# A review of energy-efficient scheduling in intelligent production systems

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## Abstract

Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

## Keywords

Production scheduling Energy efficiency Swarm intelligence Evolutionary algorithm## Introduction

Energy consumption is an important issue in current society. In last 40 years, the energy demand of the world has doubled and will double again in next 10 years [1]. In general, the industry is one of the primary consumers of energy. In 2018, industry accounted for approximately 25% of energy consumption by end use in the European Union [2]. The energy consumption of industrial fields is about 26.3% of estimated U.S. energy consumption of 2018 [3]. As energy-intensive fields, manufacturing industries consumed nearly a third of the global energy consumption of the world [4, 5]. In China, more than 56% of the total energy consumption is occupied in manufacturing sector attributed [6].

It is essential to reduce the manufacturing industry’s energy consumption and demand. The manufacturing industries play a key role to satisfy continuously growing of various goods as living standards increasing. It is unrealistic to reduce the energy supply for manufacturing industries directly, since energy is a non-substitutable production factor. In other words, it is limited to a certain extent to reduce energy consumption and to subject to the desired output simultaneously. Hence, how to improve energy efficiency or to reduce energy demands for the same output becomes a critical approach to achieve the purpose of reducing energy consumption and developing sustainably. It is a consensus in academic and business that “energy-efficiency gap” is a strong barrier which hinders energy-efficient manufacturing.

In manufacturing shops, the energy consumption is noticeable. In actual machining processes, machine tools stay in an idle state for the most of the time and consume about 80% energy with the idle state [7]. Machine tools have huge potential space for energy saving [8]. In general, scheduling is an effective approach to solve the issues about machine status and is an important decision-making process to decide which tasks to execute, when to execute them, and where to process them in which sequence. It is rarely considered as a suitable instrument to improve energy efficiency. In recent years, many researchers use scheduling approaches to improve energy efficiency in manufacturing industries and energy-efficient scheduling has been proved to be an effective way of reducing the energy consumption with none or little capital investment [9, 10]. Energy efficiency or energy consumption is considered as constraints or scheduling objectives, like makespan, machine workload, and due-date objectives.

Production scheduling has been proven as an NP-hard problem; hence, energy-efficiency scheduling is no exception. Traditional optimization approaches cannot solve large-scale scheduling problems with high efficiency, especially for some large-scale instances with real-time constraints. Therefore, swarm intelligence and evolutionary algorithms are employed for solving such problems as reported in many publications, since low computational time and high efficiency are the most important criteria [4, 5]. For large-scale cases with real-time constraints, swarm intelligence and evolutionary algorithms can obtain high-quality feasible solutions in very short computational time. Swarm intelligence and evolutionary algorithms are becoming more and more popular for solving large-scale scheduling and optimization problems with time constraints, including energy-efficiency scheduling in complex production.

The motivation of this review work is the green manufacturing and energy-saving awareness in production scheduling area. The design of intelligent scheduling strategies should consider energy efficiency and reducing energy consumption which is an important scheduling objective in current production scheduling area. The purpose of this study is to perform a literature review about production/shop scheduling with considering energy-efficiency objectives. It can be considered as a comprehensive reference for readers from both academia and industry. We will summarize existing achievements, analyze and discuss the current and ongoing research, and indicate some future directions, especially the swarm intelligence and evolutionary algorithms for tackling such NP-hard problems. The remainder of this paper is organized as follows. “Scope” section describes the scope of this review work. Afterwards, existing achievement is summarized and analyzed in “Classification” section. Next, detail discussion and analyzing up to date and ongoing research in the field of production/shop scheduling with energy efficiency/consumption and constraints/objectives are given in “Research trends” section. Furthermore, some future research directions are also indicated in this section. Finally, this review paper is concluded in the last section.

## Scope

An indispensable part of a literature review work is the review scope and purpose. The topic of this review is production/shop scheduling with energy efficiency, consumption, or cost as constraints or objectives, given in the following: (1) the category of the shop floor, (2) model of the scheduling problems, (3) objectives (e.g., energy, completion time, machine workload, due date, and so forth), (4) research approaches (strategies) or algorithms, especially swarm intelligence and evolutionary algorithms, and (5) the aspects of energy consumption.

