NDLI: An Artificial Immune Algorithm for Minimizing Total Cost of Resources in the Resource Constrained Project Scheduling Problem

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An Artificial Immune Algorithm for Minimizing Total Cost of Resources in the Resource Constrained Project Scheduling Problem

Content Provider	Semantic Scholar
Author	Golestaneh, Ramin Jafari, Azizollah
Copyright Year	2013
Abstract	In this article, an Artificial Immune Algorithm (AIA) for minimizing total costs of both renewable and non-renewable resources in the Resource-Constrained Project Scheduling Problem (RCPSP) is presented. We assume renewable resources that are limited in number, are restricted to very expensive equipment and machines, therefore they are rented and used in other projects, and are not available in all project periods. In other words, there is a predefined ready date as well as a due date for each renewable resource type, so that no resource is used before its ready date. However, resources are permitted to be used after their due date by paying penalty costs depending on the resource type. The objective is to minimize the total costs of both renewable and non-renewable resource usages. For this purpose, we present a metaheuristic algorithm namely Artificial Immune Algorithm (AIA) inspired by the vertebrate immune system to solve this problem. In order to examine the performance of this algorithm, data derived from studied literature were used, and their answers were compared with those of the Simulated Annealing (SA) algorithm. Results show that in average, quality of AIA answers was better than those of the SA algorithm. Moreover, AIA was more sustainable.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.researchgate.net/profile/Azizollah_Jafari/publication/258360533_An_Artificial_Immune_Algorithm_for_Minimizing_Total_Cost_of_Resources_in_the_Resource_Constrained_Project_Scheduling_Problem/links/0c9605281d03ecd799000000.pdf
Alternate Webpage(s)	https://works.bepress.com/universityofscienceculture_usc/12/download/
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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