# Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Review Scope and Methodology

- i.
- scope definition—described in the previous paragraphs;
- ii.
- keywords definition—extracted from recent literature (review and research papers) by a heuristic search;
- iii.
- structured search (data gathering)—bibliographic databases were searched using the keywords found in stage ii (in a generic format and considering both American and English spellings) and considering the date range 2011–2022;
- iv.
- data structuring and organizing—the sets of records resulting from the structured search were then combined, screened, and cleaned as explained below;
- v.
- search expansion (additional data gathering by backward/forward reference search)— works citing and works cited by the set of works resulting from stage iv were added, and the new set of records was then subject to structuring and organizing (stages iv and v were repeated several times until the set of records remained unchanged); and
- vi.
- bibliography classification and analysis—reported in the following sections.

- (“*machin*” or “production*” or “operation*”, “manufactur*” or “job-shop*” or “jobshop*” or “job shop*”, or “flexib*”)
- And (“schedul*” or “planning”)
- And (“optimization*” or “optimisation*” or “*heuristic*”)
- And (“energy*” or “sustainab*” or “tariff*” or “*carbon*” or “*green*”)

^{®}. To avoid missing relevant papers due to varying authors’ keyword choices, papers were also gathered through backward/forward reference search, including from the most recent review papers [2,3,4,5].

^{®}, respectively.

## 3. Literature Analysis

## 4. Features of the Papers on EEJSPs

#### 4.1. Shop Floor

#### 4.2. Strategies for Energy Efficiency

#### 4.3. Energy Efficiency Objective Functions

#### 4.4. Other Objective Functions

#### 4.5. Additional Scheduling Problems

## 5. Solution Approaches

#### 5.1. Heuristic Methods

#### 5.2. Metaheuristics

#### 5.3. Hybrid Metaheuristics

#### 5.4. Multi-Objective Algorithms

## 6. Problem Instances and Data Sets

_{EE}) and additional objective function(s) if considered (Obj

_{other}); the next two columns report the energy efficiency strategy (EE strategy) used (refer to Section 4.2) and the additional scheduling problems (Features) considered (refer to Section 4.5); lastly, the problem instances column provides information on the origin of the instances considered (original instance when adapted, “App” when based on a real-world application, and “Rnd Ins” when randomly generated).

## 7. Conclusions

^{®}in March 2022, the H-index for EEJSP papers is 35; however, only 6 papers published in or after 2019 are among the top 35 most highly cited papers. This may be a sign that the EEJPs research community is not fully aware of the rich literature on EEJSPs, specifically with regard to work published in the most recent years.

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ABC | Artificial bee colony |

ABO | African buffalo optimization. |

ACO | Ant colony optimization |

AHP | Analytical hierarchy process |

AMO | Animal migration optimization |

BA | Bees algorithm |

Bat | Bat optimization algorithm |

BBO | Biogeography-based optimization |

BOA | Bacterial foraging optimization algorithm |

BS | Batch scheduling |

BSA | Backtracking search algorithm |

${C}_{max}$ | Makespan |

CD | Crowding distance |

CP | Constraint programming |

CSO | Cat swarm optimization |

$CT$ | Total completion time of the jobs |

$CuS$ | Customer satisfaction |

DEA | Differential evolution algorithm |

Dens | Density estimator |

DMS | Distributed manufacturing scheduling |

DP | Dynamic programming |

DS | Dynamic sheduling |

E | Energy consumption |

EA | Evolutionary algorithm |

$EC$ | Energy cost |

EDA | Estimation of distribution algorithm |

EEJSP | Energy-efficient job shop scheduling problem |

EMA | Electromagnetism-like mechanism algorithm |

EVP | Energy variable price |

FA | Firefly algorithm |

FFO | Fruit fly optimization |

FJSP | Flexible job shop scheduling problem |

GA | Genetic algorithm |

GD | Generational distance |

GEP | Gene expression programming |

GP | Genetic programming |

GRASP | Greedy randomized adaptive search procedure |

GSO | Glow-worm swarm optimization |

GWO | Grey wolf optimization |

HSA | Harmony search algorithm |

HV | Hyper volume |

I/O | On/off |

ICA | Imperialist competitive algorithm |

$Id$ | Idle time energy consumption |

IGD | Inverted generational distance |

ILS | Iterated local search |

$In$ | Indirect energy consumption |

IoMT | Internet of manufacturing things |

JA | Jaya algorithm |

JPP | Job process planning |

JSP | Job shop schedulin problem |

$LC$ | Labour cost |

Lexic | Lexicographic |

Learn | Learning methods |

LOP | Layout optimization problem |

MA | Memetic algorithm |

MaS | Maintenance scheduling |

MBO | Migrating birds optimization |

MILP | Mixed-integer linear programming |

MIP | Mixed integer programming |

$ML$ | Total machine workload |

$\overline{ML}$ | Mean machine workload |

${ML}_{max}$ | Maximum machine workload |

moEA | Multi-objective EA |

moGA | Multi-objective GA |

moPSO | Multi-objective PSO |

MS | Machine speed |

N | Noise |

NDS | Non-dominated sets |

NPA | Nested partitions algorithm |

NSGAII | Non-dominated sorting GA II |

P | Processing energy consumption |

$PC$ | Total production cost |

$PP$ | Peak power consumption |

PSO | Particle swarm optimization |

Q | Quality |

QEA | Quantum evolution algorithm |

RD | Rescheduling disruptions |

$ReL$ | Reliability |

$RM$ | Raw material consumption |

RPD | Reference point direction |

S | Setup energy consumption |

SA | Simulated annealing |

SDST | Sequence-dependent setup time |

Simul | Simulation |

SPEAII | Strength Pareto evolutionary algorithm II |

Sum | Summation of the objective functions |

wSum | Summation of weighted objective functions |

Norm wSum | Summation of weighted normalized objective functions |

SFLA | Shuffled frog-leaping algorithm |

T | Total tardiness |

$\overline{T}$ | Mean tardiness |

$TC$ | Total carbon emission |

$Tr$ | Transportation energy consumption |

${T}_{cost}$ | Cost of total tardiness |

$TE$ | Total tardiness and earliness |

${T}_{max}$ | Maximum tardiness |

TrT | Transport Time |

TrS | Transport scheduling |

TS | Tabu search |

$wT$ | Total weighted tardiness |

$wTE$ | Total weighted tardiness and earliness |

VNS | Variable neighborhood search |

$WIP$ | Work in progress |

WOA | Whale optimization algorithm |

WoS | Worker scheduling |

WWO | Water wave optimization |

## Appendix A

Ref | Floor | Obj_{EE} | P | Id | S | T | In | Obj_{other} | MO App | Sol App | EE Strategy | Features |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2022 | ||||||||||||

[92] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | wSum | Learn+GP | TrS; DS | ||

[51] | JSP | $EC$ | ✓ | $TE$; $Rel$ | NDS+ CD | NSGAII+Simul | MS | MaS | ||||

[60] | JSP | $EC$ | ✓ | ✓ | ✓ | ✓ | FA | EVP; I/O | ||||

[55] | FJSP | E | ✓ | ✓ | ✓ | ✓ | GEP | I/O | ||||

[75] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$; $PC$ | EMA | ||||

[76] | FJSP | E | ✓ | ✓ | ✓ | ✓ | AMO | |||||

[53] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | MS | |||

[124] | FJSP | $TC$ | ✓ | ✓ | ${C}_{max}$; $RD$ | Ensemble deep forest | DS | |||||

