Green flexible job shop scheduling problem (FJSP) aims to improve profit and reduce energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy processing time is proposed to predict the uncertainty in timing constrain for better simulating the practical production. This study addresses the multi-objective energy-efficient flexible job shop scheduling problem with type-2 processing time (ET2FJSP), where the minimization of makespan and total energy consumption are considered simultaneously. The previous studies do not propose the model verification and energy-saving strategy. Moreover, the best parameters required by an algorithm in different stage is different. Therefore, we propose a mixed integer linear programming model and design a learning-based reference vector memetic algorithm (LRVMA). Its main features are: i) four problem-specific initial rules are presented for initialization to generate diverse solutions; ii) four problem-specific local search methods are incorporated to enhance the exploitation; iii) an effective solution selection method depending on the Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; iv) a reinforcement learning-based parameter selection strategy is proposed to improve the diversity of nondominated solutions, and v) an energy-saving strategy is designed to reduce energy consumption. To verify the effectiveness of LRVMA, it is compared against other related algorithms. The results demonstrate that LRVMA outperforms the compared algorithms for solving ET2FJSP.

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is an extension of the traditional FJS, which is harder to solve. This work aims to minimize total energy consumption (TEC) and makespan for DHFJS. A deep $Q$ -networks-based co-evolution algorithm (DQCE) is proposed to solve this NP-hard problem, which includes four parts: First, a new co-evolutionary framework is proposed, which allocates sufficient computation to global searching and executes local search surrounding elite solutions. Next, nine problem features-based local search operators are designed to accelerate convergence. Moreover, deep $Q$ -networks are applied to learn and select the best operator for each solution. Furthermore, an efficient heuristic method is proposed to reduce TEC. Finally, 20 instances and a real-world case are employed to evaluate the effectiveness of DQCE. Experimental results indicate that DQCE outperforms the six state-of-the-art algorithms for DHFJS.

With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood search. However, the local search (LS) operators are inefficient due to strong randomness; 2) the confidence-based adaptive operator selection model follows the experiences of the major crowds, which ignores the efficient operators with low weight, so it can not select the really efficient operator; 3) the previous works lack of efficient strategy to save energy; and 4) the mainstream memetic framework adopts LS to all solutions, which causes the population to converge too quickly and the diversity is extremely reduced. Thus, we propose a surprisingly popular-based adaptive MA (SPAMA) to overcome the above deficiencies. The contributions are as follows: 1) four problem-based LS operators are employed to improve the convergence; 2) a surprisingly popular degree (SPD) feedback-based self-modifying operators selection model is proposed to find the efficient operators with low weight and correct crowd decision making; 3) the full active scheduling decoding is presented to reduce the energy consumption; and 4) an elite strategy is designed to balance the resources between global and LS. In order to evaluate the effectiveness of SPAMA, it is compared with state-of-the-art algorithms on Mk and DP benchmarks. The results demonstrate the superiority of SPAMA to the state-of-art algorithms for solving EDFJSP.

Human–robot collaborative scheduling has been widely applied in modern manufacturing industry. A rational scheduling of human–robot cooperation plays an important role in improving production efficiency. However, human–robot collaborative scheduling problem in welding production has not been studied so far. Thus, this article addresses a human–robot collaborative welding shop scheduling problem (HCWSSP) with minimization objectives of makespan and total energy consumption (TEC). To solve this multiobjective HCWSSP, a Pareto-based memetic algorithm (PMA), which hybridizes a genetic operator and variable neighborhood search (VNS), is presented to obtain a set of tradeoff solutions between makespan and TEC. In PMA, each solution is represented by two parts, i.e., job processing sequence and resource assignment. A novel integrated initialization strategy is proposed to generate one initial population with high quality and good diversity. Furthermore, five kinds of VNS are designed to improve the exploitation capability of PMA. Experimental results on test problems manifest that the proposed PMA performs better than its competitors.