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.

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.

In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.

Green manufacturing has attracted increasing attention under the background of carbon peaking and carbon neutrality. Distributed production has widely existed in various manufacturing industries with the development of globalization. This article investigates an energy-efficient distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (DNWFSP-SDST) to minimization of makespan and total energy consumption (TEC). A mixed-integer linear programming model of energy-efficient DNWFSP-SDST is constructed and a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) is proposed to address energy-efficient DNWFSP-SDST in this article. In CMAQ, a heuristic named RNRa is proposed to generate initial solutions. A bipopulation cooperative framework based on double Q-learning is designed to further optimize the solutions. According to the properties of energy-efficient DNWFSP-SDST, an energy-saving strategy based on knowledge is proposed to improve makespan and TEC. The results of experiments show that the performance of CMAQ is superior to certain state-of-the-art comparison algorithms in solving energy-efficient DNWFSP-SDST.

This paper investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Last, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this paper.