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.

Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with $Q$ -learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). $Q$ -learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The $\varepsilon$ -greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP.

Facing increasingly serious ecological problems, sustainable development and green manufacturing have attracted much attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming widespread. This paper addresses an energy-efficient scheduling of the distributed permutation flow-shop (EEDPFSP) with the criteria of minimizing both makespan and total energy consumption. Considering the distributed and multiobjective optimization complexity, a knowledge-based cooperative algorithm (KCA) is proposed to solve the EEDPFSP. First, a cooperative initialization scheme is presented with both extended energy-efficient Nawaz-Enscore-Ham heuristic and slowest allowable speed rule that are specially designed to produce good initial solutions with certain diversity. Second, several properties of the nondominated solutions are investigated based on the characteristics of the bi-objective problem, which are used to develop the knowledge-based search operators. Third, a cooperative search strategy of multiple operators is designed for the solutions with different characteristics to tradeoff two objectives. Fourth, a knowledge-based local intensification is used for exploiting better nondominated solutions sufficiently. Moreover, an energy saving method based on the critical path is used to further improve the performance. The effect of parameter setting on the KCA is investigated with the Taguchi method of design-of-experiment. Extensive computational tests and comparisons are carried out, which verify the effectiveness of the special designs of the KCA in solving the EEDPFSP.

Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.

With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, distributed manufacturing systems have become emerging due to the development of globalization. This article addresses the energy-aware distributed hybrid flow-shop scheduling (EADHFSP) with minimization of makespan and energy consumption simultaneously. We present a mixed-integer linear programming model and propose a cooperative memetic algorithm (CMA) with a reinforcement learning (RL)-based policy agent. First, an encoding scheme and a reasonable decoding method are designed, considering the tradeoff between two conflicting objectives. Second, two problem-specific heuristics are presented for hybrid initialization to generate diverse solutions. Third, solutions are refined with appropriate improvement operator selected by the RL-based policy agent. Meanwhile, an effective solution selection method based on the decomposition strategy is utilized to balance the convergence and diversity. Fourth, an intensification search with multiple problem-specific operators is incorporated to further enhance the exploitation capability. Moreover, two energy-saving strategies are designed for improving the nondominated solutions. The effect of parameter setting is investigated and extensive numerical tests are carried out. The comparative results demonstrate that the special designs are effective and the CMA is superior to the existing algorithms in solving the EADHFSP.

Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.