Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tightly constrained large-scale solution space and requires well balanced diversification and intensification in search. In Variable Depth Neighbourhood Search, large neighbourhood depth prevents the search from trapping into local optima prematurely, while small depth provides thorough exploitation in local areas. Considering the multi-dimensional solution structure and tight constraints in OPVRPTW, a Variable-Depth Adaptive Large Neighbourhood Search (VD-ALNS) algorithm is proposed in this paper. Contributions of four tailored destroy operators and three repair operators at variable depths are investigated. Comparing to existing methods, VD-ALNS makes a good trade-off between exploration and exploitation, and produces promising results on both small and large size benchmark instances

Optimisation Models and Algorithms for Real-life Transportation Routing and Scheduling Problems

This thesis investigates the transportation routing and scheduling problems derived from the container transportation in Ningbo Port, which is the second largest port in China. In the container transportation problem, a fleet of trucks are scheduled to accommodate a sequence of container transshipment requests among multiple container terminals. This problem closely relates to the classic vehicle routing problem with time windows (VRPTW) in which the customers are routed to be serviced with minimal cost, satisfying capacity and time constraints. Facing the fast growing throughput in the port, an efficient automatic routing and scheduling system is urgently needed. In the Ningbo Port problem, there are mainly two types of transshipment requests (long-distance and short-distance) need to be scheduled to optimise fleet usage, while the transportation cost for the two types of tasks are different. As a result, to promote the vehicle utility and reduce operational cost at the same time, not only producing solutions of high quality are pursued in this research but also advanced problem modelling methods. The research starts from studying the VRPTW to understand the characteristics of vehicle routing problems and the features of solution methodologies in detail. An improved variable neighbourhood search algorithm is developed. With the proposed new compounded neighbourhoods, the algorithm combines variable neighbourhood search and variable-depth neighbourhood search. In this single solution-based metaheuristic method, two objectives are optimised simultaneously, generating a number of new non-dominated solutions on benchmark instances. This research explores the ways of balancing diversification and intensification in metaheuristic search, providing experience and guidance for addressing the real-life Ningbo Port container transportation problem. After the preliminary research on the VRPTW, the Ningbo Port problem then is studied in two steps. At the first stage, the short-haul problem in the port districts is considered. The mathematical model of Vehicle Routing Problem with Time Windows and Open routes (VRPTW-O) is established in this study, providing a more efficient scheduling scheme for Ningbo Port company. Two metaheuristic algorithms are implemented for this new problem, while both of them outperform the state-of-the-art methods for similar problems. Different construction heuristics are compared on diverse instances, providing recommendation heuristics for real-world scenarios. At the second stage of the Ningbo Port problem research, the routing and scheduling of long-haul requests related to inland dry ports are combined with the short-haul requests. By introducing artificial depot to the problem, both types of transshipment tasks are integrated into one vehicle routing problem model, called Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW). The driver salary cost is taken into consideration in this model to optimise the operational cost of the company, leading to a bi-objective VRP variant. To provide a generic solution methodology for multi-objective VRPs instead of a problem-specific algorithm, a selection perturbation hyper-heuristic is implemented in this study. The experimental results show that the proposed method performs well in both the real-life Ningbo Port problem and the classic VRPTW

A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

Based on a real-life container transport problem, a model of Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is proposed in this paper. In a wide planning horizon, which is divided into a number of shifts, a fixed number of trucks are scheduled to complete container transportation tasks between terminals subject to time constraints. In this problem, the routes traveled by trucks are open, as returning to the starting depot is not required in every single shift but every two shifts.Our study shows that it is unrealistic to address this large scale and nonlinearly constrained problem with exact search methods. A Reinforcement Learning Based Variable Neighbourhood Search algorithm (VNSRLS) is developed for OPVRPTW. The initial solution is constructed with an urgency level-based insertion heuristic, while different insertion selection strategies are compared. In the local search phase of VNS-RLS, reinforcement learning is used to guide the search, adjusting the probabilities of operators being invoked adaptively according to the change of generated solutions’ feasibility and quality. In addition, the impact of sampling neighbourhood space in single solution-based algorithms is also investigated. Three indicators are designed in the proposed Sampling module to set the starting configuration of local search.Experiment results on different sizes of real and artificial benchmark instances show that, the proposed Sampling scheme and feasibility indicator decrease the infeasible rate during the search. However, Sampling’s contribution to solution quality improvement is not significant in this single solution-based algorithm. Comparing to the exact search and two state-of-the-art algorithms, VNS-RLS produces promising result

Reducing Communication for Split Learning by Randomized Top-k Sparsification

Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model generalize and converge better. This is done by selecting top-k elements with a large probability while also having a small probability to select non-top-k elements. Empirical results show that compared with other communication-reduction methods, our proposed randomized top-k sparsification achieves a better model performance under the same compression level.Comment: Accepted by IJCAI 202

Characterization of the soybean KRP gene family reveals a key role for GmKRP2a in root development

Kip-related proteins (KRPs), as inhibitory proteins of cyclin-dependent kinases, are involved in the growth and development of plants by regulating the activity of the CYC-CDK complex to control cell cycle progression. The KRP gene family has been identified in several plants, and several KRP proteins from Arabidopsis thaliana have been functionally characterized. However, there is little research on KRP genes in soybean, which is an economically important crop. In this study, we identified nine GmKRP genes in the Glycine max genome using HMM modeling and BLASTP searches. Protein subcellular localization and conserved motif analysis showed soybean KRP proteins located in the nucleus, and the C-terminal protein sequence was highly conserved. By investigating the expression patterns in various tissues, we found that all GmKRPs exhibited transcript abundance, while several showed tissue-specific expression patterns. By analyzing the promoter region, we found that light, low temperature, an anaerobic environment, and hormones-related cis-elements were abundant. In addition, we performed a co-expression analysis of the GmKRP gene family, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) set enrichment analysis. The co-expressing genes were mainly involved in RNA synthesis and modification and energy metabolism. Furthermore, the GmKRP2a gene, a member of the soybean KRP family, was cloned for further functional analysis. GmKRP2a is located in the nucleus and participates in root development by regulating cell cycle progression. RNA-seq results indicated that GmKRP2a is involved in cell cycle regulation through ribosome regulation, cell expansion, hormone response, stress response, and plant pathogen response pathways. To our knowledge, this is the first study to identify and characterize the KRP gene family in soybean

Can oblique lateral interbody fusion (OLIF) create more lumbosacral lordosis in lumbar spine surgery than minimally invasive transforaminal interbody fusion (MIS-TLIF)?

