Stemmed from our novel single-photon imaging concept of detector self-collimation—which leverages detectors themselves as collimators to overcome the inherent resolution-sensitivity trade-off in conventional SPECT—this study presents the design and evaluation of the first full-ring self-collimation SPECT (SC-SPECT) scanner for small animal imaging. The system features four concentric detector rings and two interchangeable high-aperture-ratio tungsten collimator rings optimized for high-resolution (HR) and general-purpose (GP) imaging applications. Detector rings contain 480, 720, 960, and 1,200 evenly distributed GAGG(Ce) scintillators, each measuring 0.84 mm (tangential) × 6 mm (radial) × 20 mm (axial) and separated by 0.84-mm gaps to enable effective photon collimation. Inner detector rings and the collimator ring collectively provide collimation for photons reaching subsequent outer rings. Dual-end SiPM readouts facilitate axial depth-of-interaction measurements. Phantom and mouse studies are performed to assess the system’s resolution, sensitivity, and field-of-view volume, and SC-SPECT demonstrates generally superior performance compared with state-of-the-art small-animal SPECT systems. Mouse bone images using99mTc-MDP show CT-like resolution, clearly delineating detailed tracer uptake distributions within small structures such as mouse paws and skulls, indicating a significant technological advancement in small-animal SPECT imaging.

In the production enterprises, the distributed hybrid flow-shop scheduling problems widely exist in the actual production controlling and decision, especially in the production of steel and aluminum. Considering the uncertain processing time in the actual production environment, the constraint of deteriorated variable processing time is added in some problems. In this paper, the distributed hybrid flow-shop scheduling problem with deterioration effect (DHFSP-DE) is investigated. The framework of evolutionary multitasking memetic algorithm (MTMA) is designed to address the proposed model. In the proposed method, evolutionary transfer learning is utilized to communicate between two independent DHFSP-DE solvers. The implicit knowledge of the scheduling scheme can be transferred into other solvers to guide the evolution of the population. The memetic algorithm combines a strategy of local intensification with a population-based paradigm. These strategies can capture the implicit knowledge of DHFSP-DE. This paper makes the first attempt to work on the framework of evolutionary multitasking learning for DHFSP-DE problems. The experimental results on the different instances show the effectiveness and efficiency of the proposed MTMA algorithm.Note to Practitioners—The distributed hybrid flow-shop scheduling problem with deterioration effect (DHFSP-DE) is modeled based on the production process of aluminum. The DHFSP-DE is also widely used in the production process of various industries. As the processing time of the job is varied with time, the determined scheduling problem is transformed into a scheduling problem with deterioration effect. This transformation makes the problem even more complicated. The problems are more complex than static problems because they require greater computational dimensionality for evolutionary computation, resulting in the use of computational resources. An evolutionary multitasking memetic algorithm is designed to solve DHFSP-DEs cooperatively. The solutions of the solver can be transferred to other solvers through the mapping of DHFSP-DEs. The transferred solution can affect the evolution process. The efficiency and effectiveness of the proposed MTMA are verified by the comparison experiments. In addition, the MTMA framework can be applied to other scheduling problems.

Selecting appropriate algorithms for specific problems has become a significant challenge with the remarkable growth of heuristics and meta-heuristics. To address this challenge, an iterative greedy selection hyper-heuristic algorithm with linear population size reduction (LIGSHH) was proposed in this paper. Using an iterative greedy strategy to choose the high level of exploration, this heuristic selects the Low-Level Heuristics (LLHs) that best suit the current problem. Nine LLHs are specifically designed for continuous optimization problems. Additionally, the exploration and exploitation capabilities of the LIGSHH are balanced by reducing the population size linearly at different stages of the problem. The proposed LIGSHH algorithm and comparison algorithms are tested on the CEC2017 benchmark test suite, and the experimental results show that the LIGSHH algorithm outperforms other comparison algorithms.

This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine, which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Finally, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this article.

The blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDST), which has important ramifications in the modern industry, is investigated in this paper. The SDST/BFSP is extended from the BFSP, which included the setup times in processing times. However, the setup times in actual processing depend on the preceding and successive jobs in the processing order. Hence, the setup times are considered independently of processing times in this paper. The mixed-integer linear programming (MILP) model of SDST/BFSP is designed. Furthermore, a discrete whale optimization algorithm (DWOA) based on problem-specific knowledge is proposed to solve certain SDST/BFSP. Firstly, a construction heuristic that depends on the properties of the problem is designed to reduce the blocking time and idle times created by SDSTs. Secondly, the leading whales in DWOA are replaced by the critical factories in SDST/BFSP. Further, three different search strategies including the searching for prey, encircling prey, and bubble-net attacking prey are designed to improve the exploitation and exploration capability of the DWOA. The statistical and computational experimentation in an extensive benchmark testified that the DWOA outperforms the state-of-the-art algorithms regarding efficiency and significance in solving SDST/BFSP.

The distributed blocking flow-shop scheduling problem (DBFSP), which has been proven to be a strongly NP-hard problem, has important applications in a variety of industrial systems. In this paper, a self-adapting water wave optimization (SAWWO) algorithm is proposed to solve the blocking flow-shop scheduling problem with the criterion of minimizing the makespan. In SAWWO, the candidates are represented as discrete job permutations. Two heuristics are utilized to obtain the desirable initial solution. In the propagation phase, the self-adapting spatial dispersal operator is designed to balance the exploration and exploitation of SAWWO. Four local search methods are introduced to intensify the exploitation ability of the algorithm in the local region. Furthermore, the redesigned path-relinking method is presented as the modified refraction operator to help the algorithm jump out the local optimal. Additionally, the performance of the proposed algorithm is evaluated by comparing with five other state-of-the-art algorithms. The statistical results demonstrate the effectiveness of SAWWO for solving the DBFSP.