A Graph Partition Model for Cross Docking Schedule Problem

2012 ◽  
Vol 601 ◽  
pp. 470-475
Author(s):  
Jun Huang ◽  
Hai Bo Wang
Keyword(s):  
Graph Theory ◽  
Schedule Problem ◽  
Oriented Graph ◽  
Graph Partition ◽  
Partition Model ◽  
Cross Docking ◽  
The Cross

Cross docking has been received a great attention in logistics field. This study uses graph theory to solve cross docking schedule problem. A specific example is given and a node oriented, graph partition model is introduced to exploit a new way in dealing with the cross docking schedule problem.

Truck scheduling problems in the cross docking network

2019 ◽  
Vol 33 (3) ◽  
pp. 420
Author(s):  
Piya Hengmeechai ◽  
Takashi Irohara ◽  
Warisa Wisittipanich
Keyword(s):  
Scheduling Problems ◽  
Cross Docking ◽  
Truck Scheduling ◽  
The Cross

scholarly journals Big Data Driven Oriented Graph Theory Aided tagSNPs Selection for Genetic Precision Therapy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3746-3754
Author(s):  
Tianshuo Cong ◽  
Jingjing Wang ◽  
Sanghai Guan ◽  
Yifei Mu ◽  
Tong Bai ◽  
...  
Keyword(s):  
Big Data ◽  
Graph Theory ◽  
Oriented Graph ◽  
Data Driven ◽  
Precision Therapy ◽  
Selection For

Costs and benefits of using cross-docking in the retail supply chain

2019 ◽  
Vol 47 (4) ◽  
pp. 412-432 ◽  
Author(s):  
Yassine Benrqya
Keyword(s):  
Supply Chain ◽  
Cost Model ◽  
Costs And Benefits ◽  
Actual Case ◽  
Content Type ◽  
Cross Docking ◽  
The Cross ◽  
The Right ◽  
The Cost

Purpose The purpose of this paper is to investigate the costs/benefits of implementing the cross-docking strategy in a retail supply chain context using a cost model. In particular, the effects of using different typologies of cross-docking compared to traditional warehousing are investigated, taking into consideration an actual case study of a fast-moving consumer goods (FMCG) company and a major French retailer. Design/methodology/approach The research is based on a case study of an FMCG company and a major French retailer. The case study is used to develop a cost model and to identify the main cost parameters impacted by implementing the cross-docking strategy. Based on the cost model, a comparison of the main cost factors characterizing four different configurations is made. The configurations studied are, the traditional warehousing strategy (AS-IS configuration, the reference configuration for comparison), where both retailers and suppliers keep inventory in their warehouses; the cross-docking pick-by-line strategy, where inventory is removed from the retailer warehouse and the allocation and sorting are performed at the retailer distribution centre (DC) level (TO-BE1 configuration); the cross-docking pick-by-store strategy, where the allocation and sorting are done at the supplier DC level (TO-BE2 configuration); and finally a combination of cross-docking pick-by-line strategy and traditional warehousing strategy (TO-BE3 configuration). Findings The case study provides three main observations. First, compared to traditional warehousing, cross-docking with sorting and allocation done at the supplier level increases the entire supply chain cost by 5.3 per cent. Second, cross-docking with allocation and sorting of the products done at the retailer level is more economical than traditional warehousing: a 1 per cent reduction of the cost. Third, combining cross-docking and traditional warehousing reduces the supply chain cost by 6.4 per cent. Research limitations/implications A quantitative case study may not be highly generalisable; however, the findings form a foundation for further understanding of the reconfiguration of a retail supply chain. Originality/value This paper fills a gap by proposing a cost analysis based on a real case study and by investigating the costs and benefits of implementing different configurations in the retail supply chain context. Furthermore, the cost model may be used to help managers choose the right distribution strategy for their supply chain.

scholarly journals Receptor flexibility in molecular cross-docking

2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Ligand Binding ◽  
Computational Cost ◽  
Md Simulations ◽  
Binding Pocket ◽  
Receptor Flexibility ◽  
Cross Docking ◽  
Internal Cavities ◽  
The Cross

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"

scholarly journals The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chao Shen ◽  
Xueping Hu ◽  
Junbo Gao ◽  
Xujun Zhang ◽  
Haiyang Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Detailed Knowledge ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Link Type ◽  
Cross Docking ◽  
Calculation Results ◽  
The Cross ◽  
The Impact

AbstractStructure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and docking pose ranks as the features can achieve the best performance, according to the validation through the random splitting or refined-core splitting and the testing on the re-docked or cross-docked poses. Besides, it is found that, despite the significant decrease of the performance for the threefold clustered cross-validation, the inclusion of the Vina energy terms can effectively ensure the lower limit of the performance of the models and thus improve their generalization capability. Furthermore, our calculation results also highlight the importance of the incorporation of the cross-docked poses into the training of the SFs with wide application domain and high robustness for binding pose prediction. The source code and the newly-developed cross-docking datasets can be freely available at https://github.com/sc8668/ml_pose_prediction and https://zenodo.org/record/5525936, respectively, under an open-source license. We believe that our study may provide valuable guidance for the development and assessment of new machine learning-based SFs (MLSFs) for the predictions of protein–ligand binding poses.

scholarly journals Receptor flexibility in molecular cross-docking

2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Ligand Binding ◽  
Computational Cost ◽  
Md Simulations ◽  
Binding Pocket ◽  
Receptor Flexibility ◽  
Cross Docking ◽  
Internal Cavities ◽  
The Cross

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"

scholarly journals Improved Harmony Search Algorithm for Truck Scheduling Problem in Multiple-Door Cross-Docking Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zhanzhong Wang ◽  
Yue Lu ◽  
Liying Zhao ◽  
Ningbo Cao
Keyword(s):  
Mixed Integer Linear Programming ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Mixed Integer ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Improved Harmony Search ◽  
The Cross ◽  
Improved Harmony Search Algorithm

The key of realizing the cross docking is to design the joint of inbound trucks and outbound trucks, so a proper sequence of trucks will make the cross-docking system much more efficient and need less makespan. A cross-docking system is proposed with multiple receiving and shipping dock doors. The objective is to find the best door assignments and the sequences of trucks in the principle of products distribution to minimize the total makespan of cross docking. To solve the problem that is regarded as a mixed integer linear programming (MILP) model, three metaheuristics, namely, harmony search (HS), improved harmony search (IHS), and genetic algorithm (GA), are proposed. Furthermore, the fixed parameters are optimized by Taguchi experiments to improve the accuracy of solutions further. Finally, several numerical examples are put forward to evaluate the performances of proposed algorithms.

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