1998 Fiscal Year Final Research Report Summary
MANAGEMENT OF RESOURCE CONSTRAINED MULTI-PROJECTS WITH REATTEMPT AT FAILURE
| Project/Area Number |
08458095
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | TOKYO INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
MORI Masao Graduate School of Decision Science and Technology, TOKYO INSTITUTE OF TECHNOLOGY Professor, 大学院・社会理工学研究科, 教授 (80016568)
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| Co-Investigator(Kenkyū-buntansha) |
IIDA Tetsuo Graduate School of Decision Science and Technology, TOKYO INSTITUTE OF TECHNOLOG, 大学院・社会理工学研究科, 助手 (20262305)
YAJIMA Yasutoshi Graduate School of Decision Science and Technology, TOKYO INSTITUTE OF TECHNOLOG, 大学院・社会理工学研究科, 助教授 (80231645)
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| Project Period (FY) |
1996 – 1998
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| Keywords | Project Scheduling / Resource Constrained / PERT Network / Genetic Algorithm / Stochastic PERT / Stochstic Programming / Multi-project / Activity with failure |
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| Research Abstract |
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.
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