[138] | JSP | E | ✓ | ✓ | $CuS$; $Util$ | Norm wSum | Simul | DS | ||||

[104] | JSP | E | ✓ | ✓ | ${C}_{max}$; T | NDS | moGA | |||||

[125] | JSP | E | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | ||||||

2021 | ||||||||||||

[50] | JSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; T | Fuzzy RPD | GA | MS | SDST | ||

[150] | FJSP | $TC$ | ✓ | ✓ | ${C}_{max}$; $ML$ | NDS+ RPD | NSGAIII | |||||

[89] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | LOP; TrS | |

[88] | FJSP | E; $EC$ | ✓ | ✓ | $PC$ | NDS+ CD | moPSO | TrS | ||||

[94] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$ | EDA+VNS | DMS; TrS | ||||

[87] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | GA+DEA | MaS; TrS | ||||

[120] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; N | NDS+ CD | ICA | WoS; TrT | |||

[95] | FJSP | $TC$ | ✓ | ✓ | ✓ | ${C}_{max}$; $PC$; Q | Fuzzy AHP | GA+TS | DMS; TrT | |||

[59] | JSP | E | ✓ | ✓ | ${C}_{max}$; $wTE$ | NDS+ RPD | NSGAIII | I/O | ||||

[81] | JSP | E | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | MA | MS | ||||

[112] | FJSP | $TC$ | ✓ | ${C}_{max}$; $PC$ | NDS+ CD | NSGAII+QEA | ||||||

[97] | FJSP | $TC$ | ✓ | ✓ | ✓ | ✓ | $PC$; ${C}_{max}$; $TE$ | NDS+ Dens | moGA | DMS; TrT; SDST | ||

[106] | FJSP | $EC$ | ✓ | ✓ | ${T}_{cost}$ | NDS+ CD | NSGAII | DS | ||||

[109] | FJSP | E | ✓ | ✓ | ${C}_{max}$; ${T}_{max}$ | NDS+ CD | NSGAII | MS | ||||

[110] | JSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; T; $LC$ | Fuzzy RPD | moEA | MS | WoS | ||

[135] | JSP | $EC$ | ✓ | $wT$ | NDS+ CD | NSGAII | EVP | |||||

[84] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$; T | NDS | ICA | MS | SDST; TrT | |

[119] | FJSP | E | ✓ | ✓ | ${C}_{max}$; $Erg$ | NDS+ RPD | NSGAIII | WoS | ||||

[83] | FJSP | E | ✓ | ✓ | ${C}_{max}$, $PC$ | NDS+ CD | NSGA-II+VNS | TrT | ||||

[85] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | NSGA-II+LS | TrT | ||

[90] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | wSum | SA | TrS | ||||

[67] | JSP | ✓ | ${C}_{max}$ | MIP | MS | |||||||

[77] | FJSP | $TC$ | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$, T | NDS+ CD | NSGA-II | JPP | ||

[91] | JSP | E | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS | GWO | TrS | |||

[117] | FJSP | E | ✓ | ${C}_{max}$; $FP$ | NDS+ CD | NSGA-II | ||||||

[74] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS | PSO+GA | |||

[107] | FJSP | E | ✓ | ✓ | $\overline{T}$ | NDS+ CD | GP | DS | ||||

[137] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$, ${ML}_{max}$ | Game theory | DS; DMS | ||||

[140] | JSP | E | ✓ | ✓ | ${C}_{max}$; $\overline{T}$ | Simul | DS; MaS; SDST | |||||

2020 | ||||||||||||

[93] | JSP | E | ✓ | ✓ | ${C}_{max}$ | NDS | moEA | MS | DMS | |||

[105] | JSP | E | ✓ | $wT$ | NDS+ CD | MA | MS | MaS | ||||

[98] | FJSP | E | ✓ | ✓ | ${C}_{max}$; ${ML}_{max}$ | NDS+ CD | NSGAII | DMS; TrT | ||||

[103] | JSP | $EC$ | ✓ | T; $RD$ | Norm wSum | GA | MS | DS | ||||

[57] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $RD$ | NDS+ CD | BSA | I/O | DS | ||

[123] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | wSum | PSO | DS | ||||

[86] | FJSP | $EC$ | ✓ | ✓ | ${T}_{cost}$ | Sum | PSO+LS; PSO+SA | LOP; TrT | ||||

[58] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | I/O | DS | |||

[73] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$; T; $PC$ | NDS+ CD | NSGAII | MaS; TrT | |

[111] | FJSP | $TC$ | ✓ | ✓ | ${C}_{max}$; $LC$ | NDS+ CD | MA | WoS | ||||

[70] | JSP | $PP$ | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | ||||||

[78] | FJSP | E | ✓ | ✓ | ✓ | ✓ | MBO | WoS | ||||

[96] | FJSP | E | ✓ | ✓ | ✓ | SFLA | DMS | |||||

[136] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $ML$ | wSum | Game theory | DS; JPP | |||

[61] | FJSP | $TC$ | ✓ | ✓ | ✓ | ${C}_{max}$; $\overline{ML}$ | NDS+ CD | ABC | MS; I/O | |||

[127] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $CuS$ | GA+AIA | |||||

[126] | JSP | E | ✓ | $wT$ | NDS+ CD | NSGAII | ||||||

2019 | ||||||||||||

[11] | FJSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | GA+SA+PSO | TrT | ||

[63] | FJSP | ✓ | ✓ | ${C}_{max}$; T | NDS+ CD | ICA+VNS | ||||||

[20] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | wSum | GA+GSO | TrT | ||||

[148] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | GWO | MS | ||||

[35] | FJSP | $EC$ | ✓ | ✓ | ✓ | ${C}_{max}$; $LC$; $ML$ | NDS+ RPD | NSGAIII | I/O; EVP | WoS; SDST | ||

[114] | JSP | $EC$ | ✓ | ✓ | $PC$ | Sum | WOA | MS | ||||

[115] | JSP | $EC$ | ✓ | $PC$ | Sum | Bat | MS | |||||

[101] | FJSP | $TC$ | ✓ | ✓ | ✓ | $CT$; $Vib$; N | NDS+ CD | NSGAII; NSGAIII | ||||

[108] | FJSP | E | ✓ | ✓ | ${T}_{max}$; ${C}_{max}$; $ML$ | NDS+ RPD | ICA | |||||

[100] | JSP | E | ✓ | ✓ | GEP | EVP | ||||||

[116] | JSP | $EC$ | ✓ | ✓ | $PC$; $CuS$ | Sum | GA | TrT | ||||

[37] | FJSP | $EC$ | ✓ | ✓ | $PC$; T | Fuzzy Sum | NSGAII | EVP | TrS; WoS | |||

[56] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | |||||

[27] | FJSP | E | ✓ | ✓ | ✓ | ✓ | MILP | I/O | ||||

[68] | JSP | $EC$ | ✓ | ✓ | MILP | EVP | ||||||

[141] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $PC$; Q | Simul | DS | ||||

[145] | JSP | $EC$ | ✓ | $PC$ | Sum | MILP | ||||||

2018 | ||||||||||||

[24] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $\#I/O$ | NDS+ CD | NSGAII | MS; I/O | |||

