A self-adapting genetic algorithm for project scheduling under resource constraints

2002 ◽  
Vol 49 (5) ◽  
pp. 433-448 ◽  
Author(s):  
S�nke Hartmann
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Resource Constraints

A New Genetic Algorithm for the RCPSP in Large Scale

2013 ◽  
Vol 4 (2) ◽  
pp. 29-40 ◽  
Author(s):  
Hossein Zoulfaghari ◽  
Javad Nematian ◽  
Nader Mahmoudi ◽  
Mehdi Khodabandeh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Project Scheduling ◽  
Large Scale ◽  
Resource Constraints ◽  
Research Area ◽  
New Method ◽  
Scheduling Problem ◽  
Academic Problem ◽  
Project Scheduling Problem

The Resource Constrained Project Scheduling Problem (RCPSP) is a well-studied academic problem that has been shown to be well suited to optimization via Genetic Algorithms (GA). In this paper, a new method will be designed that would be able to solve RCPSP. This research area is very common in industry especially when a set of activities needs to be finished as soon as possible subject to two sets of constraints, precedence constraints and resource constraints. The presented algorithm in this paper is used to solve large scale RCPSP and improves solutions. Finally, for comparing, results are reported for the most famous classical problems that are taken from PSPLIB.

Development of Hybrid Genetic Algorithms for the Resource Constrained Multi-Project Scheduling Problem

2005 ◽  
Author(s):  
Wenjian Liu ◽  
Jinghua Li
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Hybrid Genetic Algorithm ◽  
Second Phase ◽  
Scheduling Problem ◽  
Priority Rules ◽  
Resource Constrained ◽  
Chromosome Representation ◽  
Project Scheduling Problem

In multi-project environment, multiple projects share and compete for the limited resources to achieve their own goals. Besides resource constraints, there exist precedence constraints among activities within each project. This paper presents a hybrid genetic algorithm to solve the resource-constrained multi-project scheduling problem (RCMPSP), which is well known NP-hard problem. Objectives described in this paper are to minimize total project time of multiple projects. The chromosome representation of the problem is based on activity lists. The proposed algorithm was operated in two phases. In the first phase, the feasible schedules are constructed as the initialization of the algorithm by permutation based simulation and priority rules. In the second phase, this feasible schedule was optimized by genetic algorithm, thus a better approximate solution was obtained. Finally, after comparing several different algorithms, the validity of proposed algorithm is shown by a practical example.

Fuzzy Activity Network Method for Project Scheduling Under Resource Constraints

2007 ◽  
Vol 11 (8) ◽  
pp. 914-921
Author(s):  
Luong Duc Long ◽  
◽  
Ario Ohsato
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Fuzzy Numbers ◽  
Resource Constraints ◽  
Project Schedule ◽  
Project Duration ◽  
Activity Network ◽  
Decision Variables ◽  
Network Method ◽  
Critical Chain

In this article, a fuzzy activity network method is developed for project scheduling under resource constraints. Trapezoidal fuzzy numbers are used for estimating uncertain durations of activities, and then these fuzzy numbers are replaced by suitable crisp durations for project scheduling under resource constraints. In the next step, the critical chain is identified for determining the project duration, and uncertainties associated with activities are addressed by using feeding/project buffers to protect the project schedule from disturbances. For minimizing project duration, the proposed method considers both the suitable crisp durations and the start times of activities as decision variables. Hence, a new procedure based on genetic algorithm and priority heuristics is also developed for efficiently determining these decision variables. Furthermore, the method also considers selecting the best possible relationships between activities to minimize project duration. The proposed method using buffers makes it possible to improve project scheduling under resource constraints.

scholarly journals Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm

2021 ◽  
Vol 11 (12) ◽  
pp. 5531
Author(s):  
Linlin Xie ◽  
Yajiao Chen ◽  
Ruidong Chang
Keyword(s):  
Project Scheduling ◽  
Construction Projects ◽  
Resource Constraints ◽  
Construction Project ◽  
Engineering Application ◽  
Project Schedule ◽  
Scheduling Optimization ◽  
Implementation Method ◽  
Traditional Construction ◽  
Construction Project Scheduling

Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.

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