• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.41-53
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• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.737-743
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• 2013

This study was to investigate the effects of balance and trunk repositioning sense through multisensorial training using visual cue deprivation in subacute stroke patients. Multisensorial training group practiced after visual deprivation, control group practiced in the same training except visual deprivation with thirty subjects. Both groups were conducted training programs on sixty minutes a day, five days a week during four weeks. The subjects were evaluated by static balance, dynamic balance and trunk repositioning sense in the pre-posttest. There was significant improvement by multisensorial training that static balance, dynamic balance and the trunk repositioning sense between pretest and post test (p<.05). There were significantly differences in the amount of change of dynamic balance, trunk repositioning sense between the two groups(p<.05). Through this study, multisensorial training suggest that is effective in the improvement of dynamic balance and trunk repositioning sense.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.4
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• pp.125-137
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• 2021

PURPOSE: The growing number of people exposed to a static sitting posture has resulted in an increase in people with a poor posture out of the optimally aligned posture because of the low awareness of a correct sitting posture. Learning the correct sitting posture by applying sensory feedback is essential because a poor posture has negative consequences for the spine. Therefore, this study examined the effects of the sensory feedback types on learning correct sitting posture. METHODS: Thirty-six healthy adult males were assigned to a visual feedback group, a tactile feedback group, and a visuotactile feedback group to learn the correct sitting posture by applying sensory feedback. The spine angle, muscle activity, and muscle thickness were measured in the sitting position using retro-reflexive markers, electromyography, and ultrasound immediately after, five minutes, and 10 minutes after intervention. RESULTS: The intervention time was significantly shorter in the visuotactile feedback group than the visual feedback group (p < .05). Compared to the pre-intervention, the repositioning error angles of the thoracic and lumbar vertebrae of all groups were reduced significantly immediately after intervention and after five minutes. After 10 minutes, there was a significant difference in the thoracic and lumbar repositioning error angles of the tactile feedback group and the visuotactile feedback group (p < .05). No significant difference was noted at any time compared to the pre-intervention in all groups (p > .05). CONCLUSION: The use of tactile and visuotactile feedback in intervention to correct the sitting posture is proposed.