Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

In the face of energy crisis, manufacturers pay more and more attention to energy-saving scheduling. In the paper, we consider the distributed heterogeneous re-entrant hybrid flow shop scheduling problem (DHRHFSP) with sequence dependent setup times (DHRHFSP-SDST) considering factory eligibility constraints under time of use (TOU) price, which means that each job can only be assigned to its available set of factories and all factories have different number of machines and processing capacity, and so on. To deal with DHRHFSP-SDST, a multi-objective Artificial Bee Colony Algorithm (MOABC) is proposed to optimize both the makespan and total energy consumption. For the MOABC, firstly, a hybrid initialization method is presented to initialize the population; then, due to the electricity price shows significant differences vary from periods under TOU price, the energy saving operator based on right-shift strategy is proposed to avoid processing jobs with the high electricity price without affecting the productivity; thirdly, based on the full consideration of distributed heterogeneous and factory eligibility, crossover and mutation operators, three neighborhood search operators and new food sources generation strategy are designed; lastly, extensive experiments demonstrate the effectiveness of the proposed algorithm on solving the DHRHFSP-SDST.

This paper focuses on the multi‐objective optimization of the reentrant hybrid flowshop scheduling problem (RHFSP) with machines turning on and off control strategy. RHFSP exhibits significance in many industrial applications, but scheduling with both energy consumption consideration and reentrant concept is relatively unexplored at present. In this study, an improved Multi‐Objective Multi‐Verse Optimizer (IMOMVO) algorithm is proposed to optimize the RHFSP with objectives of makespan, maximum tardiness, and idle energy consumption. To solve the proposed model more effectively, a series of improved operations are carried out, including population initialization based on Latin hypercube sampling (LHS), individual position updating based on Lévy flight, and chaotic local search based on logical self‐mapping. In addition, a right‐shift procedure is used to adjust the start time of operations aiming to minimize the idle energy consumption without changing the makespan. Then, Taguchi method is utilized to study the influence of different parameter settings on the scheduling results of the IMOMVO algorithm. Finally, the performance of the proposed IMOMVO algorithm is evaluated by comparing it with MOMVO, MOPSO, MOALO, and NSGA‐II on the same benchmark set. The results show that IMOMVO algorithm can solve the RHFSP with machines turning on and off control strategy effectively, and in terms of convergence and diversity of non‐dominated solutions, IMOMVO is obviously superior to other algorithms. However, the distribution level of the five algorithms has little difference. Meanwhile, by turning on and off the machine properly, the useless energy consumption in the production process can be reduced effectively.

The classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop scheduling problem with dual resource constraints (DHFSP). Firstly, according to the characteristics of DHFSP and various constraints, the model is built to minimize the maximum completion time (makespan), total tardiness time and workload balance of worker. Then, an improved multi-objective memetic algorithm (IMOMA) is proposed to solve the DHFSP, which mainly includes the improvement of initial population, crossover, mutation and local search. In addition, Taguchi method is used to set parameters. Finally, through numerical experiments, IMOMA is compared with NSGA-II, MODE and MOMVO algorithms. The experimental results show that IMOMA can solve the multi-objective hybrid flow shop scheduling problem with dual resource constraints effectively. In terms of convergence, diversity and dominance of non-dominated solutions, IMOMA is significantly superior to other algorithms, but the distribution uniformity of non-dominated solutions of the four algorithms are not significantly different.

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Although re-entrant hybrid flow shop scheduling is widely used in industry, its processing and delivery times are typically determined using precise values that frequently ignore the influence of machine failure, human factors, the surrounding environment, and other uncertain factors, resulting in a significant gap between theoretical research and practical application. For fuzzy re-entrant hybrid flow shop scheduling problem (FRHFSP), an integrated scheduling model is established to minimize the maximum completion time and maximize the average agreement index. According to the characteristics of the problem, a hybrid NSGA-II (HNSGA-II) algorithm is designed. Firstly, a two-layer encoding strategy based on operation and machine is designed; Then, a hybrid population initialization method is designed to improve the quality of the initial population; At the same time, crossover and mutation operators and five neighborhood search operators are designed to enhance the global and local search ability of the algorithm; Finally, a large number of simulation experiments verify the effectiveness and superiority of the algorithm.