A New Meta-Heuristic PSO Algorithm for Resource Constraint Project Scheduling Problem

2012 ◽  
pp. 381-392 ◽  
Author(s):  
Tuli Bakshi ◽  
Arindam Sinharay ◽  
Bijan Sarkar ◽  
Subir K. Sanyal
Keyword(s):  
Project Scheduling ◽  
Pso Algorithm ◽  
Resource Constraint ◽  
Scheduling Problem ◽  
Project Scheduling Problem

The R-PSO algorithm solving multi-skill resource-constrained project scheduling problem

2021 ◽  
pp. 71-82
Author(s):  
Dang Quoc Huu
Keyword(s):  
Project Scheduling ◽  
Optimal Solution ◽  
Simulated Data ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Feasible Schedule ◽  
Approximate Methods ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is a combinational optimization problem with many applications in science and practical areas. This problem aims to find out the feasible schedule for the completion of projects and workflows that is minimal duration or cost (or both of them - multi objectives). The researches show that MS-RCPSP is classified into NP-Hard classification, which could not get the optimal solution in polynomial time. Therefore, we usually use approximate methods to carry out the feasible schedule. There are many publication results for that problem based on evolutionary methods such as GA, Greedy, Ant, etc. However, the evolutionary algorithms usually have a limitation that is fallen into local extremes after a number of generations. This paper will study a new method to solve the MS-RCPSP problem based on the Particle Swarm Optimization (PSO) algorithm that is called R-PSO. The new improvement of R-PSO is re-assigning the resource to execute solution tasks. To evaluate the new algorithm's effectiveness, the paper conducts experiments on iMOPSE datasets. Experimental results on simulated data show that the proposed algorithm finds a better schedule than related works.

Non-dominated Sorting Genetic Algorithms for a Multi-objective Resource Constraint Project Scheduling Problem

2017 ◽  
Vol 28 (5) ◽  
pp. 791-806 ◽  
Author(s):  
Xixi Wang ◽  
Farouk Yalaoui ◽  
Frédéric Dugardin
Keyword(s):  
Project Scheduling ◽  
Ideal Point ◽  
Selection Procedure ◽  
Resource Constraint ◽  
Scheduling Problem ◽  
Due Dates ◽  
Lorenz Dominance ◽  
Project Scheduling Problem ◽  
Job Tardiness ◽  
The Ideal

Abstract The resource constraint project scheduling problem (RCPSP) has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II (NSGAII) as well as the recently proposed NSGAIII. Giving emphasis to the selection procedure, we apply both the traditional Pareto dominance and the less documented Lorenz dominance into the niching mechanism of NSGAIII. Hence, we adopt and modify L-NSGAII to our problem and propose L-NSGAIII by integrating the notion of Lorenz dominance. Our methods are tested by 1350 randomly generated instances, considering problems with 30–150 jobs and different configurations of resources and due dates. Hypervolume and C-metric are considered to evaluate the results. The Lorenz dominance leads the population more toward the ideal point. As experiments show, it allows improving the original NSGA approach.

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