scholarly journals A Theme Park Tourist Service System with a Personalized Recommendation Strategy

2018 ◽  
Vol 8 (10) ◽  
pp. 1745 ◽  
Author(s):  
Feng-Chi Yu ◽  
Pei-Chun Lee ◽  
Pei-Hsuan Ku ◽  
Sheng-Shih Wang
Keyword(s):  
Waiting Time ◽  
Service System ◽  
Field Testing ◽  
Mobile App ◽  
Personalized Recommendation ◽  
Theme Park ◽  
Location Awareness ◽  
Session Time ◽  
Recommendation Strategy ◽  
Tourist Service

In general, there exists numerous attractions installed in a theme park, and tourists in a theme park dynamically change their locations during a tour. Thus, a tourist may cope with the issues of selecting the attractions to visit while planning the tour route. This paper, based on the concept of location awareness, proposes a novel waiting time, called the personalized waiting time, to introduce a location-aware recommendation strategy. In addition, this paper presents an architecture of tourist service system using the proposed recommendation strategy to relieve the pressure on tourists and create the pleasant experience in their tours. The proposed location-based system consists of mobile app, ticket-reader, detecting/counting, and central subsystems, and the whole system was implemented in this study. We conducted numerous experiments and field testing results validated that the entire proposed system can correctly provide information, such as attraction introduction, recommended session time, estimated moving and waiting time, tour map, and the number of reservations. The system functions, including dynamical scheduling, attraction reservation, ticket verification, visitor detection, and visitor counting, also worked well.

Reducing Perceived Waiting Time in Theme Park Queues via an Augmented Reality Game

2020 ◽  
Vol 27 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Fabio Zambetta ◽  
William Raffe ◽  
Marco Tamassia ◽  
Florian ’Floyd‚ Mueller ◽  
Xiaodong Li ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Waiting Time ◽  
Theme Park ◽  
Augmented Reality Game

scholarly journals SIMULASI ANTRIAN PASIEN RAWAT INAP UNTUK MENGURANGI WAITING LIST VIP DI RUMAH SAKIT

2019 ◽  
Vol 8 (1) ◽  
pp. 13
Author(s):  
Frylie Frescia Falen ◽  
Subagyo Subagyo
Keyword(s):  
Emergency Room ◽  
Waiting Time ◽  
Net Present Value ◽  
Service System ◽  
Health Workers ◽  
Waiting List ◽  
Rate Of Return ◽  
Inpatient Service ◽  
Class 1 ◽  
Alternative Choice

Inefficiency of service and the waiting time can occur in inpatient services, especially in VIP rooms. Reduction in waiting lists for patients entering the VIP room is carried out by increasing the bed capacity and the allocation of health workers. The study was conducted by modeling the service system in hospitalization, which began from the patient service at the Emergency Room (IGD) until service in the VIP room of a hospital in Sukabumi. The model was built with 2 scenarios, scenario 1 was designed to improve the performance of the IGD by adding beds and health workers so that patients had an alternative choice in class 1 before getting service in the VIP room, and scenario 2 was designed by not changing the system in the emergency room, but adding 10 VIP rooms. Scenario 2 is a solution and can visually improve the VIP service system by reducing the waiting time by 50.55 hours, with the investment value of net present value (NPV) > 0 and internal rate of return (IRR) 32.2%, against the Minimum Attractive Rate of Return (MARR) 12% per year profitable. Scenario 2 by adding 10 VIP rooms, initially 25 units to 35 units, effectively being able to produce improvements in significantly reducing the waiting list originally from 50.94 hours to 39 minutes there was a decrease (99.23%), and could reduce the utility level or busyness of service in VIP was originally 93.49% to 87.19%. From the results of NPV analysis > 0, and IRR 32.2% assuming MARR 12% per year, constructing 10 VIP rooms in 5 years can give a profit of 20.2% per year. Scenario 2 can be used as the basis for hospitals in making inpatient service system policies, especially in VIP rooms.

