scholarly journals A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lan Xu ◽  
Yiliu Tu ◽  
Yuting Zhang
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Task Assignment ◽  
Operational Mode ◽  
Assignment Model ◽  
Simulation Results ◽  
Grasshopper Optimization ◽  
Integrated Logistics ◽  
And Task ◽  
Biobjective Programming

A framework for the algorithm-based CL platform is established, based on which, the operational mode of it is described in detail. An integrated logistics task assignment model is built to optimally match logistics service resources and task of large scale in the algorithm-based CL. Particularly, an improved grasshopper optimization-based bitarget optimization algorithm (GROBO) is proposed to solve the biobjective programming model for service matching in CL. The case of Linyi small commodity logistics is taken as an application. Simulation results show that the proposed GROBO provides better solutions regarding to searching efficiency and stability in solving the model.

scholarly journals Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 587 ◽  
Author(s):  
Jun Wang ◽  
Pengcheng Luo ◽  
Xinwu Hu ◽  
Xiaonan Zhang
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Programming Model ◽  
Task Assignment ◽  
Target Information ◽  
Interval Numbers ◽  
Weighted Arithmetic ◽  
Assignment Model ◽  
Arithmetic Averaging Operator ◽  
Multiple Uncertainties

Uncertainty should be taken into account when establishing multiobjective task assignment models for multiple unmanned combat aerial vehicles (UCAVs) due to errors in the target information acquired by sensors, implicit preferences of the commander for operational objectives, and partially known weights of sensors. In this paper, we extend the stochastic multicriteria acceptability analysis-2 (SMAA-2) method and combine it with integer linear programming to achieve multiobjective task assignment for multi-UCAV under multiple uncertainties. We first represent the uncertain target information as normal distribution interval numbers so that the values of criteria (operational objectives) concerned can be computed based on the weighted arithmetic averaging operator. Thus, we obtain multiple criteria value matrices for each UCAV. Then, we propose a novel aggregation method to generate the final criteria value matrix based on which the holistic acceptability indices are computed by the extended SMAA-2 method. On this basis, we convert the task assignment model with uncertain parameters into an integer linear programming model without uncertainty so as to implement task assignment using the integer linear programming method. Finally, we conduct a case study and demonstrate the feasibility of the proposed method in solving the multiobjective task assignment problem multi-UCAV under multiple uncertainties.

scholarly journals A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowei Fu ◽  
Peng Feng ◽  
Bin Li ◽  
Xiaoguang Gao
Keyword(s):  
Large Scale ◽  
Task Assignment ◽  
Small Scale ◽  
Resource Assignment ◽  
Assignment Algorithm ◽  
Uav Swarm ◽  
Feature Weight ◽  
Bundle Algorithm ◽  
Aerial Vehicle ◽  
Simulation Results

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

scholarly journals Self-Supervised Deep Low-Rank Assignment Model for Prototype Selection

2018 ◽  
Author(s):  
Xingxing Zhang ◽  
Zhenfeng Zhu ◽  
Yao Zhao ◽  
Deqiang Kong
Keyword(s):  
Large Scale ◽  
Task Assignment ◽  
Representation Learning ◽  
Low Rank ◽  
Prototype Selection ◽  
Weak Supervision ◽  
Large Scale Data ◽  
Assignment Model ◽  
Uncertain Distribution ◽  
Task Assignment Problem

Prototype selection is a promising technique for removing redundancy and irrelevance from large-scale data. Here, we consider it as a task assignment problem, which refers to assigning each element of a source set to one representative, i.e., prototype. However, due to the outliers and uncertain distribution on source, the selected prototypes are generally less representative and interesting. To alleviate this issue, we develop in this paper a Self-supervised Deep Low-rank Assignment model (SDLA). By dynamically integrating a low-rank assignment model with deep representation learning, our model effectively ensures the goodness-of-exemplar and goodness-of-discrimination of selected prototypes. Specifically, on the basis of a denoising autoencoder, dissimilarity metrics on source are continuously self-refined in embedding space with weak supervision from selected prototypes, thus preserving categorical similarity. Conversely, working on this metric space, similar samples tend to select the same prototypes by designing a low-rank assignment model. Experimental results on applications like text clustering and image classification (using prototypes) demonstrate our method is considerably superior to the state-of-the-art methods in prototype selection.