[9] | FJSP | $TC$ | ✓ | ✓ | T | Norm wSum | GA | |||||

[10] | FJSP | E | ✓ | ✓ | $PC$ | wSum | GA; GA+PSO | |||||

[13] | FJSP | E | ✓ | ${C}_{max}$; $LC$ | wSum rank | GA | WoS | |||||

[80] | JSP | $EC$ | ✓ | ✓ | ✓ | WOA | MS | |||||

[82] | FJSP | $EC$ | ✓ | ✓ | CSO | |||||||

[102] | JSP | $EC$ | ✓ | T | Sum | GWO | ||||||

[72] | JSP | E | ✓ | ✓ | GA | DS | ||||||

[113] | JSP | $TC$ | ✓ | ✓ | ✓ | $PC$; $CT$ | Norm wSum | GA | ||||

[38] | FJSP | $EC$ | ✓ | ✓ | $PC$; Q | wSum | GA+ACO | EVP | TrS | |||

[17] | FJSP | E | ✓ | ✓ | ${C}_{max}$; $ML$ | Multi-agent Sys | DS | |||||

2017 | ||||||||||||

[8] | FJSP | $EC$ | ✓ | $CT$; $Rel$ | Norm wSum | GA+SA | MaS | |||||

[31] | FJSP | E | ✓ | ✓ | $ML$ | NDS | SFLA | MS | ||||

[32] | FJSP | E | ✓ | ${C}_{max}$; N | Norm wSum | GA | MS | |||||

[29] | FJSP | E | ✓ | ✓ | ✓ | GEP | I/O | |||||

[18] | FJSP | $TC$ | ✓ | ✓ | ✓ | ${C}_{max}$ | NDS | FFO | TrT | |||

[79] | JSP | E | ✓ | ${C}_{max}$ | Norm wSum | GA+LS | MS | DS | ||||

[118] | JSP | E | ✓ | ${C}_{max}$; N | Norm wSum | GA | MS | |||||

[71] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $ML$ | Lexic | ILS | ||||

[36] | FJSP | $EC$ | ✓ | ✓ | ${C}_{max}$ | wSum | BBO | EVP; MS | ||||

[65] | JSP | ✓ | ${C}_{max}$ | GRASP | ||||||||

[64] | JSP | E | ✓ | ✓ | ✓ | ✓ | ${C}_{max}$ | wSum | GA | TrT | ||

[12] | FJSP | E | ✓ | ✓ | ✓ | ${C}_{max}$; $ML$ | Game theory | DS; SDST | ||||

[139] | JSP | E | ✓ | ${C}_{max}$ | Simul | MS | ||||||

[144] | JSP | $EC$ | ✓ | ✓ | $PC$ | - | MILP | MS | SDST; BS; Inventory | |||

2016 | ||||||||||||

[30] | JSP | E | ✓ | ✓ | $wT$ | NDS+ Dens | moGA+ILS | MS | ||||

[33] | JSP | E | ✓ | ${C}_{max}$ | Norm wSum | GA | MS | |||||

[25] | JSP | E | ✓ | ✓ | $wT$ | NDS+ CD | NSGAII | I/O | ||||

[21] | JSP | E | ✓ | ${C}_{max}$; $wT$ | GA | DS | ||||||

[22] | FJSP | E | ✓ | ${C}_{max}$; $PC$; Q; $RM$ | BA | |||||||

[147] | FJSP | E | ✓ | ${C}_{max}$ | NDS+ CD | NSGAII | ||||||

[66] | JSP | ✓ | ${C}_{max}$ | MIP | ||||||||

[142] | JSP | E | ✓ | ${C}_{max}$ | wSum | CP | MS | |||||

[143] | JSP | E | ✓ | ${C}_{max}$ | Norm wSum | GA | MS | |||||

2015 | ||||||||||||

[26] | JSP | E | ✓ | ✓ | ${C}_{max}$ | Dom + Dens | moGA; SPEAII | I/O | ||||

[7] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | Norm wSum | NPA | Tools | ||||