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

scholarly journals The design of Scenic tourist service system

2018 ◽  
Vol 131 ◽  
pp. 1253-1259
Author(s):  
ChengHu Zhang
Keyword(s):  
Service System ◽  
Tourist Service

The Analysis of Behavioral Responses to Transit Information

2012 ◽  
Vol 253-255 ◽  
pp. 1431-1437
Author(s):  
Rui Sun ◽  
Hai Tao Yu ◽  
Yong Du ◽  
Song Lin Geng
Keyword(s):  
Waiting Time ◽  
Logit Model ◽  
Service System ◽  
Mode Selection ◽  
Information Service ◽  
Behavioral Responses ◽  
Travel Mode ◽  
Ordered Logit Model ◽  
Information Demand ◽  
Static Information

Road conditions, time for travelling and transfer information can be provided by transit information service system for the person travelling during the trip. The system can help people to select more reasonable routes, which can improve the travelling efficiency and attract more and more people using public transport. Based on the survey of transit information service in Beijing, this article will analysis the demand types of transit information. The effects of transit information-travel mode selection can be researched by ordered logit model. The results indicated that the passengers prefer static information at present. The information contains various elements for people travelling such as gender, main travel mode, average waiting time and the accuracy of transit information, demand types and releasing devices. These factors will impact passengers on selecting and changing their mode choice.

Improving Productivity in a Bank System by Using Computer Simulation

2014 ◽  
Vol 606 ◽  
pp. 259-263
Author(s):  
Milad Hatami ◽  
Seyed Mojib Zahraee ◽  
Milad Ahmadi ◽  
Saeed Rahimpour Golroudbary ◽  
Jafri Mohd Rohani
Keyword(s):  
Computer Simulation ◽  
Customer Satisfaction ◽  
Waiting Time ◽  
Service System ◽  
Simulation Software ◽  
Real Situation ◽  
The Real ◽  
Arena Simulation ◽  
Bank System ◽  
High Technologies

With advent of high technologies, simulation software becomes more applicable between organizations’ managers. Simulation can model the real situation on a visual program. It will make the understanding of system, properly. Nowadays in each organization, the main considered factor is how they can improve its services confidently. This study emphasize on customer satisfaction and reducing the waiting time for customers in a bank service system. The goal of this paper is applying ARENA simulation software for modeling the system and measuring the performances. In addition, three strategies are implemented that each strategy consists of several scenarios. 17 possible scenarios are compared to achieve all kind of results that can be imagined. It would be very helpful for manager to analyzes and compare the results then find the lowest and highest effective element for improvement.

scholarly journals Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App (Preprint)

2017 ◽  
Author(s):  
Kelly Glazer Baron ◽  
Jennifer Duffecy ◽  
Kathryn Reid ◽  
Mark Begale ◽  
Lauren Caccamo
Keyword(s):  
Sleep Duration ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Field Testing ◽  
Mobile App ◽  
Cardiovascular Disease Risk Factor ◽  
User Testing ◽  
Short Sleep Duration ◽  
Short Sleep ◽  
Populations At Risk

BACKGROUND Despite the high prevalence of short sleep duration (29.2% of adults sleep <6 hours on weekdays), there are no existing theory-based behavioral interventions to extend sleep duration. The popularity of wearable sleep trackers provides an opportunity to engage users in interventions. OBJECTIVE The objective of this study was to outline the theoretical foundation and iterative process of designing the “Sleep Bunny,” a technology-assisted sleep extension intervention including a mobile phone app, wearable sleep tracker, and brief telephone coaching. We conducted a two-step process in the development of this intervention, which was as follows: (1) user testing of the app and (2) a field trial that was completed by 2 participants with short sleep duration and a cardiovascular disease risk factor linked to short sleep duration (body mass index [BMI] >25). METHODS All participants had habitual sleep duration <6.5 hours verified by 7 days of actigraphy. A total of 6 individuals completed initial user testing in the development phase, and 2 participants completed field testing. Participants in the user testing and field testing responded to open-ended surveys about the design and utility of the app. Participants in the field testing completed the Epworth Sleepiness Scale and also wore an actigraph for a 1-week baseline period and during the 4-week intervention period. RESULTS The feedback suggests that users enjoyed the wearable sleep tracker and found the app visually pleasing, but they suggested improvements to the notification and reminder features of the app. The 2 participants who completed the field test demonstrated significant improvements in sleep duration and daytime sleepiness. CONCLUSIONS Further testing is needed to determine effects of this intervention in populations at risk for the mental and physical consequences of sleep loss.

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