Simulation Tool for Assignment Models: SIMASI

2014 ◽  
Vol 13 (8) ◽  
pp. 4723-4728
Author(s):  
Pratiksha Saxena ◽  
Smt. Anjali
Keyword(s):  
Optimization Procedure ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Integrated Simulation ◽  
Assignment Model ◽  
Classical Models ◽  
Simulation Results ◽  
User Friendly ◽  
Assignment Models

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples. 

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

scholarly journals A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

2021 ◽  
Author(s):  
Zahra Homayouni ◽  
Mir Saman Pishvaee ◽  
Hamed Jahani ◽  
Dmitry Ivanov
Keyword(s):  
Supply Chains ◽  
Large Scale ◽  
Programming Model ◽  
Supply Chain Design ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Economic Uncertainty ◽  
Cap And Trade ◽  
Sustainable Logistics ◽  
Goal Programming Model

AbstractAdoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

scholarly journals The Flexible Operation of Coal Power and Its Renewable Integration Potential in China

2019 ◽  
Vol 11 (16) ◽  
pp. 4424 ◽  
Author(s):  
Chunning Na ◽  
Huan Pan ◽  
Yuhong Zhu ◽  
Jiahai Yuan ◽  
Lixia Ding ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Large Scale ◽  
Renewable Integration ◽  
Flexible Resources ◽  
Feasible Option ◽  
Coal Power ◽  
Simulation Results ◽  
Flexible Operation

At present time, China’s power systems face significant challenges in integrating large-scale renewable energy and reducing the curtailed renewable energy. In order to avoid the curtailment of renewable energy, the power systems need significant flexibility requirements in China. In regions where coal is still heavily relied upon for generating electricity, the flexible operations of coal power units will be the most feasible option to face these challenges. The study first focused on the reasons why the flexible operation of existing coal power units would potentially promote the integration of renewable energy in China and then reviewed the impacts on the performance levels of the units. A simple flexibility operation model was constructed to estimate the integration potential with the existing coal power units under several different scenarios. This study’s simulation results revealed that the existing retrofitted coal power units could provide flexibility in the promotion of the integration of renewable energy in a certain extent. However, the integration potential increment of 20% of the rated power for the coal power units was found to be lower than that of 30% of the rated power. Therefore, by considering the performance impacts of the coal power units with low performances in load operations, it was considered to not be economical for those units to operate at lower than 30% of the rated power. It was believed that once the capacity share of the renewable energy had achieved a continuously growing trend, the existing coal power units would fail to meet the flexibility requirements. Therefore, it was recommended in this study that other flexible resources should be deployed in the power systems for the purpose of reducing the curtailment of renewable energy. Furthermore, based on this study’s obtained evidence, in order to realize a power system with high proportions of renewable energy, China should strive to establish a power system with adequate flexible resources in the future.

scholarly journals Control method for the stable operation of distributed inverters following the introduction of large-scale renewable energy to a remote island grid

Clean Energy ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 196-207
Author(s):  
Shoichi Sato ◽  
Yasuhiro Noro
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
Control Method ◽  
Synchronous Generator ◽  
Stable Operation ◽  
Battery Energy Storage Systems ◽  
Simulation Results ◽  
Battery Energy ◽  
Systems Simulation ◽  
Different Characteristics

Abstract The introduction of large-scale renewable energy requires a control system that can operate multiple distributed inverters in a stable way. This study proposes an inverter control method that uses information corresponding to the inertia of the synchronous generator to coordinate the operation of battery energy storage systems. Simulation results for a system with multiple inverters applying the control method are presented. Various faults such as line-to-line short circuits and three-phase line-to-ground faults were simulated. Two fault points with different characteristics were compared. The voltage, frequency and active power quickly returned to their steady-state values after the fault was eliminated. From the obtained simulation results, it was verified that our control method can be operated stably against various faults.

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