Keywords for database index

Energy-related keywords | Scheduling-related keywords | Intelligent strategies |
---|---|---|

Energy efficiency | Production scheduling | Swarm intelligence |

Energy consumption | Shop scheduling | Evolutionary algorithm |

Energy cost | Energy scheduling | Meta-heuristics |

Reviewed journals and number of relevant papers

Journals’ names | Number of publications |
---|---|

Journal of Cleaner Production | 16 |

International Journal of Production Research | 8 |

CIRP Annals-Manufacturing Technology | 5 |

IEEE Access | 4 |

Sustainability | 4 |

Computers & Chemical Engineering | 3 |

International Journal of Advanced Manufacturing Technology | 3 |

Journal of Engineering Manufacture | 3 |

Computers in Industry | 2 |

IEEE Transactions on Automation Science and Engineering | 2 |

IEEE Transactions on Industrial Informatics | 2 |

Mathematical Problems in Engineering | 2 |

Sensors | 2 |

## Classification, analysis, and synthesis of existing achievements

Literature classification [4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94]

Articles | Shop floor category | Model | Objectives | Approach (algorithm) | Energy consumption aspect |
---|---|---|---|---|---|

Lora et al., 2003 | Unknown | NLMIP | Energy | GA | Start-up and shut-down |

He et al., 2005 | Job shop | Others | Energy and makespan, | TS | Unknown |

Bruzzone et al., 2012 | Flexible flow shop | MIP | Energy | Heuristics | Unknown |

Cao et al., 2013 | Unknown | Neural network model | Energy | FSM | On/off, warm up, idle, and processing |

Chen et al., 2013 | Unknown | Others | Energy | Greedy algorithm | Machine start-up/shut-down |

Dai et al., 2013 | Flexible flow shop | Others | Energy and makespan | Genetic SA | Setup and idle |

Moon et al., 2013 | Unknown | MILP | Energy and makespan | Improved GA | Unknown |

Jiang et al., 2014 | Flexible job shop | Others | Energy and others | NSGAII | Processing |

Moon and Park, 2014 | Flexible job shop | MIP | Energy and others | CPLEX | Peak load, mid-load and off-peak load |

Pach et al., 2014 | FMS | Unknown | Energy and others | Unknown | Unknown |

Shrouf et al., 2014 | Single machine | Others | Energy | GA and analytical solution | Processing and Idle |

Zhang et al., 2014 | Flow shop | IP | Energy | Unknown | peak hour, mid-peak, and off peak |

Dai et al., 2015 | Job shop | MIP | Energy and makespan | Modified GA | Loading/unloading, processing |

Duerden et al., 2015 | Unknown | Others | Energy | Modified GA | Unknown |

Garcia-Santiago et al., 2015 | Job shop | Unknown | Energy | HS | Unknown |

Lee and Prabhu, 2015 | Unknown | Feedback control | Energy and others | Integral controller approach | Processing |

May et al., 2015 | Job shop | Others | Energy and makespan | Green GA | Processing, idle, setup, standby, and ramp |

Tang and Dai, 2015 | Job shop | MIP | Energy and makespan | Genetic SA | Setup, processing and idle |

Tong et al., 2015 | Unknown | MINLP | Energy | DICOPT | Unknown |

Zhang et al., 2015 | Multi-factories | Others | Energy | Distributed optimization | Unknown |

Escamilla et al., 2016 | Job shop | Others | Energy and makespan | GA and CPLEX | Unknown |

Li et al., 2016 | Specific product | MIP | Energy | CPLEX | Unknown |

Oddi and Rasconi, 2016 | Flexible job shop | Others | Energy and makespan | NLS | Unknown |

Salido et al., 2016a | Job shop | Others | Energy and others | CPLEX | Unknown |

Salido et al., 2016b | Job shop | Others | Energy and makespan | GA and CPLEX | Unknown |

Su et al., 2016 | Cracking production | MINLP | Energy and others | DICOPT | Unknown |

Tang et al., 2016 | Flexible flow shop | Others | Energy and makespan | Improved PSO | Setup, idle and operation |

Tonelli et al., 2016 | Unknown | MIP | Energy and tardiness | Multi-agent | Unknown |

Tong et al., 2016 | Unknown | Others | Energy | Improved GA | Unknown |

Yan et al., 2016 | Flexible flow shop | Others | Energy and makespan | GA | Unknown |

Yang et al., 2016 | Flexible job shop | Others | Energy and makespan | NSGA-II | Start-up, shut-down, idle and processing |

Zhang and Chiong, 2016 | Job shop | MILP | Energy and tardiness | MOGA | Processing and Idle energy |

Giglio et al., 2017 | Job shop scheduling | MIP | Energy | Relax-and-fix heuristic | Setup and processing |

Gong et al., 2017 | Specific product | Others | Energy and labor-aware | GA with heuristic | Unknown |

Kim et al., 2017 | Unknown | Unknown | Energy | Additive regression algorithm | Unknown |

