Solving MRCPSP by a Hybrid Genetic Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 2369-2372
Author(s):  
Yan Li ◽  
Lloyd Gibson
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Computational Results ◽  
Nonrenewable Resource ◽  
Project Scheduling Problem ◽  
Checking Procedure ◽  
Multi Mode

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. To solve the problem, we apply a hybrid genetic algorithm, which makes use of nonrenewable resource feasibility checking procedure, local search based mutation and topological sort procedure. We present detailed computational results for the MRCPSP, which reveal that our procedure is effective in solving the problem.

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.

Sign in / Sign up

Export Citation Format

Share Document