A priority-based genetic algorithm approach for solving multiple alternative project scheduling problems with resource constraints and variable activity times

2008 ◽  
Author(s):  
Seren Ozmehmet Tasan ◽  
Mitsuo Gen
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Scheduling Problems ◽  
Variable Activity ◽  
Multiple Alternative ◽  
Genetic Algorithm Approach ◽  
A Priority ◽  
Activity Times

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

2009 ◽  
Vol 419-420 ◽  
pp. 633-636 ◽  
Author(s):  
James C. Chen ◽  
Wun Hao Jaong ◽  
Cheng Ju Sun ◽  
Hung Yu Lee ◽  
Jenn Sheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Total Tardiness ◽  
Scheduling Problems ◽  
Test Bed ◽  
Resource Constrained ◽  
Multiple Resources ◽  
Practical Constraints

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

scholarly journals A bi-objective genetic algorithm approach to risk mitigation in project scheduling

2008 ◽  
Vol 112 (1) ◽  
pp. 202-216 ◽  
Author(s):  
Murat Kılıç ◽  
Gündüz Ulusoy ◽  
Funda Sivrikaya Şerifoğlu
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Risk Mitigation ◽  
Genetic Algorithm Approach
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