Lee et al., 2017 | Single machine | MINLP | Energy and E/T | DIATC Heuristic | Unknown |

Lei et al., 2017 | Flexible job shop | Others | Energy and workload balance | SFLA | Unknown |

Liu et al., 2017 | Fuzzy flow shop | Others | Energy and tardiness | IGA with heuristics | Idle, setup and processing |

Lu et al., 2017 | Permutation flow shop | Others | Energy and makespan | HBSA | Setup, transportation, and idle |

Misra et al., 2017 | Specific product | MILP | Energy | FXOS | Unknown |

Modos et al., 2017 | Single machine | Others | Energy constraint | Branch-and-Bound and TS | Unknown |

Mokhtari and Hasani, 2017 | Flexible job shop | Unknown | Energy and makespan | Enhanced GA | Unknown |

Otis and Hampson, 2017 | Unknown | Unknown | Energy | Advanced scheduling and ERP | Changeover and start-up |

Plitsos et al., 2017 | Flexible job shop | DSS | Energy constraint | ILS | Processing, idle, subsidiary equipment |

Rahimi and Ziaee, 2017 | Permutation flow shop | Others | Energy and makespan | GA and SA | Setup and processing |

Raileanu et al., 2017 | Job shop | Others | Energy and makespan | CPLEX | Unknown |

Ramos and Leal, 2017 | Unknown | ILP | Energy | CPLEX | Unknown |

Sundstrom et al., 2017 | Unknown | MINLP | Energy and others | Systematic method | Unknown |

Wang et al., 2017 | Unknown | MINLP | Energy | DICOPT | Production |

Xu and Wang, 2017 | Job shop | Others | Energy and makespan | feedback control method | Unknown |

Yin et al., 2017 | Job shop | MIP | Energy and others | GA with simplex lattice design | Loading, idle, and processing |

Zhai et al., 2017 | Unknown | Time-series model | Energy | Dynamic scheduling | Unknown |

Zhang et al., 2017a | Flow shop | IP | Energy | Unknown | Unknown |

Zhang et al., 2017b | Flexible job shop | Others | Energy and makespan | BBO + VNS | Unknown |

Aghelinejad et al., 2018 | Single machine | Others | Energy | GA and CPLEX | Processing and idle energy |

Escamilla and Salido, 2018 | Job shop | Unknown | Energy and makespan | GA + LS | Unknown |

Feng et al., 2018 | Job shop | Monitoring system | Energy | Modified GA | Processing and idle |

Jiang and Deng 2018 | Flexible job shop | Others | Energy and E/T | DCSO | Processing and idle |

Jiang et al., 2018a | Job shop | Others | Energy and tardiness | GWO | Idle cost and tardiness |

Jiang et al., 2018b | Job Shop | Others | Energy and completion time | Improved WOA | Machine speed, processing, and idle |

Khalaf and Wang, 2018 | Flow shop | MILP | Energy | General Algebraic Modeling System | Unknown |

Lei et al., 2018 | Hybrid flow shop | Others | Energy and tardiness | TLBO | Unknown |

Leo and Engell, 2018 | Unknown | MILP | Energy | CPLEX | Unknown |

Li et al., 2018a | Hybrid flow shop | Others | Energy and makespan | MOA | Processing, standby, and setup |

Li et al., 2018b | Flow shop | Others | Energy and makespan | ABC | Unknown |

Liu et al., 2018a | Permutation flow shop | Others | Energy | NEH heuristic | Idle |

Liu et al., 2018b | Flexible flow shop | Others | Energy and makespan | Improved NSGAII | Unknown |

Lu et al., 2018 | Flow shop | Others | Energy and others | GWA + LS Grey wolf + LS | Unknown |

Meziane et al., 2018 | Flexible job shop | Others | Energy | NSGAII | Unknown |

Wang et al., 2018a | Blocking flow shop | Others | Energy and makespan | PVNS + LS + NEH | Blocking and idle energy |

Wang et al., 2018b | Flexible job shop | NLP | Energy and cost | GA + PSO | Unknown |

Wu et al., 2018 | Flexible flow shop | Others | Energy and makespan | Hybrid NSGA-II with local search | Processing and Idle |

Wu and Sun, 2018 | Flexible job shop | Others | Energy and others | NGSA_II with heuristics | Unknown |

Zhang et al., 2018a | Flexible job shop | Others | Energy and others | IMHGA | Processing, idle and transportation |

Zhang et al., 2018b | Flexible job shop | Unknown | Energy and makespan | Modified SFLP | Unknown |

Zhao et al., 2018 | Unknown | MILP | Energy | CPLEX. | Unknown |

Cui et al., 2019 | Unknown | NLMP | Energy | Sub-gradient descent | On-peak and off-peak |

