scholarly journals WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Lin Sun ◽  
Qi Zhu
Keyword(s):  
Heterogeneous Networks ◽  
User Satisfaction ◽  
Mobile User ◽  
User Cost ◽  
Analytic Hierarchy ◽  
Wifi Networks ◽  
Q Learning ◽  
Reward Function ◽  
Wifi Offloading ◽  
Topsis Technique

This paper proposes a WiFi offloading algorithm based on Q-learning and MADM (multiattribute decision making) in heterogeneous networks for a mobile user scenario where cellular networks and WiFi networks coexist. The Markov model is used to describe the changes of the network environment. Four attributes including user throughput, terminal power consumption, user cost, and communication delay are considered to define the user satisfaction function reflecting QoS (Quality of Service), and Q-learning is used to optimize it. Through AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) in MADM, the intrinsic connection between each attribute and the reward function is obtained. The user uses Q-learning to make offloading decisions based on current network conditions and their own offloading history, ultimately maximizing their satisfaction. The simulation results show that the user satisfaction of the proposed algorithm is better than the traditional WiFi offloading algorithm.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

scholarly journals A deep q-learning-based optimization of the inventory control in a linear process chain

2020 ◽  
Author(s):  
M.-A. Dittrich ◽  
S. Fohlmeister
Keyword(s):  
Inventory Control ◽  
Data Exchange ◽  
Linear Process ◽  
Process Chain ◽  
Production Networks ◽  
Initial State ◽  
Q Learning ◽  
Reward Function ◽  
Decision Optimization ◽  
Process Chains

AbstractDue to growing globalized markets and the resulting globalization of production networks across different companies, inventory and order optimization is becoming increasingly important in the context of process chains. Thus, an adaptive and continuously self-optimizing inventory control on a global level is necessary to overcome the resulting challenges. Advances in sensor and communication technology allow companies to realize a global data exchange to achieve a holistic inventory control. Based on deep q-learning, a method for a self-optimizing inventory control is developed. Here, the decision process is based on an artificial neural network. Its input is modeled as a state vector that describes the current stocks and orders within the process chain. The output represents a control vector that controls orders for each individual station. Furthermore, a reward function, which is based on the resulting storage and late order costs, is implemented for simulations-based decision optimization. One of the main challenges of implementing deep q-learning is the hyperparameter optimization for the training process, which is investigated in this paper. The results show a significant sensitivity for the leaning rate α and the exploration rate ε. Based on optimized hyperparameters, the potential of the developed methodology could be shown by significantly reducing the total costs compared to the initial state and by achieving stable control behavior for a process chain containing up to 10 stations.

scholarly journals Use of the PVM Method Computed in Vector Space of Increments in Decision Aiding Related to Urban Development

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 446 ◽  
Author(s):  
Marek Kannchen ◽  
Paweł Ziemba ◽  
Mariusz Borawski
Keyword(s):  
Urban Development ◽  
Vector Space ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Preference Ranking ◽  
Analytic Hierarchy ◽  
Vector Method ◽  
Decision Aiding ◽  
Topsis Technique ◽  
Hierarchy Process

The paper presents a possibility to use a new PVM-VSI (Preference Vector Method computed in Vector Space of Increments) method in making decisions that demand that different variants should be considered, while being evaluated with respect to different criteria. Hence, knowledge about them is a must, and that knowledge is not necessarily available quantitatively, whereas the very evaluation should be relatively objective; that is, independent from the decision maker’s preferences or opinions. The paper presents the use of the PVM-VSI method in support decisions related to urban development—to rank projects submitted for implementation within the framework of a citizen budget. The ranking will make it feasible to determine which of the submitted projects will have the dominant influence on the town’s sustainable development, and, subsequently, which ones should be presented to citizens as the better ones out of the projects submitted, and to compare the method mentioned with methods used in similar decision-making problems in the past: Fuzzy AHP (Analytic Hierarchy Process), Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and Fuzzy PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluation).

scholarly journals A deep Q-learning portfolio management framework for the cryptocurrency market

2020 ◽  
Vol 32 (23) ◽  
pp. 17229-17244
Author(s):  
Giorgio Lucarelli ◽  
Matteo Borrotti
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Dynamic Decision Making ◽  
Financial Assets ◽  
Management Framework ◽  
Q Learning ◽  
Reward Function ◽  
Dynamic Portfolio ◽  
Definition Of ◽  
Dynamic Portfolio Optimization

AbstractDeep reinforcement learning is gaining popularity in many different fields. An interesting sector is related to the definition of dynamic decision-making systems. A possible example is dynamic portfolio optimization, where an agent has to continuously reallocate an amount of fund into a number of different financial assets with the final goal of maximizing return and minimizing risk. In this work, a novel deep Q-learning portfolio management framework is proposed. The framework is composed by two elements: a set of local agents that learn assets behaviours and a global agent that describes the global reward function. The framework is tested on a crypto portfolio composed by four cryptocurrencies. Based on our results, the deep reinforcement portfolio management framework has proven to be a promising approach for dynamic portfolio optimization.

