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Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs. [ABSTRACT FROM AUTHOR]

To meet the production demand of workshop, this paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to produce a AGV transportation solution that minimizes the total cost, including travel cost, time cost, and AGV cost. Therefore, a mixed integer linear programming model is established. To improve the transportation efficiency, a dynamic calculation method is developed. In the DABC algorithm, a heuristic algorithm and a median based probability selection method are used. For improving the quality of the solutions, four effective neighborhood operators are introduced. In the local search, a rule is given to save the operation time and a problem-based search operator is proposed to improve the quality of the best individual. Finally, a series of comparison experiments were implemented with the iterative greedy algorithm, artificial bee colony algorithm, hybrid fruit fly optimization algorithm, discrete artificial bee colony algorithm, improved harmony search, and hybrid genetic-sweep algorithm. The results show that the proposed DABC algorithm has high performance on solving the delivery and pickup problem. [ABSTRACT FROM AUTHOR]

Disassembly is one of the most time-consuming and labor-intensive activities during the value recovery of end-of-life (EOL) products. The completion time (makespan) of disassembling EOL products is highly associated with the allocation of operators, especially in parallel disassembly. In this paper, asynchronous parallel disassembly planning (APDP), which avoids the necessity to synchronize disassembly tasks of manipulators during the parallel disassembly process, is studied to optimize the task assignment of manipulators for minimal makespan. We utilize four mixed integer linear programming (MILP) formulations to identify the optimal solutions. A set of different-sized instances are used to test and compare the performance of the proposed models, including some real-world cases. Finally, the proposed exact algorithm is further compared with the existing approach to solving APDP. Results indicate that a significant difference exists in terms of the computational efficiency of the MILP models, while three of four MILP formulations can efficiently achieve better solutions than that of the existing approach. [ABSTRACT FROM AUTHOR]

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome. [ABSTRACT FROM AUTHOR]

The lot-streaming flow shop scheduling problem plays an important role in modern industry. This paper addresses this problem with the objective of minimizing the total weighted earliness and tardiness penalties and then proposes a simple but effective iterated local search (ILS) algorithm. In the proposed ILS algorithm, an adapted Nawaz-Enscore-Ham (NEH) heuristic is used to generate an initial solution. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. A simulated-annealing-typed acceptance criterion is utilized to determine the start point for next iteration. Extensive experiments are conducted to compare the proposed ILS algorithm with some existing algorithms. The computational results and comparisons demonstrate the effectiveness of the proposed ILS algorithm. [ABSTRACT FROM AUTHOR]