• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.4
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• pp.467-481
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• 2013

The study examined the main effect of the reward channel and reward time of customer loyalty programs for on-offline shopping channels; in addition, it investigated the interaction effect of the reward channels and merchandise as well as the interaction effect of the reward time and merchandise. An online apparel shopping web experiment was conducted with a 2 (reward channel: online channel reward vs. offline channel reward) ${\times}2$ (reward time: immediate vs. delayed) ${\times}2$ (merchandise: online channel product vs. offline channel products) between-subject factorial design. An online shopping channel was considered the core-shopping channel and a department store was considered the cross-shopping channel. Loyalty program value, core-channel loyalty and cross-channel loyalty were measured as dependent variables. A total of 845 shoppers (who had experiences in shopping in both channels) participated in the experiment. The results of the study revealed (1) the main effect of the reward channel on loyalty program value, core-channel loyalty and cross-channel loyalty [online>offline channel rewards], (2) the main effect of reward time on loyalty program value, core-channel loyalty and cross-channel loyalty [immediate>delayed reward], and (3) the interaction effect of the reward channel and merchandise on loyalty program value, core-channel loyalty, and cross-channel loyalty. (4) Finally the study found that loyalty program value affected cross-channel loyalty indirectly through core-channel loyalty. This study suggested diverse theoretical and managerial implications for multi-channel retailers.

• Culinary science and hospitality research
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• v.15 no.4
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• pp.313-330
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• 2009

This study aimed at inquiring into the effect of the reward program in an online web homepage, which is most widely applied as a sales promotion tool for the foodservice industry, on customer trust, commitment and loyalty. The result is summarized as follows. The effect of the reward characteristics within a reward program on their trust and commitment showed that first, for the reward time, both compensation for delaying time and immediate time has a significantly positive effect on their trust and commitment, suggesting that the reward type as well as the reward time of a reward program is an important factor for improving their trust and commitment. In particular, indirect reward was a significant in raising their commitment or loyalty continuously, and for the reward attribute, economic reward was a significant attribute in building their trust.

• Korean Journal of Child Studies
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• v.17 no.2
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• pp.221-233
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• 1996

The purpose of the present study was to investigate the effects of age, sex, cognitive style and reward presentation on delay of gratification. The subjects of this study were 120 children 4, 6 and 8 years of age attending preschool and an elementary school in Pusan. They were identified as impulsive or reflective according to their performance on Kagan's Matching Familiar Figures Test. The levels of reward presentation consisted of the reward which was presented physically and the reward which wasn't presented physically. Length of waiting time was recorded as the measure of maintenance of delay of gratification. The data of this study were analyzed with Two-way ANOVA, Duncan's Multiple Range Test. The major finding were as follows: (1) Delay time increased with age. (2) No sex difference is found in delay time. (3) Reflective children delayed longer than impulsive children in all age groups. (4) The reward which wasn't physically presented produced loner delay time than the reward which was physically presented in all age groups.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.13-20
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• 2012

This paper considers a revenue maximization problem on a single processor. Each job is identified as its processing time, initial reward, reward decreasing rate, and preferred start time. If the processor starts a job at time zero, revenue of the job is its initial reward. However, the revenue decreases linearly with the reward decreasing rate according to its processing start time till its preferred start time and finally its revenue is zero if it is started the processing after the preferred time. Our objective is to find the optimal sequence which maximizes the total revenue. For the problem, we characterize the optimal solution properties and prove the NP-hardness. Based upon the characterization, we develop a branch-and-bound algorithm for the optimal sequence and suggest five heuristic algorithms for efficient solutions. The numerical tests show that the characterized properties are useful for effective and efficient algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2202-2217
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• 2012

The transmission opportunities between nodes in Delay Tolerant Network (DTNs) are uncertain, and routing algorithms in DTNs often need nodes serving as relays for others to carry and forward messages. Due to selfishness, nodes may ask the source to pay a certain reward, and the reward may be varying with time. Moreover, the reward that the source obtains from the destination may also be varying with time. For example, the sooner the destination gets the message, the more rewards the source may obtain. The goal of this paper is to explore efficient ways for the source to maximize its total reward in such complex applications when it uses the probabilistic two-hop routing policy. We first propose a theoretical framework, which can be used to evaluate the total reward that the source can obtain. Then based on the model, we prove that the optimal forwarding policy confirms to the threshold form by the Pontryagin's Maximum Principle. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical framework. Furthermore, we demonstrate that the performance of the optimal forwarding policy with threshold form is better through extensive numerical results, which conforms to the result obtained by the Maximum Principle.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.101-109
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• 2002

A cooperative teaming has been taken to consolidate the autonomous motivation of students and to develop a desirable attitude in a mutual cooperative atmosphere. Some studies on the reward effect showed that the reward after the evaluation, in the processes of cooperative learning, worked on students' learning motive directly, and the group reward was effective in learning attitude and the individual reward in academic achievement, respectively. Assuming that the group reward and the individual reward are organized and applied as a complex reward, the effects of rewards will appear, this study examined the effect of the complex reward on academic achievement and teaming attitude. For this study. 2 classes were randomly selected out of a elementary school in Gwangju and the teaming unit was based on chapter 4「The structure and function of plants」 in the 5-1 elementary Science textbook. This research has been done for 4 weeks after the students learned STAD for 8 weeks previously. The learning attitude was examined in pre and post tests, and the academic achievement was inspected twice at 2-week intervals after the pre test. The results were analysized by the SAS program In the case of academic achievement, both groups showed a significant improvement(p<.05). The experimental group showed no significant improvement in the first test, compared with the control group(p>.05), but after 4 weeks, it showed a significant improvement in the second test, compared with the control group(p<.05). From this result, it is identified that the reward should be done for a long time and the individual reward of the complex reward is successful in improving academic achievement. However, in the case of learning attitude, there was no meaningful difference in both groups(p>.05). But the control group showed a significant improvement, compared with the experimental group(p<.05). According to this result, it is indicated that the group reward only is more effective in improving learning attitude and complex reward can decrease the individual competition in experimental group.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.165-172
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• 2005

In recently, Popova and Wu(1999) proved a theorem which presents the long-run average fuzzy reward per unit time. In this note, we improve this result. Indeed we will show uniform convergence of a renewal reward processes with respect to the level ${\alpha}$ modeled as a fuzzy random variables.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.1
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• pp.12-21
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• 2003

In this paper, we propose an on-line scheduling algorithm with the goal of maximizing the total reward of IRIS (Increasing Reward with Increasing Service) real-time tasks that have reward functions and arrive dynamically into the system. We focus on enhancing the performance of scheduling algorithm, which W.: based on the following two main ideas. First, we show that the problem to maximize the total reward of dynamic tasks can also be solved by the problem to find minimum of maximum derivatives of reward functions. Secondly, we observed that only a few of scheduled tasks are serviced until a new task arrives, and the rest tasks are rescheduled with the new task. Based on our observation, the Proposed algorithm doesn't schedules all tasks in the system at every scheduling print, but a part of tasks. The performance of the proposed algorithm is verified through the simulations for various cases. The simulation result showed that the computational complexity of proposed algorithm is$O(N_2)$ in the worst case which is equal to those of the previous algorithms, but close to O(N) on the average.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.