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At present, Multi-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence (MISATPS) is an advanced algorithm in the field of integrated scheduling with multi-devices-process problems. This algorithm ignores the influence of the pre-process on the post-process when solving the multi-devices-process integrated scheduling problem, which leads to the problem of poor closeness between serial processes and poor parallelism between parallel processes. This paper points out that there is no restriction of scheduling sequence between parallel processes on the same processing device. It can be scheduled flexibly of the sequence between parallel processes of the same device. Therefore, based on the scheduling scheme of MISATPS, the algorithm is improved by applying the interchange strategy and the interchange adjustment strategy of multi-device adjacent parallel process. In this way, the influence of the pre-process on the post-process is avoided, the compactness of the serial process and the parallelism of the parallel process are improved, and the scheduling result is optimized.

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

Currently, integrated scheduling algorithms schedule processes using fixed rules, making it difficult to balance serial and parallel processes in product craftsmanship trees while conducting complicated single product scheduling. To solve this problem, we propose a time‐selective integrated scheduling algorithm with a backtracking adaptation (TISAWBA) strategy. The proposed process sequence sorting strategy aims to determine process scheduling sequences based on the overall structure of the machining craftsmanship tree. The proposed time‐selective scheduling strategy aims to select the process portfolio with the minimum total elapsed time for machining from a process portfolio set based on craftsmanship tree structure. The proposed backtracking adaptation strategy conducts backtracking adaptation to find process portfolios having a total elapsed machining time greater than the “scheduling reference time.” Finally, illustrative use cases verify that TISAWBA guarantees parallel processing for parallel processes and elevates the proximity of serial processes, generating optimized integrated scheduling results.

To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy and Backtracking Strategy is proposed. The scheduling order of each process in the process tree needs to be determined, which will be completed by the process sequence sequencing strategy. The scheduling plan also needs to be determined, which will be completed using the time-selective scheduling strategy for the two workshops. At the same time, the “reference time” is set for the current scheduling process. To find a better scheduling scheme, the “scheduling reference time” is recorded as T. If the time of the current scheduling process scheme is greater than T, the backtracking adjustment strategy will be used to track the process scheduling scheme. Finally, experiments show that the algorithm not only ensures the parallel processing of parallel processes but also effectively improves the tightness of serial processes and optimizes the results of integrated scheduling.