Faria et al., 2019 | Unknown | Others | Energy | GA | Unknown |

Gong et al., 2019 | Flexible job shop | Others | Energy and others | NSGA-III | Processing, setup, idling |

Gong et al., 2019 | Specific product | Others | Energy and labor | MA and NSGA-II | Processing, setup, idling |

Hassani et al., 2019 | Job shop | MILP | Energy | CPLEX | Setup, processing, and inventory |

Jiang and Wang 2019 | permutation flow shop | Others | Energy and makespan | MOEA | Setup, processing, and transportation |

Jiang and Zhang, 2019 | Hybrid flow shop | MILP | Energy and tardiness | EOMO algorithm | Non-processing |

Lei et al., 2019 | Flexible job shop | Others | Energy constraint | ICA | Unknown |

Liu et al., 2019 | Flexible job shop | MIP | Energy and makespan | GA + GSO | Processing and transportation |

Meng et al., 2019a | Flexible job shop | MILP | Energy | CPLEX | Idle and common consumption |

Meng et al., 2019b | Hybrid flow shop | MILP | Energy | IGA | Processing, idle, and common |

Shen et al., 2019 | Unknown | Others | Energy | Improved GA | Processing and failure |

Wu et al., 2019 | Unknown | MILP | Energy | Score ranking algorithm | Unknown |

Zhang et al., 2019 | Flexible job shop | Others | Energy and makespan | NSGA-II | Processing, idle, and setup |

### Shop floor category

### Problem models

### Swarm intelligence and evolutionary algorithms

- 1.
Initializing algorithm’s parameters and the population.

- 2.
Evaluate initializing solutions in population and objectives values.

- 3.
Generating new solutions based on the current solutions in population.

- 4.
Evaluate new solutions and replace the current ones.

- 5.
If the stop criterion is not satisfied, go to Step (3); else, go to Step (6).

- 6.
Stop and report the best solution and corresponding objective values.

For different algorithms, the strategies to generate new solutions are different, and the stop criterion is different. For different scheduling problems in intelligent production systems, there are different initializing rules and different local search operators.

To improve the convergence performance of swarm intelligence and evolutionary algorithms, some intelligent strategies are proposed in some reviewed articles. Here, we introduce several representative ones. Wu et al. [79] proposed idle time and machine turn on/off strategies for operation assignment and energy-saving purposes. Elapsed time for time turning off the idle machines is dependent on the length of idle time slots. May et al. [25] proposed an intelligent strategy to remove the overlapping solutions in the population initialization and new solutions generating phases. It can improve the diversity of population and avoid repeat solutions with the same function values. Zhang et al. [6] developed an intelligent strategy to reduce energy consumption and improved energy efficiency in a production sequence fixed solution, by controlling the additional tardiness allowed for each job in the solution. These intelligent strategies improve the convergence performance of swarm intelligence and evolutionary algorithms, and their details can be found in the corresponding articles.

### Other approaches

### Objectives or constraints

### Aspects of energy consumption

## Research trends

The awareness of energy efficiency, sustainability, and green manufacturing, and production scheduling with energy objectives has becomes a hot topic in the past 5 years (see Fig. 1). From 2013, the number of published articles increases year by year. The number of published articles in 2017 and 2018 is more than 20. Till April 2019, more than ten production scheduling articles for energy-related objectives are published. Energy-related objectives become a new trend of production scheduling and will play vital and important role in production scheduling.

### From single objective to multi-objective

### Swarm intelligence and evolutionary algorithms

The total ratio of GA and NSGA-II is about 52% among all swarm intelligence and evolutionary algorithms. Beside the NSGA-II, multi-objective evolutionary algorithm (MOEA) is also used in about 8% articles. The ratios of SA, PSO, shuffled frog-leaping algorithm (SFLA), and Grey wolf optimizer (GWO) are equal to or larger than 4%. The algorithms with ratios less than 4% are recorded as “Others” class and the total ratio of them is 22%, which means that at least six swarm intelligence and evolutionary algorithms are included in the “Others” class. Totally, more than 13 algorithms are employed or improved for solving production scheduling problems with energy-related objectives in the past 5 years. In fact, swarm intelligence and evolutionary algorithms are effective and widely used for solving energy-efficiency scheduling problem.

### Extending of energy consumption aspects

In summary, energy-related objectives are aware of production scheduling from 2012, especially in the past 5 years. In view of the high complexity of production scheduling, many swarm intelligence and evolutionary algorithms are employed and improved to solve this problem. Energy-related objectives are optimized with traditional scheduling objectives, simultaneously. The main aspects of energy consumption, e.g., processing energy, idle, and setup energy, in a production process are modeled and evaluated. Some researchers have started to consider other energy consumption in a production process, for instance, energy for component transportation [80, 88].

