【外文翻译】采用遗传算法选择以风险为基础的虚拟企业伙伴.rar
【外文翻译】采用遗传算法选择以风险为基础的虚拟企业伙伴,摘要动态联盟和虚拟企业(ve)是全球制造主要组成部分。为了确保成功克服ve的关键问题是尽量减少选择风险合作伙伴，并确保到期的项目。本文描述和建模以风险为基础的合作伙伴选择问题,基于效率不高的候选人,解决问题的方法是有效地降低。通过使用的特点，考虑问题，项目调度，以规则为基础的遗传算法（注册商标算法）与嵌入式开发项目调度...
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摘要
动态联盟和虚拟企业(VE)是全球制造主要组成部分。为了确保成功克服VE的关键问题是尽量减少选择风险合作伙伴，并确保到期的项目。本文描述和建模以风险为基础的合作伙伴选择问题,基于效率不高的候选人,解决问题的方法是有效地降低。通过使用的特点，考虑问题，项目调度，以规则为基础的遗传算法（注册商标算法）与嵌入式开发项目调度来解决这个问题。表现该算法像是一个问题所表现出施工中遇到的一个体育场和实验大小不同的问题。此次测试的结果表明现实生活的算法能力。
Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise
Abstract
Dynamic alliance and virtual enterprise (VE) are essential components of global manufacturing. Minimizing risk in partner selection and ensuring the due date of a project are the key problems to overcome in VE, in order to ensure success. In this paper, a risk-based partner selection problem is described and modeled. Based on the concept of inefficient candidate, the solution space of the problem is reduced effectively. By using the characteristics of the problem considered and the knowledge of project scheduling, a rule-based genetic algorithm (R-GA) with embedded project scheduling is developed to solve the problem. The performance of this algorithm is demonstrated by a problem encountered in the construction of a stadium station and the experimental problems of different sizes. The results of this trial demonstrate the real life capability of the algorithm.
动态联盟和虚拟企业(VE)是全球制造主要组成部分。为了确保成功克服VE的关键问题是尽量减少选择风险合作伙伴，并确保到期的项目。本文描述和建模以风险为基础的合作伙伴选择问题,基于效率不高的候选人,解决问题的方法是有效地降低。通过使用的特点，考虑问题，项目调度，以规则为基础的遗传算法（注册商标算法）与嵌入式开发项目调度来解决这个问题。表现该算法像是一个问题所表现出施工中遇到的一个体育场和实验大小不同的问题。此次测试的结果表明现实生活的算法能力。
Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise
Abstract
Dynamic alliance and virtual enterprise (VE) are essential components of global manufacturing. Minimizing risk in partner selection and ensuring the due date of a project are the key problems to overcome in VE, in order to ensure success. In this paper, a risk-based partner selection problem is described and modeled. Based on the concept of inefficient candidate, the solution space of the problem is reduced effectively. By using the characteristics of the problem considered and the knowledge of project scheduling, a rule-based genetic algorithm (R-GA) with embedded project scheduling is developed to solve the problem. The performance of this algorithm is demonstrated by a problem encountered in the construction of a stadium station and the experimental problems of different sizes. The results of this trial demonstrate the real life capability of the algorithm.