[14] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | GA+SA | JPP | |||||

[15] | FJSP | $TC$ | ✓ | ✓ | ${C}_{max}$ | Lexic | VNS | WoS | ||||

[16] | FJSP | $TC$ | ✓ | ✓ | ${C}_{max}$; $ML$; $WIP$ | NDS+ CD | NSGAII | TrT | ||||

[34] | JSP | ✓ | ✓ | ✓ | ${C}_{max}$ | GA+SA | MS | |||||

[62] | FJSP | E | ✓ | ✓ | HSA | I/O | BS; SDST | |||||

2014 | ||||||||||||

[23] | JSP | E | ✓ | $wT$ | NDS+ CD | NSGAII | ||||||

[19] | FJSP | E | ✓ | ${C}_{max}$; $PC$; Q | NDS+ CD | NSGAII | ||||||

[69] | JSP | $PP$ | ✓ | ${C}_{max}$; $wTE$ | wSum | TS | ||||||

[28] | FJSP | E | ✓ | ✓ | ${C}_{max}$ | Simul | I/O | DS |

## References

- U.S. Energy Information Administration. International Energy Outlook 2021; EIA: Washington, DC, USA, 2021.
- Para, J.; Del Ser, J.; Nebro, A.J. Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives. Appl. Sci.
**2022**, 12, 1491. [Google Scholar] [CrossRef] - Bansch, K.; Busse, J.; Meisel, F.; Rieck, J.; Scholz, S.; Volling, T.; Wichmann, M.G. Energy-aware decision support models in production environments: A systematic literature review. Comput. Ind. Eng.
**2021**, 159, 107456. [Google Scholar] [CrossRef] - Gao, K.; Huang, Y.; Sadollah, A.; Wang, L. A review of energy-efficient scheduling in intelligent production systems. Complex Intell. Syst.
**2020**, 6, 237–249. [Google Scholar] [CrossRef] [Green Version] - Akbar, M.; Irohara, T. Scheduling for sustainable manufacturing: A review. J. Clean. Prod.
**2018**, 205, 866–883. [Google Scholar] [CrossRef] - Gahm, C.; Denz, F.; Dirr, M.; Tuma, A. Energy-efficient scheduling in manufacturing companies: A review and research framework. Eur. J. Oper. Res.
**2016**, 248, 744–757. [Google Scholar] [CrossRef] - He, Y.; Li, Y.; Wu, T.; Sutherland, J.W. An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J. Clean. Prod.
**2015**, 87, 245–254. [Google Scholar] [CrossRef] - Mokhtari, H.; Hasani, A. An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Comput. Chem. Eng.
**2017**, 104, 339–352. [Google Scholar] [CrossRef] - Piroozfard, H.; Wong, K.Y.; Wong, W.P. Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour. Conserv. Recycl.
**2018**, 128, 267–283. [Google Scholar] [CrossRef] - Wang, H.; Jiang, Z.; Wang, Y.; Zhang, H.; Wang, Y. A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. J. Clean. Prod.
**2018**, 188, 575–588. [Google Scholar] [CrossRef] - Dai, M.; Tang, D.; Giret, A.; Salido, M.A. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot. Comput.-Integr. Manuf.
**2019**, 59, 143–157. [Google Scholar] [CrossRef] - Zhang, Y.; Wang, J.; Liu, Y. Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact. J. Clean. Prod.
**2017**, 167, 665–679. [Google Scholar] [CrossRef] - Gong, G.; Deng, Q.; Gong, X.; Liu, W.; Ren, Q. A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. J. Clean. Prod.
**2018**, 174, 560–576. [Google Scholar] [CrossRef] - Dai, M.; Tang, D.; Xu, Y.; Li, W. Energy-aware integrated process planning and scheduling for job shops. J. Eng. Manuf.
**2015**, 229, 13–36. [Google Scholar] [CrossRef] [Green Version] - Lei, D.; Guo, X. An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective. Int. J. Prod. Econ.
**2015**, 159, 296–303. [Google Scholar] [CrossRef] - Zhang, C.; Gu, P.; Jiang, P. Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.
**2015**, 229, 328–342. [Google Scholar] [CrossRef] - Wang, J.; Zhang, Y.; Liu, Y.; Wu, N. Multiagent and bargaining-game-based real-time scheduling for internet of things-enabled flexible job shop. IEEE Internet Things J.
**2018**, 6, 2518–2531. [Google Scholar] [CrossRef] [Green Version] - Liu, Q.; Zhan, M.; Chekem, F.O.; Shao, X.; Ying, B.; Sutherland, J.W. A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. J. Clean. Prod.
**2017**, 168, 668–678. [Google Scholar] [CrossRef] - Jiang, Z.; Le, Z.; E, M. Study on multi-objective flexible job-shop scheduling problem considering energy consumption. J. Ind. Eng. Manag.
**2014**, 7, 589–604. [Google Scholar] [CrossRef] [Green Version] - Liu, Z.; Guo, S.; Wang, L. Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. J. Clean. Prod.
**2019**, 211, 765–786. [Google Scholar] [CrossRef] - Zhang, L.; Li, X.; Gao, L.; Zhang, G. Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency. Int. J. Adv. Manuf. Technol.
**2016**, 87, 1387–1399. [Google Scholar] [CrossRef] - Xu, W.; Shao, L.; Yao, B.; Zhou, Z.; Pham, D.T. Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing. J. Manuf. Syst.
**2016**, 41, 86–101. [Google Scholar] [CrossRef] - Liu, Y.; Dong, H.; Lohse, N.; Petrovic, S.; Gindy, N. An investigation into minimising total energy consumption and total weighted tardiness in job shops. J. Clean. Prod.
**2014**, 65, 87–96. [Google Scholar] [CrossRef] - Wu, X.; Sun, Y. A green scheduling algorithm for flexible job shop with energy-saving measures. J. Clean. Prod.
**2018**, 172, 3249–3264. [Google Scholar] [CrossRef] - Liu, Y.; Dong, H.; Lohse, N.; Petrovic, S. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int. J. Prod. Econ.
**2016**, 179, 259–272. [Google Scholar] [CrossRef] [Green Version] - May, G.; Stahl, B.; Taisch, M.; Prabhu, V. Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int. J. Prod. Res.
**2015**, 53, 7071–7089. [Google Scholar] [CrossRef] - Meng, L.; Zhang, C.; Shao, X.; Ren, Y. MILP models for energy-aware flexible job shop scheduling problem. J. Clean. Prod.
**2019**, 210, 710–723. [Google Scholar] [CrossRef] - Pach, C.; Berger, T.; Sallez, Y.; Bonte, T.; Adam, E.; Trentesaux, D. Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields. Comput. Ind.
**2014**, 65, 434–448. [Google Scholar] [CrossRef] - Zhang, L.; Tang, Q.; Wu, Z.; Wang, F. Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy
**2017**, 138, 210–227. [Google Scholar] [CrossRef] - Zhang, R.; Chiong, R. Solving the energy-efficient job shop scheduling problem: A multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. J. Clean. Prod.
**2016**, 112, 3361–3375. [Google Scholar] [CrossRef] - Lei, D.; Zheng, Y.; Guo, X. A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption. Int. J. Prod. Res.
**2017**, 55, 3126–3140. [Google Scholar] [CrossRef] - Yin, L.; Li, X.; Gao, L.; Lu, C.; Zhang, Z. A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustain. Comput. Inform. Syst.
**2017**, 13, 15–30. [Google Scholar] [CrossRef] - Salido, M.A.; Escamilla, J.; Giret, A.; Barber, F. A genetic algorithm for energy-efficiency in job-shop scheduling. Int. J. Adv. Manuf. Technol.
**2016**, 85, 1303–1314. [Google Scholar] [CrossRef] - Tang, D.; Dai, M. Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chin. J. Mech. Eng.
**2015**, 28, 1048–1055. [Google Scholar] [CrossRef] - Gong, X.; De Pessemier, T.; Martens, L.; Joseph, W. Energy-and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation. J. Clean. Prod.
**2019**, 209, 1078–1094. [Google Scholar] [CrossRef] [Green Version] - Zhang, H.; Dai, Z.; Zhang, W.; Zhang, S.; Wang, Y.; Liu, R. A new energy-aware flexible job shop scheduling method using modified biogeography-based optimization. Math. Probl. Eng.
**2017**, 2017, 7249876. [Google Scholar] [CrossRef] [Green Version] - Far, M.H.; Haleh, H.; Saghaei, A. A fuzzy bi-objective flexible cell scheduling optimization model under green and energy-efficient strategy using Pareto-based algorithms: SATPSPGA, SANRGA, and NSGA-II. Int. J. Adv. Manuf. Technol.
**2019**, 105, 3853–3879. [Google Scholar] - Hemmati Far, M.; Haleh, H.; Saghaei, A. A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy. Sci. Iran.
**2018**, 25, 339–358. [Google Scholar] [CrossRef] [Green Version] - Garey, M.R.; Johnson, D.S.; Sethi, R. The complexity of flowshop and jobshop scheduling. Math. Oper. Res.
