40 research outputs found
Computing (R, S) policies with correlated demand
This paper considers the single-item single-stocking non-stationary
stochastic lot-sizing problem under correlated demand. By operating under a
nonstationary (R, S) policy, in which R denote the reorder period and S the
associated order-up-to-level, we introduce a mixed integer linear programming
(MILP) model which can be easily implemented by using off-theshelf optimisation
software. Our modelling strategy can tackle a wide range of time-seriesbased
demand processes, such as autoregressive (AR), moving average(MA),
autoregressive moving average(ARMA), and autoregressive with autoregressive
conditional heteroskedasticity process(AR-ARCH). In an extensive computational
study, we compare the performance of our model against the optimal policy
obtained via stochastic dynamic programming. Our results demonstrate that the
optimality gap of our approach averages 2.28% and that computational
performance is good
Mathematical programming heuristics for nonstationary stochastic inventory control
This work focuses on the computation of near-optimal inventory policies for a
wide range of problems in the field of nonstationary stochastic inventory control.
These problems are modelled and solved by leveraging novel mathematical programming
models built upon the application of stochastic programming bounding
techniques: Jensen's lower bound and Edmundson-Madanski upper bound.
The single-item single-stock location inventory problem under the classical
assumption of independent demand is a long-standing problem in the literature
of stochastic inventory control. The first contribution hereby presented is the
development of the first mathematical programming based model for computing
near-optimal inventory policy parameters for this problem; the model is then
paired with a binary search procedure to tackle large-scale problems.
The second contribution is to relax the independence assumption and investigate
the case in which demand in different periods is correlated. More specifically,
this work introduces the first stochastic programming model that captures Bookbinder
and Tan's static-dynamic uncertainty control policy under nonstationary
correlated demand; in addition, it discusses a mathematical programming heuristic
that computes near-optimal policy parameters under normally distributed
demand featuring correlation, as well as under a collection of time-series-based
demand process.
Finally, the third contribution is to consider a multi-item stochastic inventory
system subject to joint replenishment costs. This work presents the first mathematical
programming heuristic for determining near-optimal inventory policy
parameters for this system. This model comes with the advantage of tackling
nonstationary demand, a variant which has not been previously explored in the
literature.
Unlike other existing approaches in the literature, these mathematical programming
models can be easily implemented and solved by using off-the-shelf
mathematical programming packages, such as IBM ILOG optimisation studio
and XPRESS Optimizer; and do not require tedious computer coding.
Extensive computational studies demonstrate that these new models are competitive
in terms of cost performance: in the case of independent demand, they
provide the best optimality gap in the literature; in the case of correlated demand,
they yield tight optimality gap; in the case of nonstationary joint replenishment
problem, they are competitive with state-of-the-art approaches in the literature
and come with the advantage of being able to tackle nonstationary problems
Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform
In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems
Computing non-stationary (s,S) policies using mixed integer linear programming
This paper addresses the single-item single-stocking location stochastic lot
sizing problem under the policy. We first present a mixed integer
non-linear programming (MINLP) formulation for determining near-optimal policy parameters. To tackle larger instances, we then combine the
previously introduced MINLP model and a binary search approach. These models
can be reformulated as mixed integer linear programming (MILP) models which can
be easily implemented and solved by using off-the-shelf optimisation software.
Computational experiments demonstrate that optimality gaps of these models are
around of the optimal policy cost and computational times are
reasonable
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
Generalizable person re-identification (Re-ID) is a very hot research topic
in machine learning and computer vision, which plays a significant role in
realistic scenarios due to its various applications in public security and
video surveillance. However, previous methods mainly focus on the visual
representation learning, while neglect to explore the potential of semantic
features during training, which easily leads to poor generalization capability
when adapted to the new domain. In this paper, we propose a Multi-Modal
Equivalent Transformer called MMET for more robust visual-semantic embedding
learning on visual, textual and visual-textual tasks respectively. To further
enhance the robust feature learning in the context of transformer, a dynamic
masking mechanism called Masked Multimodal Modeling strategy (MMM) is
introduced to mask both the image patches and the text tokens, which can
jointly works on multimodal or unimodal data and significantly boost the
performance of generalizable person Re-ID. Extensive experiments on benchmark
datasets demonstrate the competitive performance of our method over previous
approaches. We hope this method could advance the research towards
visual-semantic representation learning. Our source code is also publicly
available at https://github.com/JeremyXSC/MMET
Mining the candidate genes of rice panicle traits via a genome-wide association study
Panicle traits are important for improving the panicle architecture and grain yield of rice. Therefore, we performed a genome-wide association study (GWAS) to analyze and determine the genetic determinants of five panicle traits. A total of 1.29 million single nucleotide polymorphism (SNP) loci were detected in 162 rice materials. We carried out a GWAS of panicle length (PL), total grain number per panicle (TGP), filled grain number per panicle (FGP), seed setting rate (SSR) and grain weight per panicle (GWP) in 2019, 2020 and 2021. Four quantitative trait loci (QTLs) for PL were detected on chromosomes 1, 6, and 9; one QTL for TGP, FGP, and GWP was detected on chromosome 4; two QTLs for FGP were detected on chromosomes 4 and 7; and one QTL for SSR was detected on chromosome 1. These QTLs were detected via a general linear model (GLM) and mixed linear model (MLM) in both years of the study period. In this study, the genomic best linear unbiased prediction (BLUP) method was used to verify the accuracy of the GWAS results. There are nine QTLs were both detected by the multi-environment GWAS method and the BLUP method. Moreover, further analysis revealed that three candidate genes, LOC_Os01g43700, LOC_Os09g25784, and LOC_Os04g47890, may be significantly related to panicle traits of rice. Haplotype analysis indicated that LOC_Os01g43700 and LOC_Os09g25784 are highly associated with PL and that LOC_Os04g47890 is highly associated with TGP, FGP, and GWP. Our results offer essential genetic information for the molecular improvement of panicle traits. The identified candidate genes and elite haplotypes could be used in marker-assisted selection to improve rice yield through pyramid breeding