Employing effective optimization strategies in organizations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organizations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilized is a challenging problem as there are many factors that can impact the overall performance of the organization. We have developed a system that optimizes to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimization problems, which means there are many objectives to consider when optimizing, and there is much uncertainty in the environment. The latest version of the system uses a multiobjective genetic algorithm as its core optimization logic, with modifications such as fuzzy dominance rules (FDRs), to help overcome the issues associated with many-objective optimization. The system also utilizes genetically optimized type-2 fuzzy logic systems to better handle the uncertainty in the data and modeling. This paper shows the genetically optimized type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of ￡1 million a year, the reduction of over 2500爐 of CO2 and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.

中文翻译：

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iPatch：用于现场劳动力优化的多目标 2 类模糊逻辑系统


在拥有大量员工的组织中采用有效的优化策略可以对成本、收入和客户满意度产生明显的影响。对于雇用大量现场劳动力的组织（例如公用事业公司）来说尤其如此。确保每个员工都得到充分利用是一个具有挑战性的问题，因为有许多因素会影响组织的整体绩效。我们开发了一个优化系统，以确保我们在正确的地点、正确的时间拥有拥有正确技能的正确工程师。目前部署该系统是为了帮助解决现实世界的优化问题，这意味着优化时需要考虑很多目标，并且环境存在很多不确定性。该系统的最新版本使用多目标遗传算法作为其核心优化逻辑，并进行了模糊优势规则（FDR）等修改，以帮助克服与多目标优化相关的问题。该系统还利用遗传优化的 2 型模糊逻辑系统来更好地处理数据和建模中的不确定性。本文展示了遗传优化的 2 型模糊逻辑系统，在我们的应用中比清晰值实现产生更好的结果。我们还表明，通过使用 FDR，我们可以帮助解决标准 NSGA-II 优势计算中的弱点。这项工作的影响可以通过多种方式来衡量；每年可提高 100 万英镑的生产力效益，减少超过 2500 炉二氧化碳排放，并可能避免英国道路上 100 多人严重受伤和死亡。