**1976**, 1, 117–129. [Google Scholar] [CrossRef] - Cui, H.; Zhou, K. Industrial power load scheduling considering demand response. J. Clean. Prod.
**2018**, 204, 447–460. [Google Scholar] [CrossRef] - Weitzel, T.; Glock, C.H. Energy management for stationary electric energy storage systems: A systematic literature review. Eur. J. Oper. Res.
**2018**, 264, 582–606. [Google Scholar] [CrossRef] - Rathor, S.K.; Saxena, D. Energy management system for smart grid: An overview and key issues. Int. J. Energy Res.
**2020**, 44, 4067–4109. [Google Scholar] [CrossRef] - Le Hesran, C.; Ladier, A.L.; Botta-Genoulaz, V.; Laforest, V. Operations scheduling for waste minimization: A review. J. Clean. Prod.
**2019**, 206, 211–226. [Google Scholar] [CrossRef] - Giret, A.; Trentesaux, D.; Prabhu, V. Sustainability in manufacturing operations scheduling: A state of the art review. J. Manuf. Syst.
**2015**, 37, 126–140. [Google Scholar] [CrossRef] - Garwood, T.L.; Hughes, B.R.; Oates, M.R.; O’Connor, D.; Hughes, R. A review of energy simulation tools for the manufacturing sector. Renew. Sustain. Energy Rev.
**2018**, 81, 895–911. [Google Scholar] [CrossRef] - Narciso, D.A.; Martins, F. Application of machine learning tools for energy efficiency in industry: A review. Energy Rep.
**2020**, 6, 1181–1199. [Google Scholar] [CrossRef] - Renna, P.; Materi, S. A literature review of energy efficiency and sustainability in manufacturing systems. Appl. Sci.
**2021**, 11, 7366. [Google Scholar] [CrossRef] - Waltersmann, L.; Kiemel, S.; Stuhlsatz, J.; Sauer, A.; Miehe, R. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review. Sustainability
**2021**, 13, 6689. [Google Scholar] [CrossRef] - Biel, K.; Glock, C.H. Systematic literature review of decision support models for energy-efficient production planning. Comput. Ind. Eng.
**2016**, 101, 243–259. [Google Scholar] [CrossRef] - He, L.; Chiong, R.; Li, W.; Dhakal, S.; Cao, Y.; Zhang, Y. Multiobjective Optimization of Energy-efficient Job-Shop Scheduling with Dynamic Reference Point-based Fuzzy Relative Entropy. IEEE Trans. Ind. Inform.
**2021**, 18, 600–610. [Google Scholar] [CrossRef] - Amelian, S.S.; Sajadi, S.M.; Navabakhsh, M.; Esmaelian, M. Multi-objective optimization for stochastic failure-prone job shop scheduling problem via hybrid of NSGA-II and simulation method. Expert Syst.
**2022**, 39, e12455. [Google Scholar] [CrossRef] - Applagate, D.; Cook, W. A computational study of the job-shop scheduling instance. ORSA J. Comput.
**1991**, 3, 49–51. [Google Scholar] - Wei, Z.; Liao, W.; Zhang, L. Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds. Expert Syst. Appl.
**2022**, 197, 116785. [Google Scholar] [CrossRef] - Homayouni, S.M.; Fontes, D.B.M.M. A MILP Model for Energy-Efficient Job Shop Scheduling Problem and Transport Resources. In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021, Proceedings of the IFIP International Conference on Advances in Production Management Systems, Nantes, France, 5–9 September 2021; Springer: Cham, Switzerland, 2021; pp. 378–386. [Google Scholar]
- Rakovitis, N.; Li, D.; Zhang, N.; Li, J.; Zhang, L.; Xiao, X. Novel approach to energy-efficient flexible job-shop scheduling problems. Energy
**2022**, 238, 121773. [Google Scholar] [CrossRef] - Zhang, Z.; Wu, L.; Peng, T.; Jia, S. An improved scheduling approach for minimizing total energy consumption and makespan in a flexible job shop environment. Sustainability
**2019**, 11, 179. [Google Scholar] [CrossRef] [Green Version] - Caldeira, R.H.; Gnanavelbabu, A.; Vaidyanathan, T. An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption. Comput. Ind. Eng.
**2020**, 149, 106863. [Google Scholar] [CrossRef] - Li, Y.; He, Y.; Wang, Y.; Tao, F.; Sutherland, J.W. An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns. J. Clean. Prod.
**2020**, 254, 120009. [Google Scholar] [CrossRef] - Wei, H.; Li, S.; Quan, H.; Liu, D.; Rao, S.; Li, C.; Hu, J. Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling. IEEE Access
**2021**, 9, 54542–54557. [Google Scholar] [CrossRef] - Xu, E.; Li, Y.; Liu, Y.; Du, J.; Gao, X. Energy saving scheduling strategy for job shop under TOU and tiered electricity price. Alex. Eng. J.
**2022**, 61, 459–467. [Google Scholar] [CrossRef] - Li, Y.; Huang, W.; Wu, R.; Guo, K. An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem. Appl. Soft Comput.
**2020**, 95, 106544. [Google Scholar] [CrossRef] - Garcia-Santiago, C.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S. A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources. Eng. Optim.
**2015**, 47, 1481–1496. [Google Scholar] [CrossRef] - Lei, D.; Li, M.; Wang, L. A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold. IEEE Trans. Cybern.
**2018**, 49, 1097–1109. [Google Scholar] [CrossRef] - Xu, J.; Wang, L. A feedback control method for addressing the production scheduling problem by considering energy consumption and makespan. Sustainability
**2017**, 9, 1185. [Google Scholar] [CrossRef] [Green Version] - Kemmoe, S.; Lamy, D.; Tchernev, N. Job-shop like manufacturing system with variable power threshold and operations with power requirements. Int. J. Prod. Res.
**2017**, 55, 6011–6032. [Google Scholar] [CrossRef] - Samukawa, T.; Suwa, H. An optimization of energy-efficiency in machining manufacturing systems based on a framework of multi-mode RCPSP. Int. J. Autom. Technol.
**2016**, 10, 985–992. [Google Scholar] [CrossRef] - Carlucci, D.; Renna, P.; Materi, S. A Job-Shop Scheduling Decision-Making Model for Sustainable Production Planning with Power Constraint. IEEE Trans. Eng. Manag.
**2021**, 1–10. [Google Scholar] [CrossRef] - Masmoudi, O.; Delorme, X.; Gianessi, P. Job-shop scheduling problem with energy consideration. Int. J. Prod. Econ.
**2019**, 216, 12–22. [Google Scholar] [CrossRef] - Ichoua, S.; Pechmann, A. Production scheduling for sustainable manufacturing systems. Key Eng. Mater.
**2014**, 572, 235–238. [Google Scholar] [CrossRef] - Gondran, M.; Kemmoe, S.; Lamy, D.; Tchernev, N. Bi-objective optimisation approaches to Job-shop problem with power requirements. Expert Syst. Appl.
**2020**, 162, 113753. [Google Scholar] [CrossRef] - Plitsos, S.; Repoussis, P.P.; Mourtos, I.; Tarantilis, C.D. Energy-aware decision support for production scheduling. Decis. Support Syst.
**2017**, 93, 88–97. [Google Scholar] [CrossRef] - Feng, Y.; Wang, Q.; Gao, Y.; Cheng, J.; Tan, J. Energy-efficient job-shop dynamic scheduling system based on the cyber-physical energy-monitoring system. IEEE Access
**2018**, 6, 52238–52247. [Google Scholar] [CrossRef] - An, Y.; Chen, X.; Zhang, J.; Li, Y. A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption. J. Clean. Prod.
**2020**, 268, 121540. [Google Scholar] [CrossRef] - Ren, W.; Wen, J.; Yan, Y.; Hu, Y.; Guan, Y.; Li, J. Multi-objective optimisation for energy-aware flexible job-shop scheduling problem with assembly operations. Int. J. Prod. Res.
**2021**, 59, 7216–7231. [Google Scholar] [CrossRef] - Qu, M.; Zuo, Y.; Xiang, F.; Tao, F. An improved electromagnetism-like mechanism algorithm for energy-aware many-objective flexible job shop scheduling. Int. J. Adv. Manuf. Technol.
**2022**, 119, 4265–4275. [Google Scholar] [CrossRef] - Jiang, T.; Zhu, H.; Liu, L.; Gong, Q. Energy-conscious flexible job shop scheduling problem considering transportation time and deterioration effect simultaneously. Sustain. Comput. Inform. Syst.
**2022**, 35, 100680. [Google Scholar] [CrossRef] - Wen, X.; Wang, K.; Li, H.; Sun, H.; Wang, H.; Jin, L. A two-stage solution method based on NSGA-II for Green Multi-Objective integrated process planning and scheduling in a battery packaging machinery workshop. Swarm Evol. Comput.
