资源受限多项目调度问题的两阶段算法

在资源受限项目调度问题中,将可更新资源进一步拓展为具有胜任力差异的人力资源,建立考虑胜任力差异的人力资源受限多目标项目调度问题模型.该模型是对传统多模式资源约束项目调度问题更接近研发项目群实际的扩展.针对模型提出两阶段优化算法,第1阶段是项目时序约束优化阶段,采用蚁群算法(ACO)进行任务列表的优化求解,通过对信息素增量规则的改进、串联进度生成机制(SSGS)及资源冲突消解策略的使用,使蚁群算法的求解效率和质量得以提高;第2阶段是资源约束优化阶段,以第1阶段求得的优化任务列表为输入,逐项对人力资源约束进行核查与调整,最终生成项目调度的优化方案.数值实验表明,考虑胜任力差异的数学优化模型更符合研发项目群管理实践,同时两阶段算法在求解质量方面具有良好性能.

Two-stage algorithm for resource-constrained multi-project scheduling problem

In resource-constrained project scheduling problem, renewable resource can be expanded into human resource with competency differences, and a flexible resource-constrained project scheduling problem with competency difference is proposed, which is a practical extension close to research and development (R&D) program from the traditional multi-mode resource-constrained project scheduling problem. In order to address the problem, a two-stage algorithm is proposed. In the first stage of precedence constraints satisfactory optimization, a revised ant colony optimization(ACO) algorithm is presented to obtain the feasible activity sequence. In order to accelerate the convergence efficiency and quality, a revised pheromone increment updating operator of ACO with the combination of the serial schedule generation scheme(SSGS) and the resource conflict resolution strategy are used. At the second stage of resource-constraints satisfactory optimization, the obtained optimum precedence activity sequence is taken as the input, and the resource capacity is examined and adjusted one by one until the optimal scheduling solution is obtained. Experimental results show that the optimization model considering the difference of competency is more suitable for the R&D program management practice, and two-stage algorithm can solve the model effectively.