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Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance. [ABSTRACT FROM AUTHOR]

Parallel-batching processing and job deterioration are universal in the real industry. Scholars have deeply investigated the problem of parallel-batching scheduling and the problem of scheduling with deteriorating jobs separately. However, the situations where both parallel-batching processing and job deterioration exist simultaneously were seldom considered. This paper studies the parallel-batching scheduling problem with nonlinear processing times on a single machine, and proposes several structural properties and an optimal algorithm to solve it. Based on the above properties and optimal algorithm for the single machine setting, we further study the problem of parallel-batching scheduling with nonlinear processing times under the unrelated parallel machine setting. Since the unrelated parallel machines scheduling problem is NP-hard, a hybrid SFLA-VNS algorithm combining Shuffle Frog Leap Algorithm (SFLA) with Variable Neighborhood Search Algorithm (VNS) is proposed. Computational experiments and comparison are finally conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates a competitive two-agent parallel-batching scheduling problem with aging effect on parallel machines. The objective is to minimize the makespan of agent A with the constraint that the makespan of agent B is no more than a given threshold. Some key structural properties are first identified in two different cases, and based on these structural properties a novel decision tree of scheduling rules is constructed and a heuristic algorithm is designed. Then, an effective hybrid BF-VNS algorithm combining Bacterial Foraging (BF) with variable neighborhood search (VNS) is developed to tackle the studied problem. Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The experimental results indicate that the hybrid BF-VNS algorithm performs quite better than the compared algorithms. [ABSTRACT FROM AUTHOR]

Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments. [ABSTRACT FROM AUTHOR]

This research investigates the parallel-batching scheduling problems with a time-dependent learning effect where the job processing time is a decreasing function of its starting time. Both the single-machine and parallel-machine circumstances are considered, and the objective is to minimize the makespan. For the single parallel-batching machine scheduling problem, some structural properties and a heuristic algorithm are developed to solve it. Since the parallel-batching machines scheduling problem is NP-hard, a hybrid CS-JADE algorithm combining improved cuckoo search algorithm (CS) and self-adaptive differential evolution (DE) is proposed to solve this problem. The computational experiments indicate that the proposed hybrid algorithm performs well both in effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates a single machine serial-batching scheduling problem considering release times, setup time, and group scheduling, with the combined effects of deterioration and truncated job-dependent learning. The objective of the studied problem is to minimize the makespan. Firstly, we analyze the special case where all groups have the same arrival time, and propose the optimal structural properties on jobs sequencing, jobs batching, batches sequencing, and groups sequencing. Next, the corresponding batching rule and algorithm are developed. Based on these properties and the scheduling algorithm, we develop a hybrid VNS-ASHLO algorithm incorporating variable neighborhood search (VNS) and adaptive simplified human learning optimization (ASHLO) algorithms to solve the general case of the studied problem. Computational experiments on randomly generated instances are conducted to compare the proposed VNS-ASHLO with the algorithms of VNS, ASHLO, Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The results based on instances of different scales show the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The high-end equipment features with high value, complicated manufacturing process, and high status, and it thus brings a huge challenge to increase reliability, quality, and productivity during the production. In order to tackle this challenge and achieve automation, integration, and intelligence this paper proposes a hybrid metaheuristic for an integrated order scheduling and maintenance planning model with position-based processing time, parallel-batching processing, and multiple manufacturers. During the production, the continuous operation of the machine increases the probability of failure, and the repair work can eliminate the failure For each order, we derive some useful lemmas and develop an optimal algorithm to schedule jobs within it. Then, given the order assignment and sequence in the manufacturers, we propose a dynamic programing algorithm to make the decision on the maintenance planning. Subsequently, the investigated problem is proved to be NP-hard, thus, we propose a hybrid discrete black hole algorithm and variable neighborhood search (DBH-VNS) approach to solve the integrated problem. Some improvements are integrated into the proposed algorithm to obtain the competitive results, which include discrete encoding-based population updating scheme, the modified neighborhoods, and the VNS-based local search. Finally, we conduct computational experiments and the results demonstrate the effectiveness and validity of the proposed hybrid metaheuristic. • We propose a new integrated order scheduling and maintenance planning model. • An optimal algorithm for scheduling jobs within the order is proposed. • A dynamic programing algorithm is proposed to schedule maintenance activities. • A hybrid DBH-VNS is designed to solve the problem in general case. [ABSTRACT FROM AUTHOR]