**2021**, 61, 100820. [Google Scholar] [CrossRef] - Li, H.; Zhu, H.; Jiang, T. Modified migrating birds optimization for energy-aware flexible job shop scheduling problem. Algorithms
**2020**, 13, 44. [Google Scholar] [CrossRef] [Green Version] - Salido, M.A.; Escamilla, J.; Barber, F.; Giret, A. Rescheduling in job-shop problems for sustainable manufacturing systems. J. Clean. Prod.
**2017**, 162, S121–S132. [Google Scholar] [CrossRef] [Green Version] - Jiang, T.; Zhang, C.; Zhu, H.; Gu, J.; Deng, G. Energy-efficient scheduling for a job shop using an improved whale optimization algorithm. Mathematics
**2018**, 6, 220. [Google Scholar] [CrossRef] [Green Version] - Lu, C.; Zhang, B.; Gao, L.; Yi, J.; Mou, J. A Knowledge-Based Multiobjective Memetic Algorithm for Green Job Shop Scheduling with Variable Machining Speeds. IEEE Syst. J.
**2021**, 16, 844–855. [Google Scholar] [CrossRef] - Jiang, T.; Deng, G. Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption. IEEE Access
**2018**, 6, 46346–46355. [Google Scholar] [CrossRef] - Han, Y.; Chen, X.; Xu, M.; An, Y.; Gu, F.; Ball, A.D. A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.
**2021**, 16, 844–855. [Google Scholar] [CrossRef] - Li, M.; Lei, D. An imperialist competitive algorithm with feedback for energy-efficient flexible job shop scheduling with transportation and sequence-dependent setup times. Eng. Appl. Artif. Intell.
**2021**, 103, 104307. [Google Scholar] [CrossRef] - Zhang, H.; Xu, G.; Pan, R.; Ge, H. A novel heuristic method for the energy-efficient flexible job-shop scheduling problem with sequence-dependent set-up and transportation time. Eng. Optim.
**2021**, 15, 1–22. [Google Scholar] [CrossRef] - Ebrahimi, A.; Jeon, H.W.; Lee, S.; Wang, C. Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms. Comput. Ind. Eng.
**2020**, 141, 106295. [Google Scholar] [CrossRef] - Wang, H.; Sheng, B.; Lu, Q.; Yin, X.; Zhao, F.; Lu, X.; Luo, R.; Fu, G. A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes. Soft Comput.
**2021**, 25, 2863–2889. [Google Scholar] [CrossRef] - Barak, S.; Moghdani, R.; Maghsoudlou, H. Energy-efficient multi-objective flexible manufacturing scheduling. J. Clean. Prod.
**2021**, 283, 124610. [Google Scholar] [CrossRef] - Li, H.; Duan, J.; Zhang, Q. Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing. Trans. Inst. Meas. Control
**2021**, 43, 579–596. [Google Scholar] [CrossRef] - Li, J.Q.; Du, Y.; Gao, K.Z.; Duan, P.Y.; Gong, D.W.; Pan, Q.K.; Suganthan, P. A hybrid iterated greedy algorithm for a crane transportation flexible job shop problem. IEEE Trans. Autom. Sci. Eng.
**2021**, 1–18. [Google Scholar] [CrossRef] - Zhou, B.; Lei, Y. Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem. Appl. Soft Comput.
**2021**, 111, 107717. [Google Scholar] [CrossRef] - Li, Y.; Gu, W.; Yuan, M.; Tang, Y. Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network. Robot. Comput.-Integr. Manuf.
**2022**, 74, 102283. [Google Scholar] [CrossRef] - Jiang, E.D.; Wang, L.; Peng, Z.P. Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm Evol. Comput.
**2020**, 58, 100745. [Google Scholar] [CrossRef] - Du, Y.; Li, J.q.; Luo, C.; Meng, L.l. A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm Evol. Comput.
**2021**, 62, 100861. [Google Scholar] [CrossRef] - Xu, W.; Hu, Y.; Luo, W.; Wang, L.; Wu, R. A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Comput. Ind. Eng.
**2021**, 157, 107318. [Google Scholar] [CrossRef] - Meng, L.; Ren, Y.; Zhang, B.; Li, J.Q.; Sang, H.; Zhang, C. MILP Modeling and Optimization of Energy-Efficient Distributed Flexible Job Shop Scheduling Problem. IEEE Access
**2020**, 8, 191191–191203. [Google Scholar] [CrossRef] - Liu, Q.; Gui, Z.; Xiong, S.; Zhan, M. A principal component analysis dominance mechanism based many-objective scheduling optimization. Appl. Soft Comput.
**2021**, 113, 107931. [Google Scholar] [CrossRef] - Luo, Q.; Deng, Q.; Gong, G.; Zhang, L.; Han, W.; Li, K. An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers. Expert Syst. Appl.
**2020**, 160, 113721. [Google Scholar] [CrossRef] - Meng, L.; Zhang, C.; Zhang, B.; Ren, Y. Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access
**2019**, 7, 68043–68059. [Google Scholar] [CrossRef] - Zhang, L.; Li, Z.; Królczyk, G.; Wu, D.; Tang, Q. Mathematical modeling and multi-attribute rule mining for energy efficient job-shop scheduling. J. Clean. Prod.
**2019**, 241, 118289. [Google Scholar] [CrossRef] - Coca, G.; Castrillón, O.D.; Ruiz, S.; Mateo-Sanz, J.M.; Jiménez, L. Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems. J. Clean. Prod.
**2019**, 209, 146–168. [Google Scholar] [CrossRef] - Jiang, T.; Zhang, C.; Zhu, H.; Deng, G. Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode. Math. Probl. Eng.
**2018**, 2018, 8574892. [Google Scholar] [CrossRef] [Green Version] - Luo, J.; El Baz, D.; Xue, R.; Hu, J. Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm. Future Gener. Comput. Syst.
**2020**, 108, 119–134. [Google Scholar] [CrossRef] - Pan, Z.; Lei, D.; Wang, L. A Bi-Population Evolutionary Algorithm with Feedback for Energy-Efficient Fuzzy Flexible Job Shop Scheduling. IEEE Trans. Syst. Man Cybern. Syst.
**2021**, 1–13. [Google Scholar] [CrossRef] - Abedi, M.; Chiong, R.; Noman, N.; Zhang, R. A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines. Expert Syst. Appl.
**2020**, 157, 113348. [Google Scholar] [CrossRef] - Ayyoubzadeh, B.; Ebrahimnejad, S.; Bashiri, M.; Baradaran, V.; Hosseini, S.M.H. Modelling and an improved NSGA-II algorithm for sustainable manufacturing systems with energy conservation under environmental uncertainties: A case study. Int. J. Sustain. Eng.
**2021**, 14, 255–279. [Google Scholar] [CrossRef] - Xu, B.; Mei, Y.; Wang, Y.; Ji, Z.; Zhang, M. Genetic programming with delayed routing for multiobjective dynamic flexible job shop scheduling. Evol. Comput.
**2021**, 29, 75–105. [Google Scholar] [CrossRef] - Li, M.; Lei, D.; Xiong, H. An imperialist competitive algorithm with the diversified operators for many-objective scheduling in flexible job shop. IEEE Access
**2019**, 7, 29553–29562. [Google Scholar] [CrossRef] - Ahangar, N.K.; Khalili, M.; Tayebi, H. The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption. Teh. Glas.
**2021**, 15, 76–83. [Google Scholar] [CrossRef] - Li, W.; He, L.; Cao, Y. Many-Objective Evolutionary Algorithm with Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling with Limited Workers. IEEE Trans. Cybern.
**2021**, 1–14. [Google Scholar] [CrossRef] [PubMed] - Zhu, H.; Deng, Q.; Zhang, L.; Hu, X.; Lin, W. Low carbon flexible job shop scheduling problem considering worker learning using a memetic algorithm. Optim. Eng.
**2020**, 21, 1691–1716. [Google Scholar] [CrossRef] - Ning, T.; Huang, Y. Low carbon emission management for flexible job shop scheduling: A study case in China. J. Ambient Intell. Humaniz. Comput.
**2021**. [Google Scholar] [CrossRef] - Liao, W.; Wang, T. Promoting green and sustainability: A multi-objective optimization method for the job-shop scheduling problem. Sustainability
**2018**, 10, 4205. [Google Scholar] [CrossRef] [Green Version] - Jiang, T.; Zhang, C.; Sun, Q.M. Green job shop scheduling problem with discrete whale optimization algorithm. IEEE Access
**2019**, 7, 43153–43166. [Google Scholar] [CrossRef] - Lu, Y.; Jiang, T. Bi-population based discrete bat algorithm for the low-carbon job shop scheduling problem. IEEE Access
**2019**, 7, 14513–14522. [Google Scholar] [CrossRef] - Liao, W.; Wang, T. A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission. Sustainability
**2019**, 11, 2781. [Google Scholar] [CrossRef] [Green Version] - Sui, Z.; Li, X.; Yang, J.; Liu, J. Data-driven fault-aware multi-objective optimization for flexible job-shop scheduling problem. In Artificial Intelligence in China; Springer: Berlin/Heidelberg, Germany, 2021; pp. 261–269. [Google Scholar]
- Yin, L.; Li, X.; Gao, L.; Lu, C.; Zhang, Z. Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm. Adv. Mech. Eng.
