MANAGEMENT OF RESOURCE CONSTRAINED MULTI-PROJECTS WITH REATTEMPT AT FAILURE

在本研究中，我们考虑了资源受限的项目调度问题，其中每个活动在执行中都有几种不同的方式（模式）。每种模式都具有指定数量的资源和不同的持续时间。在执行一个活动时，我们必须考虑到资源的限制来选择一个合适的模式，并且该活动执行的成功概率会随着选择的模式而变化。针对上述问题，我们研究了以下6个课题：(1)首先给出了一个使项目总成功概率最大化的有效算法。(2)考虑了在每个活动执行完后，根据性能测试结果需要重新尝试任务的情况，并给出了一种使项目总预期完成时间最小化的启发式随机方法。通过大量的数值实验，该算法比现有的调度规则给出了更好的评价。(3)针对上述问题，我们提出了一种遗传算法，该算法在几乎相等的计算时间内给出了上述启发式的优解。(4)我们还开发了禁忌搜索算法，它被认为是现代启发式中的优秀算法。然而，通过数值实验表明，遗传算法优于禁忌搜索。(5)我们证明了类似的遗传算法对于具有总资源约束的多模式、多项目问题也是有效的。(6)我们考虑了具有随机活动持续时间的随机PERT网络问题，并给出了一个很好的算法来评估预期完成时间的上界和下界。

In this study, we consider resource constrained project scheduling problems, in which each activity has a several different ways (modes) in their execution. Each mode is carried with specified quantities of resources and with a different duration. On executing an activity we have to select a suitable mode considering constraints on resources, And further success probability in performance of the activity is changing according to a mode selected.We studied the following 6 subjects on above problems :(1) We first give an efficient algorithm to maximize total success probability of the project.(2) We consider the case that a reattempt task is required based on result of performance test done just after execution of each activity, and we give a heuristic stochastic approach minimizing total expected completion time of the project. Through many numerical experiments the proposed algorithm gives much more nice evaluation than existing dispatching rules.(3) For the above stated problem, we propose a Genetic Algorithm which brings superior solutions to the above heuristic in almost equivalent computing time.(4) We also exploit Tabu Search Algorithm, which are said to be excellent among modern heuristics. However for the problem it is shown that Genetic Algorithm is superior to Tabu Search through numerical experiments.(5) We show that the analogous Genetic Algorithm is also effective for a multi-mode, multi-project problem with total resource constraints.(6) We consider a stochastic PERT network problem with random activity duration time and give a nice algorithm to evaluate good lower and upper bounds for the expected completion time.