ObjectiveTo compare the differences in the correction effect for lumbosacral lordosis and clinical outcomes between OLIF with/without posterior pedicle screw fixation (PSF) and MIS-TLIF through a retrospective cohort study.MethodThere were 98 consecutive patients originally enrolled for the study, but 15 patients were excluded due to intraoperative endplate injury or osteotomy performed for severe spinal deformity. Thus, 83 patients included in this study (36 males and 47 females, mean age 65.8 years) underwent single to three-segment OLIF (including OLIF + PSF and OLIF Standalone) or MIS-TLIF surgery from 2016 to 2018. The operation time, bleeding and blood transfusion, fusion rate, complication, pre-and postoperative visual analogue scale (VAS), Oswestry Disability Index (ODI) were evaluated. In addition, radiological parameters including lumbosacral lordosis (LL), fused segment lordosis (FSL), anterior disc height (ADH) and posterior disc height (PDH) were measured. The clinical outcomes, LL, FSL, ADH and PDH restored and were compared between the OLIF group, OLIF subgroups and MIS-TLIF group.ResultsThe average operation time and intraoperative bleeding were significantly less in the OLIF group than in the MIS-TLIF group (163 ± 68 vs. 233 ± 79 min, 116 ± 148 vs. 434 ± 201 ml, P < 0.001). There was no statistically significant difference between the OLIF group and the MIS-TLIF group in VAS and ODI improvements, fusion rate, complication, LL and FSL correction (P > 0.05). The ADH and PDH increases in the OLIF group were more than that in MIS-TLIF group (P < 0.001). The correction of LL was significantly more in the OLIF + PSF group than in the MIS-TLIF group (9.9 ± 11.1 vs. 4.2 ± 6.1deg, P = 0.034).ConclusionOLIF and MIS-TLIF are both safe and effective procedures, capable of restoring lumbosacral lordosis and disc height partly. Combined with PSF, OLIF can achieve a better correction effect of lumbosacral lordosis than MIS-TLIF

Switchable metasurface absorber used for enabling reconfigurable power angular spectrum in reverberation chamber

Abstract The reverberation chamber (RC) provides a fast and repeatable method for over-the-air (OTA) testing of wireless devices. Moreover, the RC-based method can reduce the OTA testing cost to a great extent. But the defect of the RC is also obvious. Compared to a multi-probe anechoic chamber, the channel spatial characteristics in RC are uncontrollable. The device under test in an RC usually sees a statistically isotropic channel and there is a strong impediment to control the channel in RC, which constrains the RC’s OTA applications. In this paper, we propose a method to realize reconfigurable RC enabling arbitrary channel power angular spectrum (PAS) by using a switchable metasurface absorber. Specifically, the unit cell of the metasurface can be switched between the reflection and absorption states by providing different bias voltages. By mounting the switchable metasurface absorber on the RC’s inside walls, the boundary conditions of the RC in the covered area could be switched electronically. Consequently, the channel’s PAS can be controlled by changing the local states of the switchable metasurface absorber. As a proof of concept, a prototype of the switchable metasurface absorber is made and comprehensive experiments are conducted to verify the effectiveness of the switchable metasurface applied to enable reconfigurable PAS in RC.</jats:p

LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery

Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective data for quantifying various indices in forest and evaluating ecosystem changes. However, employing single-source remote sensing spectral or LiDAR waveform data poses limitations for LAI inversion, making the integration of multi-source remote sensing data a trend. Currently, the fusion of active and passive remote sensing data for LAI inversion primarily relies on empirical models, which are mainly constructed based on field measurements and do not provide a good explanation of the fusion mechanism. In this study, we aimed to estimate LAI based on physical model using both spectral imagery and LiDAR waveform, exploring whether data fusion improved the accuracy of LAI inversion. Specifically, based on the physical model geometric-optical and radiative transfer (GORT), a fusion strategy was designed for LAI inversion. To ensure inversion accuracy, we enhanced the data processing by introducing a constraint-based EM waveform decomposition method. Considering the spatial heterogeneity of canopy/ground reflectivity ratio in regional forests, calculation strategy was proposed to improve this parameter in inversion model. The results showed that the constraint-based EM waveform decomposition method improved the decomposition accuracy with an average 12% reduction in RMSE, yielding more accurate waveform energy parameters. The proposed calculation strategy for the canopy/ground reflectivity ratio, considering dynamic variation of parameter, effectively enhanced previous research that relied on a fixed value, thereby improving the inversion accuracy that increasing on the correlation by 5% to 10% and on R2 by 62.5% to 132.1%. Based on the inversion strategy we proposed, data fusion could effectively be used for LAI inversion. The inversion accuracy achieved using both spectral and LiDAR data (correlation=0.81, R2 = 0.65, RMSE=1.01) surpassed that of using spectral data or LiDAR alone. This study provides a new inversion strategy for large-scale and high-precision LAI inversion, supporting the field of LAI research