**2017**, 9, 1687814017695959. [Google Scholar] [CrossRef] [Green Version] - Hongyu, L.; Xiuli, W. A survival duration-guided NSGA-III for sustainable flexible job shop scheduling problem considering dual resources. IET Collab. Intell. Manuf.
**2021**, 3, 119–130. [Google Scholar] [CrossRef] - Peng, Z.; Zhang, H.; Tang, H.; Feng, Y.; Yin, W. Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect. J. Intell. Manuf.
**2021**, 1–22. [Google Scholar] [CrossRef] - Lu, C.; Gao, L.; Gong, W.; Hu, C.; Yan, X.; Li, X. Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm. Swarm Evol. Comput.
**2021**, 60, 100803. [Google Scholar] [CrossRef] - Fathollahi-Fard, A.M.; Woodward, L.; Akhrif, O. Sustainable distributed permutation flow-shop scheduling model based on a triple bottom line concept. J. Ind. Inf. Integr.
**2021**, 24, 100233. [Google Scholar] [CrossRef] - Nouiri, M.; Bekrar, A.; Trentesaux, D. An energy-efficient scheduling and rescheduling method for production and logistics systems. Int. J. Prod. Res.
**2020**, 58, 3263–3283. [Google Scholar] [CrossRef] - Zhou, G.; Chen, Z.; Zhang, C.; Chang, F. An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance. J. Clean. Prod.
**2022**, 337, 130541. [Google Scholar] [CrossRef] - Afsar, S.; Palacios, J.J.; Puente, J.; Vela, C.R.; González-Rodríguez, I. Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times. Swarm Evol. Comput.
**2022**, 68, 101016. [Google Scholar] [CrossRef] - González-Rodríguez, I.; Puente, J.; Palacios, J.J.; Vela, C.R. Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problems. Soft Comput.
**2020**, 24, 16291–16302. [Google Scholar] [CrossRef] - Shi, D.; Zhang, B.; Li, Y. A multi-objective flexible job-shop scheduling model based on fuzzy theory and immune genetic algorithm. Int. J. Simul. Model.
**2020**, 19, 123–133. [Google Scholar] [CrossRef] - Yuan, G.; Yang, Y.; Tian, G.; Fathollahi-Fard, A.M. Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm. Environ. Sci. Pollut. Res.
**2022**. [Google Scholar] [CrossRef] - Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P. Optimization by simulated annealing. Science
**1983**, 220, 671–680. [Google Scholar] [CrossRef] - Lourenço, H.R.; Martin, O.C.; Stützle, T. Iterated local search. In Handbook of Metaheuristics; Springer: Berlin/Heidelberg, Germany, 2003; pp. 320–353. [Google Scholar]
- Mladenović, N.; Hansen, P. Variable neighborhood search. Comput. Oper. Res.
**1997**, 24, 1097–1100. [Google Scholar] [CrossRef] - Glover, F. Tabu search—Part I. ORSA J. Comput.
**1989**, 1, 190–206. [Google Scholar] [CrossRef] [Green Version] - Goldberg, D.E.; Holland, J.H. Genetic algorithms and machine learning. In Proceedings of the 6th Annual Conference on Computational Learning Theory, Santa Cruz, CA, USA, 26–28 July 1988. [Google Scholar]
- Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, Australia, 27 November–1 December 1995; Volume 4, pp. 1942–1948. [Google Scholar]
- Kurniawan, B.; Song, W.; Weng, W.; Fujimura, S. Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs. Evol. Intell.
**2021**, 14, 1581–1595. [Google Scholar] [CrossRef] - Wang, J.; Yang, J.; Zhang, Y.; Ren, S.; Liu, Y. Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. J. Clean. Prod.
**2020**, 247, 119093. [Google Scholar] [CrossRef] - Wang, J.; Liu, Y.; Ren, S.; Wang, C.; Wang, W. Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop. J. Clean. Prod.
**2021**, 293, 126093. [Google Scholar] [CrossRef] - Cai, L.; Li, W.; Luo, Y.; He, L. Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment. Int. J. Prod. Res.
**2022**. [Google Scholar] [CrossRef] - Raileanu, S.; Anton, F.; Iatan, A.; Borangiu, T.; Anton, S.; Morariu, O. Resource scheduling based on energy consumption for sustainable manufacturing. J. Intell. Manuf.
**2017**, 28, 1519–1530. [Google Scholar] [CrossRef] - Gupta, S.; Jain, A. Assessing the effect of reliability-based maintenance approach in job shop scheduling with setup time and energy consideration using simulation: A simulation study. Smart Sci.
**2021**, 9, 283–304. [Google Scholar] [CrossRef] - Wang, H. Manufacturing workshop multi-objective dynamic scheduling problem and model establishment. Acad. J. Manuf. Eng.
**2019**, 17, 92–97. [Google Scholar] - Salido, M.A.; Escamilla, J.; Barber, F.; Giret, A.; Tang, D.; Dai, M. Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems. Artif. Intell. Eng. Des. Anal. Manuf.
**2016**, 30, 300–312. [Google Scholar] [CrossRef] - Escamilla, J.; Salido, M.A.; Giret, A.; Barber, F. A metaheuristic technique for energy-efficiency in job-shop scheduling. Knowl. Eng. Rev.
**2016**, 31, 475–485. [Google Scholar] [CrossRef] - Giglio, D.; Paolucci, M.; Roshani, A. Integrated lot sizing and energy-efficient job shop scheduling problem in manufacturing/remanufacturing systems. J. Clean. Prod.
**2017**, 148, 624–641. [Google Scholar] [CrossRef] - Hassani, Z.I.M.; Barkany, A.E.; Abbassi, I.E.; Jabri, A.; Darcherif, A.M. New model of planning and scheduling for job-shop production system with energy consideration. Manag. Prod. Eng. Rev.
**2019**, 10, 89–97. [Google Scholar] - Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput.
**2002**, 6, 182–197. [Google Scholar] [CrossRef] [Green Version] - Yang, X.; Zeng, Z.; Wang, R.; Sun, X. Bi-objective flexible job-shop scheduling problem considering energy consumption under stochastic processing times. PLoS ONE
**2016**, 11, e0167427. [Google Scholar] [CrossRef] [Green Version] - Luo, S.; Zhang, L.; Fan, Y. Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J. Clean. Prod.
**2019**, 234, 1365–1384. [Google Scholar] [CrossRef] - Deb, K.; Jain, H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evol. Comput.
**2013**, 18, 577–601. [Google Scholar] [CrossRef] - Sun, X.; Wang, Y.; Kang, H.; Shen, Y.; Chen, Q.; Wang, D. Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem. Processes
**2021**, 9, 62. [Google Scholar] [CrossRef] - Adams, J.; Balas, E.; Zawack, D. The shifting bottleneck procedure for job shop scheduling. Manag. Sci.
**1988**, 34, 391–401. [Google Scholar] [CrossRef] - Fisher, H.; Thompson, G.L. Probabilistic learning combinations of local job-shop scheduling rules. In Industrial Scheduling; Prentice Hall: Hoboken, NJ, USA, 1963; pp. 225–251. [Google Scholar]
- Lawrence, S. Resouce Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques; Supplement; Graduate School of Industrial Administration, Carnegie-Mellon University: Pittsburgh, PA, USA, 1984. [Google Scholar]
- Brandimarte, P. Routing and scheduling in a flexible job shop by tabu search. Ann. Oper. Res.
**1993**, 41, 157–183. [Google Scholar] [CrossRef] - Dauzère-Pérès, S.; Paulli, J. An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann. Oper. Res.
**1997**, 70, 281–306. [Google Scholar] [CrossRef] - Kacem, I.; Hammadi, S.; Borne, P. Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev.
**2002**, 32, 1–13. [Google Scholar] [CrossRef] - Hurink, J.; Jurisch, B.; Thole, M. Tabu search for the job-shop scheduling problem with multi-purpose machines. Oper.-Res.-Spektrum
**1994**, 15, 205–215. [Google Scholar] [CrossRef] [Green Version] - Essafi, I.; Mati, Y.; Dauzère-Pérès, S. A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Comput. Oper. Res.
**2008**, 35, 2599–2616. [Google Scholar] [CrossRef]

