MANAGEMENT OF RESOURCE CONSTRAINED MULTI-PROJECTS WITH REATTEMPT AT FAILURE

在这项研究中，我们考虑资源受限的项目调度问题，其中每个活动有几个不同的方式（模式）在他们的执行。每种模式都携带有指定数量的资源和不同的持续时间。在执行一个活动时，必须考虑资源的约束条件，选择一个合适的执行模式，而且活动执行的成功概率也会随着模式的选择而改变，本文针对上述问题研究了以下六个方面的问题：（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)我们考虑的情况下，需要一个重试任务的执行后，每个活动的性能测试的结果，我们给出了一个启发式的随机方法，最小化总的预期完成时间的项目。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)对于上述问题，我们提出了一种遗传算法，它带来了上级的解决方案，上述启发式在几乎相同的计算时间。(4)我们还采用了禁忌搜索算法，这是被认为是优秀的现代算法。然而，对于该问题，通过数值实验表明，遗传算法是上级优于禁忌搜索。(5)我们表明，类似的遗传算法也是有效的多模式，多项目的总资源约束问题。(6)我们考虑了一个具有随机作业持续时间的随机PERT网络问题，并给出了一个很好的算法来计算期望完工时间的上界和下界。

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.