## Future directions

Based on the previous analysis of research trends of production scheduling with energy-related objectives, we consider and indicate some future research directions for this topic.

### Modeling of energy efficiency-related constraints and objectives

All aspects of energy consumption in production should be classified based on necessity and possibility of reducing the cost. The aspects of energy consumption with reduction possibility should be considered and modeled as scheduling constraints or objectives in production scheduling. The more aspects of energy consumption with reduction possibility are modeled, the higher possibility to reduce energy consumption and to improve energy efficiency.

### Analysis of the relationship between energy-related objectives and the traditional objectives

Production scheduling is a multi-objective problem. Energy-related objectives are emerging targets compared to traditional objectives, e.g., completion time, machine workload, due date, and so on, The relationship between energy-related objectives and the traditional objectives must be researched and analyzed. Does energy-related objective conflict to a certain extent with traditional objectives? It is a precondition to solve energy-efficiency scheduling and does not affect the optimization of other goals.

### Developing swarm intelligence and evolutionary algorithms

Based on the synthesis and analysis in above two sections, swarm intelligence and evolutionary algorithms are efficient and effective to solve energy-efficiency scheduling problems, especially for the large-scale problems. How to design and develop more high-quality algorithms, especially non-dominated multi-objective algorithms, for solving energy-efficiency scheduling problem which is an important direction. Some local search operators based on the feature of energy-efficiency scheduling can conduce to improve the convergence speed of swarm intelligence and evolutionary algorithms. Algorithmic accuracy and time efficiency are the key performance indicators.

### Energy efficiency-based multi-objective scheduling strategy

Energy-efficiency scheduling is an important objective in intelligent production system. There are some other objectives, e.g., completion time-related objectives, machine workload-related objectives, and the due date-related objectives and so forth. How to design energy efficiency-based multi-objective scheduling strategy is a novel and interesting direction. Especially, the non-dominate strategy for multi-objective scheduling is the key issue to improve algorithms’ performance.

### Develop energy-efficiency intelligent scheduling framework

Energy-efficiency scheduling is a novel topic in production scheduling. It would be a great contribution to this topic if a general framework, especially an intelligent scheduling framework, can be established, which can guide the research and development of this topic. For energy-efficiency scheduling, it would be a good way to be integrated into the overall framework of production scheduling and to be embedded into an intelligent scheduling framework with intelligent scheduling strategizes. Furthermore, the existing modeling strategies, solving approaches and algorithms (including swarm intelligence and evolutionary algorithms), and benchmark instances for general production scheduling can be directly applied to energy-efficiency scheduling or be used after appropriate adjustments.

### Practice in some special fields and even special products

For production scheduling, manufacturing enterprises are more focused on the practicality of models and algorithms, especially for a special product. Till now, few published articles addressed on this matter because of since the complexity and multi-constraints in real-life situations. It is an important and practical work to model energy-efficiency scheduling for a special product and develop a high-quality meta-heuristic to solve it. This research direction can effectively promote Industry-University-Research Collaborations.

## Conclusions

The growing awareness of energy efficiency and sustainable development has led to persistent attention to energy efficiency in production scheduling. The growing number of published articles, especially in the past 5 years, makes energy-efficiency scheduling a hot research topic. In the review process of this review work, there are several newest publications related to the scope of this review paper [95, 96, 97, 98, 99] which shows the importance and high relevancy of the studied subject. Intelligent strategies are used by many researchers in scientific community. This study presented the review of five indicators, including shop floor, models, approaches and algorithms, objectives, and aspects of energy consumption in the literature for solving energy-efficiency scheduling problems. Intelligent strategies, including swarm intelligence, evolutionary algorithms, and improvement strategies, are synthesized, discussed, and analyzed in detail. Furthermore, the current research trends, especially the intelligent strategies, are analyzed and summarized. For the continued study of this topic, five instructive directions, including modeling, objectives, intelligent strategies, intelligent scheduling framework, and practice, are given which provide an insight for future studies.

## Notes

### Acknowledgements

This work was supported by the Faculty Research Grants (FRG) from Macau University of Science and Technology, the National Natural Science Foundation of China (Grant No. 61603169, 61873328), the National Key R&D Program of China (No. 2016YFB0901900), National Natural Science Fund for Distinguished Young Scholars of China (Grant No. 61525304), the DongGuan Innovative Research Team Program (No. 2018607202007), and the funding from Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology.

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