP: monolithic production planning, GP: goal programming, LP: linear programming, MIP: mixed integer programming, MILP: mixed-integer linear programming, MINLP: mixed integer nonlinear programming, MSP: multi-stage stochastic programming, CCP: chance-constrained programming, QP: quadratic programming, ALO-DQA: augmented Lagrangian optimization and diagonal quadratic approximation, GRASP: a greedy randomized search procedure, RCH: route capacity reduction, ANP: analytic network process, GA: genetic algorithm, SA: simulated annealing, PSO: particle swarm optimization, MAS: multi-agent system, LR: Lagrangean relaxation, ANN: artificial neural network.

Figure 2. A production decision-making framework (Silver et al., 1998).

Table 3. Summary of papers about production planning and scheduling in the industry 4.0 context.

Authors	Focused areas	Impacting frontier Technologies	Proposed solutions
(Giordani et al., 2013)	Distributed manufacturing and RMS	Mobile robots	MAS
(Zhong et al., 2013, 2015)	Inconsistency between highly synchronized information flow and unstandardized decision-making procedures	RFID	RAPShell
(Mourtzis et al., 2015)	Distributed manufacturing	CNC machines	Metaheuristics and AI
(Holmstrom et al., 2017)	Sustainability	DDM	Implementation roadmap
(Jun et al., 2017)	Smart manufacturing by SME	IoT, cloud, and big data	AIOP
(Legat & Vogel-Heuser, 2017)	Field level automation	PLC control	HiTraP-AT
(Yaqiong & Lin, 2017)	Distributed manufacturing	RFID	Real-time modeling with RFID-based HTCPN and smart fuzzy engine
(Zangiacomi et al., 2017)	B2B	Mobile application	eApps
(Fatorachian & Kazemi, 2018)	System integration	Industrial internet, IoT, CPS, information network, software systems, cloud computing and big data analytics	Conceptual framework for operationalisation of Industry 4.0 In production process
(Bao et al., 2019)	Production management and process control	DT	Integrated manufacturing information model (product DT, process DT, and operation DT)
(Khakifirooz et al., 2019)	Operations control of manufacturing facilities of the semiconductor	CPS, IoT, cloud computing	Literature review from the OR&DS perspective
(Tan et al., 2019)	Shop-floor assembly process	Mobile robots, CPS	MAS reinforcement learning
(Cheng et al., 2020)	Distributed collaboration	Public cloud, private cloud, hybrid cloud	Hypernetwork and graph coloring based model
(Farooqui et al., 2020)	Data collection	EDA	EDA approach for detection of deviations in operation sequences
(Kumar et al., 2020)	Distributed operation planning	CPS, IoT, computer optimization techniques	MAS
(Park et al., 2020)	Operation procedures at work-center level	DT	Various service-composition-based technical functionalities
(Chunyang et al., 2020)	Shared manufacturing	CPS, blockchain	BSM framework
(D’Aniello et al., 2021)	Distributed manufacturing	CPS, AM	MAS in a CM system based on AM technologies
(Daqiang et al., 2021)	Planning, scheduling, execution and control	CPS, IoT, DT, cloud computing, and computer optimization techniques	GiMS with real-time task allocation and execution mechanisms
(Lee, 2021)	Smart manufacturing by SME	ICT	SEM
(Mohamad et al., 2022)	Operational efficiency	AI, auomatic machinery, IoT, ICT	Panel ARDL
(Pansare & Yadav, 2022, 2022)	RPO	CPS, IoT, AI, big data, vitual reality, holography, cloud manufacturing, autonomous robots, 3D scanning, 3D printing, biosensors, DT	PFAHP and CoCoSo
(Sharma et al., 2023)	Sustainable supply chain management	AM, robots, augmented reality, cloud computing, big data, RFID, AI, IoT	FAHP and PROMETHEE

RMS: reconfigurable manufacturing system, RFID: radio frequency identification, RAPShell: RFID-enabled real-time advanced production planning and scheduling shell, CNC: computer numerical control, DDM: direct digital manufacturing, AIOP: a cloud-based applications’ integration and operation platform, PLC: programmable logic controller, HiTraP-AT: hierarchical transition planner for automation technology, HTCPN: hierarchical time colored Petri net, B2B: business to business, eApp: engineering apps, OR&DS: operations research and data science, EDA: event driven architecture, BSM: blockchain-based shared manufacturing, AM: additive manufacturing, CM: cloud manufacturing, GiMS: graduation intelligent manufacturing system, ICT: information and communications technology, SEM: structural equation model, ARDL: autoregressive distributed lag, RPO: repurposing production operations, PFAHP: Pythagorean fuzzy analytical hierarchy process, CoCoSo: combined compromise solution, FAHP: fuzzy analytical hierarchy process, PROMETHEE: preference ranking organization method for enrichment evaluation.

Figure 3. The suggested production planning and scheduling system for Industry 4.0.

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Data availability statement

The authors confirm the data supporting the findings of this study are available within the article [and/or] its supplementary materials.