**Figure 1.**Number of publications, per year, on energy-efficient scheduling problems in job shop manufacturing systems. (Early view publications were assigned to 2022.)

**Figure 4.**Number of JSP and FJSP publications incorporating energy-efficient strategies (MS—machine speed adjustment, I/O—turn-on and turn-off status, EVP—energy variable price).

**Figure 5.**Paper classification regarding energy efficiency objective functions and their components for JSPs and FSJPs. (The areas are not to scale.)

**Figure 6.**Number of papers published, per year, that include each of the five energy consumption types.

**Figure 7.**Metaheuristics proposed for EEJSPs and number of papers proposing them (including only those proposed in two or more papers). (The list of abbreviations provides the full name of every algorithm.)

**Figure 8.**Multi-objective solution methods: ranking and sorting mechanisms for evaluating solutions under multiple objectives and scalarization methods to convert the several objectives into a single one (Sum—summation, wSum—weighted sum, Norm wSum—normalized wSum, Lexic—lexicographic).

**Table 1.**Publication source titles with at least three papers published between 2013 and 2022 (first quarter) on energy-efficient scheduling problems in job shop manufacturing systems.

Source Title | # Publications |
---|---|

Journal of Cleaner Production | 24 |

IEEE Access | 12 |

Computers and Industrial Engineering | 6 |

International Journal of Production Research | 6 |

Sustainability (Switzerland) | 6 |

Expert Systems with Applications | 4 |

IEEE Transactions on Automation Science and Engineering | 4 |

International Journal of Advanced Manufacturing Technology | 4 |

PIMB, Part B: Journal of Engineering Manufacture | 4 |

Swarm and Evolutionary Computation | 4 |

Applied Soft Computing | 3 |

International Journal of Production Economics | 3 |

International Journal of Simulation Modelling | 3 |

Journal of Intelligent & Fuzzy Systems | 3 |

Mathematical Problems in Engineering | 3 |

**Table 2.**Number of papers considering multi-objective functions combining non-energy-related functions (Obj

_{other}) with energy-related functions (Obj

_{EE}) (including only those considered in at least three papers).

Obj_{other} | Obj_{EE} | ||
---|---|---|---|

$\mathit{E}$ | $\mathit{EC}$ | $\mathit{TC}$ | |

${C}_{max}$ | 45 | 2 | 6 |

$wT$ | 6 | 2 | |

$PC$ | 3 | 5 | |

${C}_{max}$; T | 5 | 1 | |

${C}_{max}$; $ML$ | 3 | 3 | |

${C}_{max}$; $PC$ | 3 | 1 | |

${C}_{max}$; N | 3 | ||

${C}_{max}$; $RD$ | 2 | 1 | |

${C}_{max}$; $PC$; Q | 2 | 1 | |

${T}_{cost}$ | 3 | ||

T | 1 | 2 |

Ref. | Obj_{EE} | Obj_{other} | EE Strategy | Features | Problem Instances |
---|---|---|---|---|---|

JSP | |||||

[21] | E | ${C}_{max}$; $wT$ | DS | ft06 | |

[33] | E | ${C}_{max}$ | MS | Rnd Ins | |

[118] | E | ${C}_{max}$; N | MS | Rnd Ins | |

[64] | E | ${C}_{max}$ | TrT | Rnd Ins | |

[50] | E | ${C}_{max}$; T | MS | SDST | orb1~3; abz7~abz9; |

la26~28; la31~33 | |||||

[26] | E | ${C}_{max}$ | I/O | ft06, 10, 20 | |

[23,25] | E | $wT$ | I/O | ft10 | |

[30] | E | $wT$ | MS | Rnd Ins | |

[105] | E | $wT$ | MS | MaS | Rnd Ins |

[80] | $EC$ | MS | la01~35; ft6, 10, 20 | ||

[114] | $EC$ | $PC$ | MS | ft06, 10, 20; la01~la40 | |

[60] | $EC$ | TOU; I/O | Rnd Ins | ||

[116] | $EC$ | $PC$; $CuS$ | TrT | App | |

[51] | $EC$ | $TE$; $Rel$ | MS | MaS | Rnd Ins |

[70] | $PP$ | ${C}_{max}$ | la01~40 | ||

FJSP | |||||

[10] | E | $PC$ | App | ||

[56] | E | ${C}_{max}$ | App | ||

[19] | E | ${C}_{max}$; $PC$; Q | App | ||

[32] | E | ${C}_{max}$; N | Rnd Ins; App | ||

[22] | E | ${C}_{max}$; $PC$; Q; $RM$ | Kacem | ||

[57] | E | ${C}_{max}$, $RD$ | I/O | DS | Rnd Ins |

[24] | E | ${C}_{max}$, # I/O | MS; I/O | mk01~10 | |

[90] | E | ${C}_{max}$ | TrS | Rnd Ins | |

[11] | E | ${C}_{max}$ | TrT | Rnd Ins; App | |

[20] | E | ${C}_{max}$ | TrT | App | |

[98] | E | ${C}_{max}$, ${ML}_{max}$ | DMS; TrT | Hurink | |

[31] | E | $ML$ | MS | mk01~13; dp1~18 | |

[84] | E | ${C}_{max}$, T | MS | SDST; TrT | mk01~15; dp1~18 |

[89] | E | ${C}_{max}$ | LOP; TrS | App | |

[29] | E | I/O | Hurink; Rnd Ins | ||

[55] | E | I/O | Rnd Ins | ||

[7] | E | ${C}_{max}$ | Tools | App | |

[14] | E | ${C}_{max}$ | JPP | Rnd Ins | |

[13] | E | ${C}_{max}$, $LC$ | WoS | App | |

[15] | E | ${C}_{max}$ | WoS | mk01~12; dp1~12 | |

[18] | $TC$ | ${C}_{max}$ | TrT | App | |

[16] | $TC$ | ${C}_{max}$; $ML$; $WIP$ | TrT | Kacem | |

[97] | $TC$ | $PC$; ${C}_{max}$; $TE$ | DMS; TrT; SDST | Kacem | |

[106] | $EC$ | ${T}_{cost}$ | DS | App |

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**MDPI and ACS Style**

Fernandes, J.M.R.C.; Homayouni, S.M.; Fontes, D.B.M.M.
Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review. *Sustainability* **2022**, *14*, 6264.
https://doi.org/10.3390/su14106264

**AMA Style**

Fernandes JMRC, Homayouni SM, Fontes DBMM.
Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review. *Sustainability*. 2022; 14(10):6264.
https://doi.org/10.3390/su14106264

**Chicago/Turabian Style**

Fernandes, João M. R. C., Seyed Mahdi Homayouni, and Dalila B. M. M. Fontes.
2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review" *Sustainability* 14, no. 10: 6264.
https://doi.org/10.3390/su14106264