Objectives: To evaluate the responsiveness of generic and mapped preference-based measures based on the anchor of global change in health condition of colorectal cancer (CRC) patients. Study Design and Setting: A base...
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Objectives: To evaluate the responsiveness of generic and mapped preference-based measures based on the anchor of global change in health condition of colorectal cancer (CRC) patients. Study Design and Setting: A baseline sample of 333 Chinese CRC patients was recruited between September 2009 and July 2010 and was surveyed prospectively at 6-month follow-up. preference-based indices were derived from the generic SF-6D measure (SF-6D(Direct)), from the Short Form-12 (SF-6D(SF-12)), and mapped from the condition-specific Functional Assessment of Cancer Therapy-Colorectal (SF-6D(FACT-C)). Responsiveness of three measures was assessed using standardized effect size, standardized response mean, responsiveness statistic, and receiver operating characteristic (ROC) curve analysis. Results: The SF-6D(SF-12) and SF-6D(FACT-C) indices were significantly more responsive to detect positive changes than the SF-6D(Direct) index in improved groups. In worsened group, the SF-6D(Direct) and SF-6D(FACT-C) indices showed significant decline from baseline to 6-month follow-up. The areas under the ROC curve for SF-6D(Direct) and SF-6D(FACT-C) indices were not statistically different from 0.7. The SF-6D(FACT-C) index was more responsive to changes in health status compared with other indices. Conclusion: Direct SF-6D measure was more responsive than mapped preference-based measures in improved group but the direction was reversed in worsened group. The use of a preference-based index mapped from a condition-specific measure captures both negative and positive important changes among CRC. (C) 2014 Elsevier Inc. All rights reserved.
In this paper, minimizing machine idle time and minimizing earliness-tardiness penalties are considered as two objectives in advanced planning and scheduling (APS). The APS problem is formulated as a mixed integer pro...
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ISBN:
(纸本)9781424448692
In this paper, minimizing machine idle time and minimizing earliness-tardiness penalties are considered as two objectives in advanced planning and scheduling (APS). The APS problem is formulated as a mixed integer programming model. Constraints including precedence, alternative machine, capacity, and setup and transition times are taken into account. A preference-based adaptive genetic algorithm is applied to solve the model. Numerical experiments are performed to illustrate the effectiveness and efficiency of the developed algorithm.
A preference-based Non-dominated Sorting Genetic Algorithm (PNSGA) is introduced to optimize multi-objective problems. PNSGA adopts the technique of the decision maker, which can combine Pareto dominance with partial ...
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ISBN:
(纸本)9781424421138
A preference-based Non-dominated Sorting Genetic Algorithm (PNSGA) is introduced to optimize multi-objective problems. PNSGA adopts the technique of the decision maker, which can combine Pareto dominance with partial preference information. And the preferable relationship is defined based on Pareto dominance and goal function. The algorithm is utilized to optimize a novel multi-objective model of the dynamic economic dispatch in power system. Experimental results demonstrate the good validity of the proposed algorithm.
A preference-based Non-dominated Sorting Genetic Algorithm (PNSGA) is introduced to optimize multi-objective problems. PNSGA adopts the technique of the decision maker, which can combine Pareto dominance with partial ...
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A preference-based Non-dominated Sorting Genetic Algorithm (PNSGA) is introduced to optimize multi-objective problems. PNSGA adopts the technique of the decision maker, which can combine Pareto dominance with partial preference information. And the preferable relationship is defined based on Pareto dominance and goal function. The algorithm is utilized to optimize a novel multi-objective model of the dynamic economic dispatch in power system. Experimental results demonstrate the good validity of the proposed algorithm.
As ubiquitous computing technology convergences into many industrial domains, most museums want to apply this technique to their domain. However, most ubiquitous museums merely provide the simplest service giving visi...
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ISBN:
(纸本)9783642164439
As ubiquitous computing technology convergences into many industrial domains, most museums want to apply this technique to their domain. However, most ubiquitous museums merely provide the simplest service giving visitors only static information of artifact. To resolve limitations of the existing ubiquitous museums, we had proposed Visitor preferencebased Museum Viewing Search Algorithm that provides the best path for visitors to reflect their preferences. However, since the Visitor preferencebased Museum Viewing Search Algorithm did not consider abnormal situations that occur while visitors look at the exhibit. So when abnormal situations occur, the exhibition may cause congestion problems that may make visitors feel very uncomfortable. In this paper, we propose an efficient congestion control algorithm to solve these problems. This algorithm automatically re-finds proper alternative paths for avoiding congestion resulting from the abnormal ones occurring during the museum viewing. The proposed algorithm improves comfortable museum viewing services by preventing congestion in advance when exceptional conditions occur. For the experiment of the proposed algorithm, we show that the algorithm can provide the best path without congestion exhibition.
Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing th...
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ISBN:
(纸本)9781595931863
Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto-optimal solution is also an important task which has received a lukewarm attention so far. In this paper, we combine one such preference-based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set solutions near the reference points can be found parallely. We propose a modified EMO procedure based on the elitist non-dominated sorting GA or NSGA-II. On two-objective to 10-objective optimization problems, the modified NSGA-II approach shows its efficacy in finding an adequate set of Pareto-optimal points. Such procedures will provide the decision-maker with a set of solutions near her/his preference so that a better and a more reliable decision can be made.
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