This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known ...This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known as a technique used to both shorten the makespan and observe the due date under limited resources;the max-plus linear representation is an approach for modeling discrete event systems as production systems and project scheduling. If a contention arises within a single resource, we must resolve it by appending precedence relations. Thus, the resolution framework is reduced to a combinatorial optimization. If we aim to obtain the exact optimal solution, the maximum computation time is longer than 10 hours for 20 jobs. We thus experiment with Simulated Annealing (SA) and Genetic Algorithm (GA) to obtain an approximate solution within a practical time. Comparing the two methods, the former was beneficial in computation time, whereas the latter was better in terms of the performance of the solution. If the number of tasks is 50, the solution using SA is better than that using GA.展开更多
文摘为解决工程施工进度管控关键链技术应用中存在的缓冲区计算方法粗略,缓冲区监控方法脱离实际施工的问题,通过引入风险控制系数、资源影响系数、工序复杂系数、工序位置系数以及环境系数五个指标,运用层次分析法和CRITIC(Criteria Importance Through Intercriteria Correlation)客观赋权法构建缓冲区优化模型;将缓冲区消耗率和项目工作链完成进度百分比相关联,建立了缓冲区动态监控机制。运用缓冲区优化模型和动态监控机制改进关键链技术,将改进关键链技术应用于泵站工程施工进度管理。结果表明:该方法所得到的缓冲区尺寸较为合理,可以实现缩短计划工期和实现缓冲区动态监控,为水利工程施工进度管控提供借鉴。
文摘This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known as a technique used to both shorten the makespan and observe the due date under limited resources;the max-plus linear representation is an approach for modeling discrete event systems as production systems and project scheduling. If a contention arises within a single resource, we must resolve it by appending precedence relations. Thus, the resolution framework is reduced to a combinatorial optimization. If we aim to obtain the exact optimal solution, the maximum computation time is longer than 10 hours for 20 jobs. We thus experiment with Simulated Annealing (SA) and Genetic Algorithm (GA) to obtain an approximate solution within a practical time. Comparing the two methods, the former was beneficial in computation time, whereas the latter was better in terms of the performance of the solution. If the number of tasks is 50, the solution using SA is better than that using GA.
