scholarly journals An Efficient Simulation Algorithm for Resource-Constrained Project Scheduling Problem

2014 ◽  
Vol 8 (1) ◽  
pp. 9-13
Author(s):  
L. Peng ◽  
P. Wuliang
Keyword(s):  
Project Scheduling ◽  
Large Scale ◽  
Exact Algorithms ◽  
Scheduling Problem ◽  
Simulation Algorithm ◽  
Resource Constrained ◽  
Resource Conflict ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
Project Scheduling Problem

Since Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP-hard problem, it is difficult to solve large-scale practical cases by using traditional exact algorithms. Genetic algorithm (GA) is a kind of intelligent algorithm for approximate optimization, which can ascertain global optimization or suboptimal solution within a reasonable time. This article presented a new simulation algorithm by using GA for solving Resource-Constrained Project Scheduling Problem. In the algorithm, the activity adjacency matrix and priority-based preemptive resource conflict resolution are used to prevent chromosome from generating infeasible schedules. Finally, the method was tested with an actual machine and electricity project case, and the results show that the presented method is efficient and practical for practical project cases.

scholarly journals Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Daniel Morillo ◽  
Federico Barber ◽  
Miguel A. Salido
Keyword(s):  
Energy Consumption ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Exact Methods ◽  
Approximate Methods ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
Project Scheduling Problem ◽  
Metaheuristic Techniques

This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.

Application of Three Meta-Heuristic Algorithms for Maximizing the Net Present Value of a Resource-Constrained Project Scheduling Problem with Respect to Delay Penalties

2016 ◽  
Vol 3 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Masoud Rabbani ◽  
Azadeh Arjmand ◽  
Mohammad Mahdi Saffar ◽  
Moeen Sammak Jalali
Keyword(s):  
Project Scheduling ◽  
Large Scale ◽  
Net Present Value ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Cash Flows ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms - Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) - to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale problems.

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