Gender Differences in User Satisfaction of Mobile Touch Screen Interfaces: University Students’ Service Sites

2018 ◽  
Vol 15 (06) ◽  
pp. 1950003
Author(s):  
Ayman N. Alkhaldi ◽  
Ahmed Al-Sa’di
Keyword(s):  
User Interfaces ◽  
User Satisfaction ◽  
Quantitative Research ◽  
Service Providers ◽  
Rapid Development ◽  
Mobile User ◽  
Learning System ◽  
Mobile Service ◽  
Online Help ◽  
Future Work

The rapid development of mobile user interfaces for students’ websites and the constant utilization of such interfaces by students have witnessed a significant upsurge in growth. However, mobile service providers may lack valuable feedback on user satisfaction, particularly for Arabic users, because the sites are designed and implemented without students’ participation. This paper empirically investigates the user satisfaction of a mobile banner system for the University of Ha’il in Saudi Arabia. Users’ satisfaction was evaluated across six scales: overall reactions, screens, terminology and system information, learning, system capabilities, and technical manuals and online help. A quantitative research method was utilized, involving a questionnaire survey of 235 students. We found that female students have significant concerns about user satisfaction. The paper proposes theoretical and practical implications for future work.

EXPLOITING IMAGE CONTENT IN LOCATION-BASED SHOPPING RECOMMENDER SYSTEMS FOR MOBILE USERS

2010 ◽  
Vol 09 (05) ◽  
pp. 759-778 ◽  
Author(s):  
O. O. OLUGBARA ◽  
S. O. OJO ◽  
M. I. MPHAHLELE
Keyword(s):  
User Satisfaction ◽  
User Study ◽  
Learning To Rank ◽  
User Profile ◽  
Similarity Score ◽  
Mobile Users ◽  
Image Content ◽  
Analytic Hierarchy ◽  
Content Similarity ◽  
Hierarchy Process

This paper demonstrates how image content can be used to realize a location-based shopping recommender system for intuitively supporting mobile users in decision making. Generic Fourier Descriptors (GFD) image content of an item was extracted to exploit knowledge contained in item and user profile databases for learning to rank recommendations. Analytic Hierarchy Process (AHP) was used to automatically select a query item from a user profile. Single Criterion Decision Ranking (SCDR) and Multiple-Criteria Decision-Ranking (MCDR) techniques were compared to study the effect of multidimensional ratings of items on recommendations effectiveness. The SCDR and MCDR techniques are, respectively, based on Image Content Similarity Score (ICSS) and Relative Ratio (RR) aggregating function. Experimental results of a real user study showed that an MCDR system increases user satisfaction and improves recommendations effectiveness better than an SCDR system.

scholarly journals APPLICATION OF TOPSIS METHOD IN PRIORITIZATION OF HIGHWAY BRIDGES FOR SEISMIC RETROFITTING

2014 ◽  
Vol 6 (3) ◽  
pp. 114-123 ◽  
Author(s):  
Ali Yousefi ◽  
Mohd Sanusi S. Ahamad ◽  
Taksiah A. Majid
Keyword(s):  
Decision Making ◽  
Highway Bridges ◽  
Seismic Retrofitting ◽  
Topsis Method ◽  
Analytic Hierarchy ◽  
Highway Network ◽  
Order Preference ◽  
Multiple Alternative ◽  
Topsis Technique ◽  
Alternative Routes

The process of bridges seismic retrofitting in the highway network is extremely costly and time consuming. In addition, the constraint on resources prevents the retrofitting of all the bridges at the same time. Besides, the bridges must be prioritized with simultaneous consideration of multiple criteria, including technical and socioeconomic aspect. This study intends to identify the major criteria and consider them simultaneously for prioritization of highway bridges additionally provides an effective technique for weighing these criteria. In this research, TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method as a Multi-Criteria Decision-Making (MCDM) model is applied. TOPSIS method enables decision makers to deal with problems involving a large number of alternatives (bridges) and criteria. This methodology reduces multiple alternative (bridge) performances into a single value (ranking score) to facilitate the decision-making process for determination of the most suitable bridges for retrofitting. Suggested criteria include structural vulnerability, seismic hazard, anticipated service life, average daily traffic, interface with other lifelines, alternative routes and bridge importance. Moreover, relative importance (weight) of the criteria is assigned using Analytic Hierarchy Process (AHP) technique. The proposed method is applied to a real case of the Isfahan highway network.

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