A branch-and-bound algorithm for the resource-constrained project scheduling problem

2000 ◽  
Vol 52 (3) ◽  
pp. 413-439 ◽  
Author(s):  
U. Dorndorf ◽  
E. Pesch ◽  
T. Phan-Huy
DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 198-207 ◽  
Author(s):  
Daniel Morillo Torres ◽  
Luis Fernando Moreno Velasquez ◽  
Francisco Javier Díaz Serna

This paper addresses the Resource Constrained Project Scheduling Problem (RCPSP). For its solution, a hybrid methodology, which uses a Branch and Bound basic algorithm with dominance rules, is developed and implemented, and is combined with four deterministic heuristics whose objective is to prune the search tree branches, taking into account the iterations available and, at the same time, to minimize the probability of discarding branches that contain optimal solutions. Essentially, these strategies allow the allocation of most iterations to the most promissory regions in an organized manner using only subsets with similar or the same characteristics as those of the optimal solutions at each level of the tree, thus assuring a broad search within the feasible region and, simultaneously, a good exploitation by the selective use of the subsets by level. Finally, the developed algorithm performance is analyzed by solving some of the problems of the PSPLIB test library.


2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Behrouz Afshar-Nadjafi ◽  
Zeinab Khalaj ◽  
Esmaeil Mehdizadeh

We study resource constrained project scheduling problem with respect to resource leveling as objective function and allowance of preemption in activities. The branch and bound algorithms proposed in previous researches on resource leveling problem do not consider preemption. So, representing a model for the problem, a branch and bound algorithm is proposed. This algorithm can handle preemption in resource leveling problem. Comparing the resource leveling problem and the preemptive resource leveling problem, it is observed that considering preemption in the problem leads to better results in the objective function. This improvement imposes additional time to solve the problem. Coding the algorithm in MATLAB and checking it on the projects with 8 and 10 activities, results show that the proposed algorithm is efficient.


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